Upload folder using huggingface_hub (part 5)

.gitattributes

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+pydantic/v1/version.py,sha256=YpHWOQKtGoxfyikzGrcmXJVKUVYB9EBdoCR994QxSnE,1039
+pydantic/validate_call_decorator.py,sha256=VLAi4hoFpjC-1eL0HixYaaWaEFO6htGcDctgsqa5VII,4416
+pydantic/validators.py,sha256=pwbIJXVb1CV2mAE4w_EGfNj7DwzsKaWw_tTL6cviTus,146
+pydantic/version.py,sha256=T5rziwDPrMjfaU0X--5fBdGVvh94QznNQ7QjD4LfgmA,3985
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.mcp-venv/lib/python3.13/site-packages/pydantic-2.13.4.dist-info/WHEEL

@@ -0,0 +1,4 @@
+Wheel-Version: 1.0
+Generator: hatchling 1.29.0
+Root-Is-Purelib: true
+Tag: py3-none-any

.mcp-venv/lib/python3.13/site-packages/pydantic-2.13.4.dist-info/licenses/LICENSE

@@ -0,0 +1,21 @@
+The MIT License (MIT)
+
+Copyright (c) 2017 to present Pydantic Services Inc. and individual contributors.
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.

.mcp-venv/lib/python3.13/site-packages/pydantic/_internal/_known_annotated_metadata.py

@@ -0,0 +1,403 @@
+from __future__ import annotations
+
+from collections import defaultdict
+from collections.abc import Iterable
+from copy import copy
+from functools import lru_cache, partial
+from typing import TYPE_CHECKING, Any
+
+from pydantic_core import CoreSchema, PydanticCustomError, ValidationError, to_jsonable_python
+from pydantic_core import core_schema as cs
+
+from ._fields import PydanticMetadata
+from ._import_utils import import_cached_field_info
+
+if TYPE_CHECKING:
+    pass
+
+STRICT = {'strict'}
+FAIL_FAST = {'fail_fast'}
+LENGTH_CONSTRAINTS = {'min_length', 'max_length'}
+INEQUALITY = {'le', 'ge', 'lt', 'gt'}
+NUMERIC_CONSTRAINTS = {'multiple_of', *INEQUALITY}
+ALLOW_INF_NAN = {'allow_inf_nan'}
+
+STR_CONSTRAINTS = {
+    *LENGTH_CONSTRAINTS,
+    *STRICT,
+    'strip_whitespace',
+    'to_lower',
+    'to_upper',
+    'pattern',
+    'coerce_numbers_to_str',
+    'ascii_only',
+}
+BYTES_CONSTRAINTS = {*LENGTH_CONSTRAINTS, *STRICT}
+
+LIST_CONSTRAINTS = {*LENGTH_CONSTRAINTS, *STRICT, *FAIL_FAST}
+TUPLE_CONSTRAINTS = {*LENGTH_CONSTRAINTS, *STRICT, *FAIL_FAST}
+SET_CONSTRAINTS = {*LENGTH_CONSTRAINTS, *STRICT, *FAIL_FAST}
+DICT_CONSTRAINTS = {*LENGTH_CONSTRAINTS, *STRICT}
+GENERATOR_CONSTRAINTS = {*LENGTH_CONSTRAINTS, *STRICT}
+SEQUENCE_CONSTRAINTS = {*LENGTH_CONSTRAINTS, *FAIL_FAST}
+
+FLOAT_CONSTRAINTS = {*NUMERIC_CONSTRAINTS, *ALLOW_INF_NAN, *STRICT}
+DECIMAL_CONSTRAINTS = {'max_digits', 'decimal_places', *FLOAT_CONSTRAINTS}
+INT_CONSTRAINTS = {*NUMERIC_CONSTRAINTS, *ALLOW_INF_NAN, *STRICT}
+BOOL_CONSTRAINTS = STRICT
+UUID_CONSTRAINTS = STRICT
+
+DATE_TIME_CONSTRAINTS = {*NUMERIC_CONSTRAINTS, *STRICT}
+TIMEDELTA_CONSTRAINTS = {*NUMERIC_CONSTRAINTS, *STRICT}
+TIME_CONSTRAINTS = {*NUMERIC_CONSTRAINTS, *STRICT}
+LAX_OR_STRICT_CONSTRAINTS = STRICT
+ENUM_CONSTRAINTS = STRICT
+COMPLEX_CONSTRAINTS = STRICT
+
+UNION_CONSTRAINTS = {'union_mode'}
+URL_CONSTRAINTS = {
+    'max_length',
+    'allowed_schemes',
+    'host_required',
+    'default_host',
+    'default_port',
+    'default_path',
+}
+
+TEXT_SCHEMA_TYPES = ('str', 'bytes', 'url', 'multi-host-url')
+SEQUENCE_SCHEMA_TYPES = ('list', 'tuple', 'set', 'frozenset', 'generator', *TEXT_SCHEMA_TYPES)
+NUMERIC_SCHEMA_TYPES = ('float', 'int', 'date', 'time', 'timedelta', 'datetime')
+
+CONSTRAINTS_TO_ALLOWED_SCHEMAS: dict[str, set[str]] = defaultdict(set)
+
+constraint_schema_pairings: list[tuple[set[str], tuple[str, ...]]] = [
+    (STR_CONSTRAINTS, TEXT_SCHEMA_TYPES),
+    (BYTES_CONSTRAINTS, ('bytes',)),
+    (LIST_CONSTRAINTS, ('list',)),
+    (TUPLE_CONSTRAINTS, ('tuple',)),
+    (SET_CONSTRAINTS, ('set', 'frozenset')),
+    (DICT_CONSTRAINTS, ('dict',)),
+    (GENERATOR_CONSTRAINTS, ('generator',)),
+    (FLOAT_CONSTRAINTS, ('float',)),
+    (INT_CONSTRAINTS, ('int',)),
+    (DATE_TIME_CONSTRAINTS, ('date', 'time', 'datetime', 'timedelta')),
+    # TODO: this is a bit redundant, we could probably avoid some of these
+    (STRICT, (*TEXT_SCHEMA_TYPES, *SEQUENCE_SCHEMA_TYPES, *NUMERIC_SCHEMA_TYPES, 'typed-dict', 'model')),
+    (UNION_CONSTRAINTS, ('union',)),
+    (URL_CONSTRAINTS, ('url', 'multi-host-url')),
+    (BOOL_CONSTRAINTS, ('bool',)),
+    (UUID_CONSTRAINTS, ('uuid',)),
+    (LAX_OR_STRICT_CONSTRAINTS, ('lax-or-strict',)),
+    (ENUM_CONSTRAINTS, ('enum',)),
+    (DECIMAL_CONSTRAINTS, ('decimal',)),
+    (COMPLEX_CONSTRAINTS, ('complex',)),
+]
+
+for constraints, schemas in constraint_schema_pairings:
+    for c in constraints:
+        CONSTRAINTS_TO_ALLOWED_SCHEMAS[c].update(schemas)
+
+
+def as_jsonable_value(v: Any) -> Any:
+    if type(v) not in (int, str, float, bytes, bool, type(None)):
+        return to_jsonable_python(v)
+    return v
+
+
+def expand_grouped_metadata(annotations: Iterable[Any]) -> Iterable[Any]:
+    """Expand the annotations.
+
+    Args:
+        annotations: An iterable of annotations.
+
+    Returns:
+        An iterable of expanded annotations.
+
+    Example:
+        ```python
+        from annotated_types import Ge, Len
+
+        from pydantic._internal._known_annotated_metadata import expand_grouped_metadata
+
+        print(list(expand_grouped_metadata([Ge(4), Len(5)])))
+        #> [Ge(ge=4), MinLen(min_length=5)]
+        ```
+    """
+    import annotated_types as at
+
+    FieldInfo = import_cached_field_info()
+
+    for annotation in annotations:
+        if isinstance(annotation, at.GroupedMetadata):
+            yield from annotation
+        elif isinstance(annotation, FieldInfo):
+            yield from annotation.metadata
+            # this is a bit problematic in that it results in duplicate metadata
+            # all of our "consumers" can handle it, but it is not ideal
+            # we probably should split up FieldInfo into:
+            # - annotated types metadata
+            # - individual metadata known only to Pydantic
+            annotation = copy(annotation)
+            annotation.metadata = []
+            yield annotation
+        else:
+            yield annotation
+
+
+@lru_cache
+def _get_at_to_constraint_map() -> dict[type, str]:
+    """Return a mapping of annotated types to constraints.
+
+    Normally, we would define a mapping like this in the module scope, but we can't do that
+    because we don't permit module level imports of `annotated_types`, in an attempt to speed up
+    the import time of `pydantic`. We still only want to have this dictionary defined in one place,
+    so we use this function to cache the result.
+    """
+    import annotated_types as at
+
+    return {
+        at.Gt: 'gt',
+        at.Ge: 'ge',
+        at.Lt: 'lt',
+        at.Le: 'le',
+        at.MultipleOf: 'multiple_of',
+        at.MinLen: 'min_length',
+        at.MaxLen: 'max_length',
+    }
+
+
+def apply_known_metadata(annotation: Any, schema: CoreSchema) -> CoreSchema | None:  # noqa: C901
+    """Apply `annotation` to `schema` if it is an annotation we know about (Gt, Le, etc.).
+    Otherwise return `None`.
+
+    This does not handle all known annotations. If / when it does, it can always
+    return a CoreSchema and return the unmodified schema if the annotation should be ignored.
+
+    Assumes that GroupedMetadata has already been expanded via `expand_grouped_metadata`.
+
+    Args:
+        annotation: The annotation.
+        schema: The schema.
+
+    Returns:
+        An updated schema with annotation if it is an annotation we know about, `None` otherwise.
+
+    Raises:
+        RuntimeError: If a constraint can't be applied to a specific schema type.
+        ValueError: If an unknown constraint is encountered.
+    """
+    import annotated_types as at
+
+    from ._validators import NUMERIC_VALIDATOR_LOOKUP, forbid_inf_nan_check
+
+    schema = schema.copy()
+    schema_update, other_metadata = collect_known_metadata([annotation])
+    schema_type = schema['type']
+
+    chain_schema_constraints: set[str] = {
+        'pattern',
+        'strip_whitespace',
+        'to_lower',
+        'to_upper',
+        'coerce_numbers_to_str',
+        'ascii_only',
+    }
+    chain_schema_steps: list[CoreSchema] = []
+
+    for constraint, value in schema_update.items():
+        if constraint not in CONSTRAINTS_TO_ALLOWED_SCHEMAS:
+            raise ValueError(f'Unknown constraint {constraint}')
+        allowed_schemas = CONSTRAINTS_TO_ALLOWED_SCHEMAS[constraint]
+
+        # if it becomes necessary to handle more than one constraint
+        # in this recursive case with function-after or function-wrap, we should refactor
+        # this is a bit challenging because we sometimes want to apply constraints to the inner schema,
+        # whereas other times we want to wrap the existing schema with a new one that enforces a new constraint.
+        if schema_type in {'function-before', 'function-wrap', 'function-after'} and constraint == 'strict':
+            schema['schema'] = apply_known_metadata(annotation, schema['schema'])  # type: ignore  # schema is function schema
+            return schema
+
+        # if we're allowed to apply constraint directly to the schema, like le to int, do that
+        if schema_type in allowed_schemas:
+            if constraint == 'union_mode' and schema_type == 'union':
+                schema['mode'] = value  # type: ignore  # schema is UnionSchema
+            else:
+                schema[constraint] = value
+            continue
+
+        #  else, apply a function after validator to the schema to enforce the corresponding constraint
+        if constraint in chain_schema_constraints:
+
+            def _apply_constraint_with_incompatibility_info(
+                value: Any, handler: cs.ValidatorFunctionWrapHandler
+            ) -> Any:
+                try:
+                    x = handler(value)
+                except ValidationError as ve:
+                    # if the error is about the type, it's likely that the constraint is incompatible the type of the field
+                    # for example, the following invalid schema wouldn't be caught during schema build, but rather at this point
+                    # with a cryptic 'string_type' error coming from the string validator,
+                    # that we'd rather express as a constraint incompatibility error (TypeError)
+                    # Annotated[list[int], Field(pattern='abc')]
+                    if 'type' in ve.errors()[0]['type']:
+                        raise TypeError(
+                            f"Unable to apply constraint '{constraint}' to supplied value {value} for schema of type '{schema_type}'"  # noqa: B023
+                        )
+                    raise ve
+                return x
+
+            chain_schema_steps.append(
+                cs.no_info_wrap_validator_function(
+                    _apply_constraint_with_incompatibility_info, cs.str_schema(**{constraint: value})
+                )
+            )
+        elif constraint in NUMERIC_VALIDATOR_LOOKUP:
+            if constraint in LENGTH_CONSTRAINTS:
+                inner_schema = schema
+                while inner_schema['type'] in {'function-before', 'function-wrap', 'function-after'}:
+                    inner_schema = inner_schema['schema']  # type: ignore
+                inner_schema_type = inner_schema['type']
+                if inner_schema_type == 'list' or (
+                    inner_schema_type == 'json-or-python' and inner_schema['json_schema']['type'] == 'list'  # type: ignore
+                ):
+                    js_constraint_key = 'minItems' if constraint == 'min_length' else 'maxItems'
+                else:
+                    js_constraint_key = 'minLength' if constraint == 'min_length' else 'maxLength'
+            else:
+                js_constraint_key = constraint
+
+            schema = cs.no_info_after_validator_function(
+                partial(NUMERIC_VALIDATOR_LOOKUP[constraint], **{constraint: value}), schema
+            )
+            metadata = schema.get('metadata', {})
+            if (existing_json_schema_updates := metadata.get('pydantic_js_updates')) is not None:
+                metadata['pydantic_js_updates'] = {
+                    **existing_json_schema_updates,
+                    **{js_constraint_key: as_jsonable_value(value)},
+                }
+            else:
+                metadata['pydantic_js_updates'] = {js_constraint_key: as_jsonable_value(value)}
+            schema['metadata'] = metadata
+        elif constraint == 'allow_inf_nan' and value is False:
+            schema = cs.no_info_after_validator_function(
+                forbid_inf_nan_check,
+                schema,
+            )
+        else:
+            # It's rare that we'd get here, but it's possible if we add a new constraint and forget to handle it
+            # Most constraint errors are caught at runtime during attempted application
+            raise RuntimeError(f"Unable to apply constraint '{constraint}' to schema of type '{schema_type}'")
+
+    for annotation in other_metadata:
+        if (annotation_type := type(annotation)) in (at_to_constraint_map := _get_at_to_constraint_map()):
+            constraint = at_to_constraint_map[annotation_type]
+            validator = NUMERIC_VALIDATOR_LOOKUP.get(constraint)
+            if validator is None:
+                raise ValueError(f'Unknown constraint {constraint}')
+            schema = cs.no_info_after_validator_function(
+                partial(validator, {constraint: getattr(annotation, constraint)}), schema
+            )
+            continue
+        elif isinstance(annotation, (at.Predicate, at.Not)):
+            predicate_name = f'{annotation.func.__qualname__!r} ' if hasattr(annotation.func, '__qualname__') else ''
+
+            # Note: B023 is ignored because even though we iterate over `other_metadata`, it is guaranteed
+            # to be of length 1. `apply_known_metadata()` is called from `GenerateSchema`, where annotations
+            # were already expanded via `expand_grouped_metadata()`. Confusing, but this falls into the annotations
+            # refactor.
+            if isinstance(annotation, at.Predicate):
+
+                def val_func(v: Any) -> Any:
+                    predicate_satisfied = annotation.func(v)  # noqa: B023
+                    if not predicate_satisfied:
+                        raise PydanticCustomError(
+                            'predicate_failed',
+                            f'Predicate {predicate_name}failed',  # pyright: ignore[reportArgumentType]  # noqa: B023
+                        )
+                    return v
+
+            else:
+
+                def val_func(v: Any) -> Any:
+                    predicate_satisfied = annotation.func(v)  # noqa: B023
+                    if predicate_satisfied:
+                        raise PydanticCustomError(
+                            'not_operation_failed',
+                            f'Not of {predicate_name}failed',  # pyright: ignore[reportArgumentType]  # noqa: B023
+                        )
+                    return v
+
+            schema = cs.no_info_after_validator_function(val_func, schema)
+        else:
+            # ignore any other unknown metadata
+            return None
+
+    if chain_schema_steps:
+        chain_schema_steps = [schema] + chain_schema_steps
+        return cs.chain_schema(chain_schema_steps)
+
+    return schema
+
+
+def collect_known_metadata(annotations: Iterable[Any]) -> tuple[dict[str, Any], list[Any]]:
+    """Split `annotations` into known metadata and unknown annotations.
+
+    Args:
+        annotations: An iterable of annotations.
+
+    Returns:
+        A tuple contains a dict of known metadata and a list of unknown annotations.
+
+    Example:
+        ```python
+        from annotated_types import Gt, Len
+
+        from pydantic._internal._known_annotated_metadata import collect_known_metadata
+
+        print(collect_known_metadata([Gt(1), Len(42), ...]))
+        #> ({'gt': 1, 'min_length': 42}, [Ellipsis])
+        ```
+    """
+    annotations = expand_grouped_metadata(annotations)
+
+    res: dict[str, Any] = {}
+    remaining: list[Any] = []
+
+    for annotation in annotations:
+        # isinstance(annotation, PydanticMetadata) also covers ._fields:_PydanticGeneralMetadata
+        if isinstance(annotation, PydanticMetadata):
+            res.update(annotation.__dict__)
+        # we don't use dataclasses.asdict because that recursively calls asdict on the field values
+        elif (annotation_type := type(annotation)) in (at_to_constraint_map := _get_at_to_constraint_map()):
+            constraint = at_to_constraint_map[annotation_type]
+            res[constraint] = getattr(annotation, constraint)
+        elif isinstance(annotation, type) and issubclass(annotation, PydanticMetadata):
+            # also support PydanticMetadata classes being used without initialisation,
+            # e.g. `Annotated[int, Strict]` as well as `Annotated[int, Strict()]`
+            res.update({k: v for k, v in vars(annotation).items() if not k.startswith('_')})
+        else:
+            remaining.append(annotation)
+    # Nones can sneak in but pydantic-core will reject them
+    # it'd be nice to clean things up so we don't put in None (we probably don't _need_ to, it was just easier)
+    # but this is simple enough to kick that can down the road
+    res = {k: v for k, v in res.items() if v is not None}
+    return res, remaining
+
+
+def check_metadata(metadata: dict[str, Any], allowed: Iterable[str], source_type: Any) -> None:
+    """A small utility function to validate that the given metadata can be applied to the target.
+    More than saving lines of code, this gives us a consistent error message for all of our internal implementations.
+
+    Args:
+        metadata: A dict of metadata.
+        allowed: An iterable of allowed metadata.
+        source_type: The source type.
+
+    Raises:
+        TypeError: If there is metadatas that can't be applied on source type.
+    """
+    unknown = metadata.keys() - set(allowed)
+    if unknown:
+        raise TypeError(
+            f'The following constraints cannot be applied to {source_type!r}: {", ".join([f"{k!r}" for k in unknown])}'
+        )

.mcp-venv/lib/python3.13/site-packages/pydantic/_internal/_mock_val_ser.py

@@ -0,0 +1,228 @@
+from __future__ import annotations
+
+from collections.abc import Iterator, Mapping
+from typing import TYPE_CHECKING, Any, Callable, Generic, Literal, TypeVar, Union
+
+from pydantic_core import CoreSchema, SchemaSerializer, SchemaValidator
+
+from ..errors import PydanticErrorCodes, PydanticUserError
+from ..plugin._schema_validator import PluggableSchemaValidator
+
+if TYPE_CHECKING:
+    from ..dataclasses import PydanticDataclass
+    from ..main import BaseModel
+    from ..type_adapter import TypeAdapter
+
+
+ValSer = TypeVar('ValSer', bound=Union[SchemaValidator, PluggableSchemaValidator, SchemaSerializer])
+T = TypeVar('T')
+
+
+class MockCoreSchema(Mapping[str, Any]):
+    """Mocker for `pydantic_core.CoreSchema` which optionally attempts to
+    rebuild the thing it's mocking when one of its methods is accessed and raises an error if that fails.
+    """
+
+    __slots__ = '_error_message', '_code', '_attempt_rebuild', '_built_memo'
+
+    def __init__(
+        self,
+        error_message: str,
+        *,
+        code: PydanticErrorCodes,
+        attempt_rebuild: Callable[[], CoreSchema | None] | None = None,
+    ) -> None:
+        self._error_message = error_message
+        self._code: PydanticErrorCodes = code
+        self._attempt_rebuild = attempt_rebuild
+        self._built_memo: CoreSchema | None = None
+
+    def __getitem__(self, key: str) -> Any:
+        return self._get_built().__getitem__(key)
+
+    def __len__(self) -> int:
+        return self._get_built().__len__()
+
+    def __iter__(self) -> Iterator[str]:
+        return self._get_built().__iter__()
+
+    def _get_built(self) -> CoreSchema:
+        if self._built_memo is not None:
+            return self._built_memo
+
+        if self._attempt_rebuild:
+            schema = self._attempt_rebuild()
+            if schema is not None:
+                self._built_memo = schema
+                return schema
+        raise PydanticUserError(self._error_message, code=self._code)
+
+    def rebuild(self) -> CoreSchema | None:
+        self._built_memo = None
+        if self._attempt_rebuild:
+            schema = self._attempt_rebuild()
+            if schema is not None:
+                return schema
+            else:
+                raise PydanticUserError(self._error_message, code=self._code)
+        return None
+
+
+class MockValSer(Generic[ValSer]):
+    """Mocker for `pydantic_core.SchemaValidator` or `pydantic_core.SchemaSerializer` which optionally attempts to
+    rebuild the thing it's mocking when one of its methods is accessed and raises an error if that fails.
+    """
+
+    __slots__ = '_error_message', '_code', '_val_or_ser', '_attempt_rebuild'
+
+    def __init__(
+        self,
+        error_message: str,
+        *,
+        code: PydanticErrorCodes,
+        val_or_ser: Literal['validator', 'serializer'],
+        attempt_rebuild: Callable[[], ValSer | None] | None = None,
+    ) -> None:
+        self._error_message = error_message
+        self._val_or_ser = SchemaValidator if val_or_ser == 'validator' else SchemaSerializer
+        self._code: PydanticErrorCodes = code
+        self._attempt_rebuild = attempt_rebuild
+
+    def __getattr__(self, item: str) -> None:
+        __tracebackhide__ = True
+        if self._attempt_rebuild:
+            val_ser = self._attempt_rebuild()
+            if val_ser is not None:
+                return getattr(val_ser, item)
+
+        # raise an AttributeError if `item` doesn't exist
+        getattr(self._val_or_ser, item)
+        raise PydanticUserError(self._error_message, code=self._code)
+
+    def rebuild(self) -> ValSer | None:
+        if self._attempt_rebuild:
+            val_ser = self._attempt_rebuild()
+            if val_ser is not None:
+                return val_ser
+            else:
+                raise PydanticUserError(self._error_message, code=self._code)
+        return None
+
+
+def set_type_adapter_mocks(adapter: TypeAdapter) -> None:
+    """Set `core_schema`, `validator` and `serializer` to mock core types on a type adapter instance.
+
+    Args:
+        adapter: The type adapter instance to set the mocks on
+    """
+    type_repr = str(adapter._type)
+    undefined_type_error_message = (
+        f'`TypeAdapter[{type_repr}]` is not fully defined; you should define `{type_repr}` and all referenced types,'
+        f' then call `.rebuild()` on the instance.'
+    )
+
+    def attempt_rebuild_fn(attr_fn: Callable[[TypeAdapter], T]) -> Callable[[], T | None]:
+        def handler() -> T | None:
+            if adapter.rebuild(raise_errors=False, _parent_namespace_depth=5) is not False:
+                return attr_fn(adapter)
+            return None
+
+        return handler
+
+    adapter.core_schema = MockCoreSchema(  # pyright: ignore[reportAttributeAccessIssue]
+        undefined_type_error_message,
+        code='class-not-fully-defined',
+        attempt_rebuild=attempt_rebuild_fn(lambda ta: ta.core_schema),
+    )
+    adapter.validator = MockValSer(  # pyright: ignore[reportAttributeAccessIssue]
+        undefined_type_error_message,
+        code='class-not-fully-defined',
+        val_or_ser='validator',
+        attempt_rebuild=attempt_rebuild_fn(lambda ta: ta.validator),
+    )
+    adapter.serializer = MockValSer(  # pyright: ignore[reportAttributeAccessIssue]
+        undefined_type_error_message,
+        code='class-not-fully-defined',
+        val_or_ser='serializer',
+        attempt_rebuild=attempt_rebuild_fn(lambda ta: ta.serializer),
+    )
+
+
+def set_model_mocks(cls: type[BaseModel], undefined_name: str = 'all referenced types') -> None:
+    """Set `__pydantic_core_schema__`, `__pydantic_validator__` and `__pydantic_serializer__` to mock core types on a model.
+
+    Args:
+        cls: The model class to set the mocks on
+        undefined_name: Name of the undefined thing, used in error messages
+    """
+    undefined_type_error_message = (
+        f'`{cls.__name__}` is not fully defined; you should define {undefined_name},'
+        f' then call `{cls.__name__}.model_rebuild()`.'
+    )
+
+    def attempt_rebuild_fn(attr_fn: Callable[[type[BaseModel]], T]) -> Callable[[], T | None]:
+        def handler() -> T | None:
+            if cls.model_rebuild(raise_errors=False, _parent_namespace_depth=5) is not False:
+                return attr_fn(cls)
+            return None
+
+        return handler
+
+    cls.__pydantic_core_schema__ = MockCoreSchema(  # pyright: ignore[reportAttributeAccessIssue]
+        undefined_type_error_message,
+        code='class-not-fully-defined',
+        attempt_rebuild=attempt_rebuild_fn(lambda c: c.__pydantic_core_schema__),
+    )
+    cls.__pydantic_validator__ = MockValSer(  # pyright: ignore[reportAttributeAccessIssue]
+        undefined_type_error_message,
+        code='class-not-fully-defined',
+        val_or_ser='validator',
+        attempt_rebuild=attempt_rebuild_fn(lambda c: c.__pydantic_validator__),
+    )
+    cls.__pydantic_serializer__ = MockValSer(  # pyright: ignore[reportAttributeAccessIssue]
+        undefined_type_error_message,
+        code='class-not-fully-defined',
+        val_or_ser='serializer',
+        attempt_rebuild=attempt_rebuild_fn(lambda c: c.__pydantic_serializer__),
+    )
+
+
+def set_dataclass_mocks(cls: type[PydanticDataclass], undefined_name: str = 'all referenced types') -> None:
+    """Set `__pydantic_validator__` and `__pydantic_serializer__` to `MockValSer`s on a dataclass.
+
+    Args:
+        cls: The model class to set the mocks on
+        undefined_name: Name of the undefined thing, used in error messages
+    """
+    from ..dataclasses import rebuild_dataclass
+
+    undefined_type_error_message = (
+        f'`{cls.__name__}` is not fully defined; you should define {undefined_name},'
+        f' then call `pydantic.dataclasses.rebuild_dataclass({cls.__name__})`.'
+    )
+
+    def attempt_rebuild_fn(attr_fn: Callable[[type[PydanticDataclass]], T]) -> Callable[[], T | None]:
+        def handler() -> T | None:
+            if rebuild_dataclass(cls, raise_errors=False, _parent_namespace_depth=5) is not False:
+                return attr_fn(cls)
+            return None
+
+        return handler
+
+    cls.__pydantic_core_schema__ = MockCoreSchema(  # pyright: ignore[reportAttributeAccessIssue]
+        undefined_type_error_message,
+        code='class-not-fully-defined',
+        attempt_rebuild=attempt_rebuild_fn(lambda c: c.__pydantic_core_schema__),
+    )
+    cls.__pydantic_validator__ = MockValSer(  # pyright: ignore[reportAttributeAccessIssue]
+        undefined_type_error_message,
+        code='class-not-fully-defined',
+        val_or_ser='validator',
+        attempt_rebuild=attempt_rebuild_fn(lambda c: c.__pydantic_validator__),
+    )
+    cls.__pydantic_serializer__ = MockValSer(  # pyright: ignore[reportAttributeAccessIssue]
+        undefined_type_error_message,
+        code='class-not-fully-defined',
+        val_or_ser='serializer',
+        attempt_rebuild=attempt_rebuild_fn(lambda c: c.__pydantic_serializer__),
+    )

.mcp-venv/lib/python3.13/site-packages/pydantic/_internal/_model_construction.py

@@ -0,0 +1,868 @@
+"""Private logic for creating models."""
+
+from __future__ import annotations as _annotations
+
+import operator
+import sys
+import typing
+import warnings
+import weakref
+from abc import ABCMeta
+from functools import cache, partial, wraps
+from types import FunctionType
+from typing import TYPE_CHECKING, Any, Callable, Generic, Literal, NoReturn, TypeVar, cast
+
+from pydantic_core import PydanticUndefined, SchemaSerializer
+from typing_extensions import TypeAliasType, dataclass_transform, deprecated, get_args, get_origin
+from typing_inspection import typing_objects
+
+from ..errors import PydanticUndefinedAnnotation, PydanticUserError
+from ..plugin._schema_validator import create_schema_validator
+from ..warnings import GenericBeforeBaseModelWarning, PydanticDeprecatedSince20
+from ._config import ConfigWrapper
+from ._decorators import DecoratorInfos, PydanticDescriptorProxy, get_attribute_from_bases, unwrap_wrapped_function
+from ._fields import collect_model_fields, is_valid_field_name, is_valid_privateattr_name, rebuild_model_fields
+from ._generate_schema import GenerateSchema, InvalidSchemaError
+from ._generics import PydanticGenericMetadata, get_model_typevars_map
+from ._import_utils import import_cached_base_model, import_cached_field_info
+from ._mock_val_ser import set_model_mocks
+from ._namespace_utils import NsResolver
+from ._signature import generate_pydantic_signature
+from ._typing_extra import (
+    _make_forward_ref,
+    eval_type_backport,
+    is_classvar_annotation,
+    parent_frame_namespace,
+)
+from ._utils import LazyClassAttribute, SafeGetItemProxy
+
+if TYPE_CHECKING:
+    from ..fields import Field as PydanticModelField
+    from ..fields import FieldInfo, ModelPrivateAttr
+    from ..fields import PrivateAttr as PydanticModelPrivateAttr
+    from ..main import BaseModel
+    from ._fields import PydanticExtraInfo
+else:
+    PydanticModelField = object()
+    PydanticModelPrivateAttr = object()
+
+object_setattr = object.__setattr__
+
+
+class _ModelNamespaceDict(dict):
+    """A dictionary subclass that intercepts attribute setting on model classes and
+    warns about overriding of decorators.
+    """
+
+    def __setitem__(self, k: str, v: object) -> None:
+        existing: Any = self.get(k, None)
+        if existing and v is not existing and isinstance(existing, PydanticDescriptorProxy):
+            warnings.warn(
+                f'`{k}` overrides an existing Pydantic `{existing.decorator_info.decorator_repr}` decorator',
+                stacklevel=2,
+            )
+
+        return super().__setitem__(k, v)
+
+
+def NoInitField(
+    *,
+    init: Literal[False] = False,
+) -> Any:
+    """Only for typing purposes. Used as default value of `__pydantic_fields_set__`,
+    `__pydantic_extra__`, `__pydantic_private__`, so they could be ignored when
+    synthesizing the `__init__` signature.
+    """
+
+
+# For ModelMetaclass.register():
+_T = TypeVar('_T')
+
+
+@dataclass_transform(kw_only_default=True, field_specifiers=(PydanticModelField, PydanticModelPrivateAttr, NoInitField))
+class ModelMetaclass(ABCMeta):
+    def __new__(
+        mcs,
+        cls_name: str,
+        bases: tuple[type[Any], ...],
+        namespace: dict[str, Any],
+        __pydantic_generic_metadata__: PydanticGenericMetadata | None = None,
+        __pydantic_reset_parent_namespace__: bool = True,
+        _create_model_module: str | None = None,
+        **kwargs: Any,
+    ) -> type:
+        """Metaclass for creating Pydantic models.
+
+        Args:
+            cls_name: The name of the class to be created.
+            bases: The base classes of the class to be created.
+            namespace: The attribute dictionary of the class to be created.
+            __pydantic_generic_metadata__: Metadata for generic models.
+            __pydantic_reset_parent_namespace__: Reset parent namespace.
+            _create_model_module: The module of the class to be created, if created by `create_model`.
+            **kwargs: Catch-all for any other keyword arguments.
+
+        Returns:
+            The new class created by the metaclass.
+        """
+        # Note `ModelMetaclass` refers to `BaseModel`, but is also used to *create* `BaseModel`, so we rely on the fact
+        # that `BaseModel` itself won't have any bases, but any subclass of it will, to determine whether the `__new__`
+        # call we're in the middle of is for the `BaseModel` class.
+        if bases:
+            raw_annotations: dict[str, Any]
+            if sys.version_info >= (3, 14):
+                if (
+                    '__annotations__' in namespace
+                ):  # `from __future__ import annotations` was used in the model's module
+                    raw_annotations = namespace['__annotations__']
+                else:
+                    # See https://docs.python.org/3.14/library/annotationlib.html#using-annotations-in-a-metaclass:
+                    from annotationlib import Format, call_annotate_function, get_annotate_from_class_namespace
+
+                    if annotate := get_annotate_from_class_namespace(namespace):
+                        raw_annotations = call_annotate_function(annotate, format=Format.FORWARDREF)
+                    else:
+                        raw_annotations = {}
+            else:
+                raw_annotations = namespace.get('__annotations__', {})
+
+            base_field_names, class_vars, base_private_attributes = mcs._collect_bases_data(bases)
+
+            config_wrapper = ConfigWrapper.for_model(bases, namespace, raw_annotations, kwargs)
+            namespace['model_config'] = config_wrapper.config_dict
+            private_attributes = inspect_namespace(
+                namespace, raw_annotations, config_wrapper.ignored_types, class_vars, base_field_names
+            )
+            if private_attributes or base_private_attributes:
+                original_model_post_init = get_model_post_init(namespace, bases)
+                if original_model_post_init is not None:
+                    # if there are private attributes and a model_post_init function, we handle both
+
+                    @wraps(original_model_post_init)
+                    def wrapped_model_post_init(self: BaseModel, context: Any, /) -> None:
+                        """We need to both initialize private attributes and call the user-defined model_post_init
+                        method.
+                        """
+                        init_private_attributes(self, context)
+                        original_model_post_init(self, context)
+
+                    namespace['model_post_init'] = wrapped_model_post_init
+                else:
+                    namespace['model_post_init'] = init_private_attributes
+
+            namespace['__class_vars__'] = class_vars
+            namespace['__private_attributes__'] = {**base_private_attributes, **private_attributes}
+
+            cls = cast('type[BaseModel]', super().__new__(mcs, cls_name, bases, namespace, **kwargs))
+            BaseModel_ = import_cached_base_model()
+
+            mro = cls.__mro__
+            if Generic in mro and mro.index(Generic) < mro.index(BaseModel_):
+                warnings.warn(
+                    GenericBeforeBaseModelWarning(
+                        'Classes should inherit from `BaseModel` before generic classes (e.g. `typing.Generic[T]`) '
+                        'for pydantic generics to work properly.'
+                    ),
+                    stacklevel=2,
+                )
+
+            cls.__pydantic_custom_init__ = not getattr(cls.__init__, '__pydantic_base_init__', False)
+            cls.__pydantic_post_init__ = (
+                None if cls.model_post_init is BaseModel_.model_post_init else 'model_post_init'
+            )
+
+            cls.__pydantic_setattr_handlers__ = {}
+
+            cls.__pydantic_decorators__ = DecoratorInfos.build(cls, replace_wrapped_methods=True)
+            cls.__pydantic_decorators__.update_from_config(config_wrapper)
+
+            # Use the getattr below to grab the __parameters__ from the `typing.Generic` parent class
+            if __pydantic_generic_metadata__:
+                cls.__pydantic_generic_metadata__ = __pydantic_generic_metadata__
+            else:
+                parent_parameters = getattr(cls, '__pydantic_generic_metadata__', {}).get('parameters', ())
+                parameters = getattr(cls, '__parameters__', None) or parent_parameters
+                if parameters and parent_parameters and not all(x in parameters for x in parent_parameters):
+                    from ..root_model import RootModelRootType
+
+                    missing_parameters = tuple(x for x in parameters if x not in parent_parameters)
+                    if RootModelRootType in parent_parameters and RootModelRootType not in parameters:
+                        # This is a special case where the user has subclassed RootModel, but has not parameterized
+                        # RootModel with the generic type identifiers being used. Ex:
+                        # class MyModel(RootModel, Generic[T]):
+                        #    root: T
+                        # Should instead just be:
+                        # class MyModel(RootModel[T]):
+                        #   root: T
+                        parameters_str = ', '.join([x.__name__ for x in missing_parameters])
+                        error_message = (
+                            f'{cls.__name__} is a subclass of `RootModel`, but does not include the generic type identifier(s) '
+                            f'{parameters_str} in its parameters. '
+                            f'You should parametrize RootModel directly, e.g., `class {cls.__name__}(RootModel[{parameters_str}]): ...`.'
+                        )
+                    else:
+                        combined_parameters = parent_parameters + missing_parameters
+                        parameters_str = ', '.join([str(x) for x in combined_parameters])
+                        generic_type_label = f'typing.Generic[{parameters_str}]'
+                        error_message = (
+                            f'All parameters must be present on typing.Generic;'
+                            f' you should inherit from {generic_type_label}.'
+                        )
+                        if Generic not in bases:  # pragma: no cover
+                            # We raise an error here not because it is desirable, but because some cases are mishandled.
+                            # It would be nice to remove this error and still have things behave as expected, it's just
+                            # challenging because we are using a custom `__class_getitem__` to parametrize generic models,
+                            # and not returning a typing._GenericAlias from it.
+                            bases_str = ', '.join([x.__name__ for x in bases] + [generic_type_label])
+                            error_message += (
+                                f' Note: `typing.Generic` must go last: `class {cls.__name__}({bases_str}): ...`)'
+                            )
+                    raise TypeError(error_message)
+
+                cls.__pydantic_generic_metadata__ = {
+                    'origin': None,
+                    'args': (),
+                    'parameters': parameters,
+                }
+
+            cls.__pydantic_complete__ = False  # Ensure this specific class gets completed
+
+            # preserve `__set_name__` protocol defined in https://peps.python.org/pep-0487
+            # for attributes not in `new_namespace` (e.g. private attributes)
+            for name, obj in private_attributes.items():
+                obj.__set_name__(cls, name)
+
+            if __pydantic_reset_parent_namespace__:
+                cls.__pydantic_parent_namespace__ = build_lenient_weakvaluedict(parent_frame_namespace())
+            parent_namespace: dict[str, Any] | None = getattr(cls, '__pydantic_parent_namespace__', None)
+            if isinstance(parent_namespace, dict):
+                parent_namespace = unpack_lenient_weakvaluedict(parent_namespace)
+
+            ns_resolver = NsResolver(parent_namespace=parent_namespace)
+
+            set_model_fields(cls, config_wrapper=config_wrapper, ns_resolver=ns_resolver)
+
+            # This is also set in `complete_model_class()`, after schema gen because they are recreated.
+            # We set them here as well for backwards compatibility:
+            cls.__pydantic_computed_fields__ = {
+                k: v.info for k, v in cls.__pydantic_decorators__.computed_fields.items()
+            }
+
+            if config_wrapper.defer_build:
+                set_model_mocks(cls)
+            else:
+                # Any operation that requires accessing the field infos instances should be put inside
+                # `complete_model_class()`:
+                complete_model_class(
+                    cls,
+                    config_wrapper,
+                    ns_resolver,
+                    raise_errors=False,
+                    create_model_module=_create_model_module,
+                )
+
+            if config_wrapper.frozen and '__hash__' not in namespace:
+                set_default_hash_func(cls, bases)
+
+            # using super(cls, cls) on the next line ensures we only call the parent class's __pydantic_init_subclass__
+            # I believe the `type: ignore` is only necessary because mypy doesn't realize that this code branch is
+            # only hit for _proper_ subclasses of BaseModel
+            super(cls, cls).__pydantic_init_subclass__(**kwargs)  # type: ignore[misc]
+            return cls
+        else:
+            # These are instance variables, but have been assigned to `NoInitField` to trick the type checker.
+            for instance_slot in '__pydantic_fields_set__', '__pydantic_extra__', '__pydantic_private__':
+                namespace.pop(
+                    instance_slot,
+                    None,  # In case the metaclass is used with a class other than `BaseModel`.
+                )
+            namespace.get('__annotations__', {}).clear()
+            return super().__new__(mcs, cls_name, bases, namespace, **kwargs)
+
+    if not TYPE_CHECKING:  # pragma: no branch
+        # We put `__getattr__` in a non-TYPE_CHECKING block because otherwise, mypy allows arbitrary attribute access
+
+        def __getattr__(self, item: str) -> Any:
+            """This is necessary to keep attribute access working for class attribute access."""
+            private_attributes = self.__dict__.get('__private_attributes__')
+            if private_attributes and item in private_attributes:
+                return private_attributes[item]
+            raise AttributeError(item)
+
+    @classmethod
+    def __prepare__(cls, *args: Any, **kwargs: Any) -> dict[str, object]:
+        return _ModelNamespaceDict()
+
+    # Due to performance and memory issues, in the ABCMeta.__subclasscheck__ implementation, we don't support
+    # registered virtual subclasses. See https://github.com/python/cpython/issues/92810#issuecomment-2762454345.
+    # This may change once CPython is fixed (possibly in 3.15), in which case we should conditionally
+    # define `register()`.
+    def register(self, subclass: type[_T]) -> type[_T]:
+        warnings.warn(
+            f"For performance reasons, virtual subclasses registered using '{self.__qualname__}.register()' "
+            "are not supported in 'isinstance()' and 'issubclass()' checks.",
+            stacklevel=2,
+        )
+        return super().register(subclass)
+
+    __instancecheck__ = type.__instancecheck__  # pyright: ignore[reportAssignmentType]
+    __subclasscheck__ = type.__subclasscheck__  # pyright: ignore[reportAssignmentType]
+
+    @staticmethod
+    def _collect_bases_data(bases: tuple[type[Any], ...]) -> tuple[set[str], set[str], dict[str, ModelPrivateAttr]]:
+        BaseModel = import_cached_base_model()
+
+        field_names: set[str] = set()
+        class_vars: set[str] = set()
+        private_attributes: dict[str, ModelPrivateAttr] = {}
+        for base in bases:
+            if issubclass(base, BaseModel) and base is not BaseModel:
+                # model_fields might not be defined yet in the case of generics, so we use getattr here:
+                field_names.update(getattr(base, '__pydantic_fields__', {}).keys())
+                class_vars.update(base.__class_vars__)
+                private_attributes.update(base.__private_attributes__)
+        return field_names, class_vars, private_attributes
+
+    @property
+    @deprecated(
+        'The `__fields__` attribute is deprecated, use the `model_fields` class property instead.', category=None
+    )
+    def __fields__(self) -> dict[str, FieldInfo]:
+        warnings.warn(
+            'The `__fields__` attribute is deprecated, use the `model_fields` class property instead.',
+            PydanticDeprecatedSince20,
+            stacklevel=2,
+        )
+        return getattr(self, '__pydantic_fields__', {})
+
+    @property
+    def __pydantic_fields_complete__(self) -> bool:
+        """Whether the fields were successfully collected (i.e. type hints were successfully resolved).
+
+        This is a private attribute, not meant to be used outside Pydantic.
+        """
+        if '__pydantic_fields__' not in self.__dict__:
+            return False
+
+        field_infos = cast('dict[str, FieldInfo]', self.__pydantic_fields__)  # pyright: ignore[reportAttributeAccessIssue]
+
+        pydantic_extra_info = cast('PydanticExtraInfo | None', self.__pydantic_extra_info__)  # pyright: ignore[reportAttributeAccessIssue]
+        if pydantic_extra_info is not None:
+            extra_complete = pydantic_extra_info.complete
+        else:
+            extra_complete = True
+
+        return all(field_info._complete for field_info in field_infos.values()) and extra_complete
+
+    def __dir__(self) -> list[str]:
+        attributes = list(super().__dir__())
+        if '__fields__' in attributes:
+            attributes.remove('__fields__')
+        return attributes
+
+
+def init_private_attributes(self: BaseModel, context: Any, /) -> None:
+    """This function is meant to behave like a BaseModel method to initialize private attributes.
+
+    It takes context as an argument since that's what pydantic-core passes when calling it.
+
+    Args:
+        self: The BaseModel instance.
+        context: The context.
+    """
+    if getattr(self, '__pydantic_private__', None) is None:
+        pydantic_private = {}
+        for name, private_attr in self.__private_attributes__.items():
+            # Avoid needlessly creating a new dict for the validated data:
+            if private_attr.default_factory_takes_validated_data:
+                default = private_attr.get_default(
+                    call_default_factory=True, validated_data={**self.__dict__, **pydantic_private}
+                )
+            else:
+                default = private_attr.get_default(call_default_factory=True)
+            if default is not PydanticUndefined:
+                pydantic_private[name] = default
+        object_setattr(self, '__pydantic_private__', pydantic_private)
+
+
+def get_model_post_init(namespace: dict[str, Any], bases: tuple[type[Any], ...]) -> Callable[..., Any] | None:
+    """Get the `model_post_init` method from the namespace or the class bases, or `None` if not defined."""
+    if 'model_post_init' in namespace:
+        return namespace['model_post_init']
+
+    BaseModel = import_cached_base_model()
+
+    model_post_init = get_attribute_from_bases(bases, 'model_post_init')
+    if model_post_init is not BaseModel.model_post_init:
+        return model_post_init
+
+
+def inspect_namespace(  # noqa C901
+    namespace: dict[str, Any],
+    raw_annotations: dict[str, Any],
+    ignored_types: tuple[type[Any], ...],
+    base_class_vars: set[str],
+    base_class_fields: set[str],
+) -> dict[str, ModelPrivateAttr]:
+    """Iterate over the namespace and:
+    * gather private attributes
+    * check for items which look like fields but are not (e.g. have no annotation) and warn.
+
+    Args:
+        namespace: The attribute dictionary of the class to be created.
+        raw_annotations: The (non-evaluated) annotations of the model.
+        ignored_types: A tuple of ignore types.
+        base_class_vars: A set of base class class variables.
+        base_class_fields: A set of base class fields.
+
+    Returns:
+        A dict containing private attributes info.
+
+    Raises:
+        TypeError: If there is a `__root__` field in model.
+        NameError: If private attribute name is invalid.
+        PydanticUserError:
+            - If a field does not have a type annotation.
+            - If a field on base class was overridden by a non-annotated attribute.
+    """
+    from ..fields import ModelPrivateAttr, PrivateAttr
+
+    FieldInfo = import_cached_field_info()
+
+    all_ignored_types = ignored_types + default_ignored_types()
+
+    private_attributes: dict[str, ModelPrivateAttr] = {}
+
+    if '__root__' in raw_annotations or '__root__' in namespace:
+        raise TypeError("To define root models, use `pydantic.RootModel` rather than a field called '__root__'")
+
+    ignored_names: set[str] = set()
+    for var_name, value in list(namespace.items()):
+        if var_name == 'model_config' or var_name == '__pydantic_extra__':
+            continue
+        elif (
+            isinstance(value, type)
+            and value.__module__ == namespace['__module__']
+            and '__qualname__' in namespace
+            and value.__qualname__.startswith(f'{namespace["__qualname__"]}.')
+        ):
+            # `value` is a nested type defined in this namespace; don't error
+            continue
+        elif isinstance(value, all_ignored_types) or value.__class__.__module__ == 'functools':
+            ignored_names.add(var_name)
+            continue
+        elif isinstance(value, ModelPrivateAttr):
+            if var_name.startswith('__'):
+                raise NameError(
+                    'Private attributes must not use dunder names;'
+                    f' use a single underscore prefix instead of {var_name!r}.'
+                )
+            elif is_valid_field_name(var_name):
+                raise NameError(
+                    'Private attributes must not use valid field names;'
+                    f' use sunder names, e.g. {"_" + var_name!r} instead of {var_name!r}.'
+                )
+            private_attributes[var_name] = value
+            del namespace[var_name]
+        elif isinstance(value, FieldInfo) and not is_valid_field_name(var_name):
+            suggested_name = var_name.lstrip('_') or 'my_field'  # don't suggest '' for all-underscore name
+            raise NameError(
+                f'Fields must not use names with leading underscores;'
+                f' e.g., use {suggested_name!r} instead of {var_name!r}.'
+            )
+
+        elif var_name.startswith('__'):
+            continue
+        elif is_valid_privateattr_name(var_name):
+            if var_name not in raw_annotations or not is_classvar_annotation(raw_annotations[var_name]):
+                private_attributes[var_name] = cast(ModelPrivateAttr, PrivateAttr(default=value))
+                del namespace[var_name]
+        elif var_name in base_class_vars:
+            continue
+        elif var_name not in raw_annotations:
+            if var_name in base_class_fields:
+                raise PydanticUserError(
+                    f'Field {var_name!r} defined on a base class was overridden by a non-annotated attribute. '
+                    f'All field definitions, including overrides, require a type annotation.',
+                    code='model-field-overridden',
+                )
+            elif isinstance(value, FieldInfo):
+                raise PydanticUserError(
+                    f'Field {var_name!r} requires a type annotation', code='model-field-missing-annotation'
+                )
+            else:
+                raise PydanticUserError(
+                    f'A non-annotated attribute was detected: `{var_name} = {value!r}`. All model fields require a '
+                    f'type annotation; if `{var_name}` is not meant to be a field, you may be able to resolve this '
+                    f"error by annotating it as a `ClassVar` or updating `model_config['ignored_types']`.",
+                    code='model-field-missing-annotation',
+                )
+
+    for ann_name, ann_type in raw_annotations.items():
+        if (
+            is_valid_privateattr_name(ann_name)
+            and ann_name not in private_attributes
+            and ann_name not in ignored_names
+            # This condition can be a false negative when `ann_type` is stringified,
+            # but it is handled in most cases in `set_model_fields`:
+            and not is_classvar_annotation(ann_type)
+            and ann_type not in all_ignored_types
+            and getattr(ann_type, '__module__', None) != 'functools'
+        ):
+            if isinstance(ann_type, str):
+                # Walking up the frames to get the module namespace where the model is defined
+                # (as the model class wasn't created yet, we unfortunately can't use `cls.__module__`):
+                frame = sys._getframe(2)
+                if frame is not None:
+                    try:
+                        ann_type = eval_type_backport(
+                            _make_forward_ref(ann_type, is_argument=False, is_class=True),
+                            globalns=frame.f_globals,
+                            localns=frame.f_locals,
+                        )
+                    except (NameError, TypeError):
+                        pass
+
+            if typing_objects.is_annotated(get_origin(ann_type)):
+                _, *metadata = get_args(ann_type)
+                private_attr = next((v for v in metadata if isinstance(v, ModelPrivateAttr)), None)
+                if private_attr is not None:
+                    private_attributes[ann_name] = private_attr
+                    continue
+            private_attributes[ann_name] = PrivateAttr()
+
+    return private_attributes
+
+
+def set_default_hash_func(cls: type[BaseModel], bases: tuple[type[Any], ...]) -> None:
+    base_hash_func = get_attribute_from_bases(bases, '__hash__')
+    new_hash_func = make_hash_func(cls)
+    if base_hash_func in {None, object.__hash__} or getattr(base_hash_func, '__code__', None) == new_hash_func.__code__:
+        # If `__hash__` is some default, we generate a hash function.
+        # It will be `None` if not overridden from BaseModel.
+        # It may be `object.__hash__` if there is another
+        # parent class earlier in the bases which doesn't override `__hash__` (e.g. `typing.Generic`).
+        # It may be a value set by `set_default_hash_func` if `cls` is a subclass of another frozen model.
+        # In the last case we still need a new hash function to account for new `model_fields`.
+        cls.__hash__ = new_hash_func
+
+
+def make_hash_func(cls: type[BaseModel]) -> Any:
+    getter = operator.itemgetter(*cls.__pydantic_fields__.keys()) if cls.__pydantic_fields__ else lambda _: 0
+
+    def hash_func(self: Any) -> int:
+        try:
+            return hash(getter(self.__dict__))
+        except KeyError:
+            # In rare cases (such as when using the deprecated copy method), the __dict__ may not contain
+            # all model fields, which is how we can get here.
+            # getter(self.__dict__) is much faster than any 'safe' method that accounts for missing keys,
+            # and wrapping it in a `try` doesn't slow things down much in the common case.
+            return hash(getter(SafeGetItemProxy(self.__dict__)))
+
+    return hash_func
+
+
+def set_model_fields(
+    cls: type[BaseModel],
+    config_wrapper: ConfigWrapper,
+    ns_resolver: NsResolver,
+) -> None:
+    """Collect and set `cls.__pydantic_fields__` and `cls.__class_vars__`.
+
+    Args:
+        cls: BaseModel or dataclass.
+        config_wrapper: The config wrapper instance.
+        ns_resolver: Namespace resolver to use when getting model annotations.
+    """
+    typevars_map = get_model_typevars_map(cls)
+    fields, pydantic_extra_info, class_vars = collect_model_fields(
+        cls, config_wrapper, ns_resolver, typevars_map=typevars_map
+    )
+
+    cls.__pydantic_fields__ = fields
+    cls.__pydantic_extra_info__ = pydantic_extra_info
+    cls.__class_vars__.update(class_vars)
+
+    for k in class_vars:
+        # Class vars should not be private attributes
+        #     We remove them _here_ and not earlier because we rely on inspecting the class to determine its classvars,
+        #     but private attributes are determined by inspecting the namespace _prior_ to class creation.
+        #     In the case that a classvar with a leading-'_' is defined via a ForwardRef (e.g., when using
+        #     `__future__.annotations`), we want to remove the private attribute which was detected _before_ we knew it
+        #     evaluated to a classvar
+
+        value = cls.__private_attributes__.pop(k, None)
+        if value is not None and value.default is not PydanticUndefined:
+            setattr(cls, k, value.default)
+
+
+def complete_model_class(
+    cls: type[BaseModel],
+    config_wrapper: ConfigWrapper,
+    ns_resolver: NsResolver,
+    *,
+    raise_errors: bool = True,
+    call_on_complete_hook: bool = True,
+    create_model_module: str | None = None,
+    is_force_rebuild: bool = False,
+) -> bool:
+    """Finish building a model class.
+
+    This logic must be called after class has been created since validation functions must be bound
+    and `get_type_hints` requires a class object.
+
+    Args:
+        cls: BaseModel or dataclass.
+        config_wrapper: The config wrapper instance.
+        ns_resolver: The namespace resolver instance to use during schema building.
+        raise_errors: Whether to raise errors.
+        call_on_complete_hook: Whether to call the `__pydantic_on_complete__` hook.
+        create_model_module: The module of the class to be created, if created by `create_model`.
+        is_force_rebuild: Whether the model is being force-rebuilt (if True, pre-built serializers and
+                          validators are not used, to avoid stale references).
+
+    Returns:
+        `True` if the model is successfully completed, else `False`.
+
+    Raises:
+        PydanticUndefinedAnnotation: If PydanticUndefinedAnnotation occurs in __get_pydantic_core_schema__
+            and `raise_errors=True`.
+    """
+    typevars_map = get_model_typevars_map(cls)
+
+    if not cls.__pydantic_fields_complete__:
+        # Note: when coming from `ModelMetaclass.__new__()`, this results in fields being built twice.
+        # We do so a second time here so that we can get the ``NameError`` for the specific undefined annotation.
+        # Alternatively, we could let `GenerateSchema()` raise the error, but there are cases where incomplete
+        # fields are inherited in `collect_model_fields()` and can actually have their annotation resolved in the
+        # generate schema process. As we want to avoid having `__pydantic_fields_complete__` set to `False`
+        # when `__pydantic_complete__` is `True`, we rebuild here:
+        try:
+            cls.__pydantic_fields__, cls.__pydantic_extra_info__ = rebuild_model_fields(
+                cls,
+                config_wrapper=config_wrapper,
+                ns_resolver=ns_resolver,
+                typevars_map=typevars_map,
+            )
+        except NameError as e:
+            exc = PydanticUndefinedAnnotation.from_name_error(e)
+            set_model_mocks(cls, f'`{exc.name}`')
+            if raise_errors:
+                raise exc from e
+
+        if not raise_errors and not cls.__pydantic_fields_complete__:
+            # No need to continue with schema gen, it is guaranteed to fail
+            return False
+
+        assert cls.__pydantic_fields_complete__
+
+    gen_schema = GenerateSchema(
+        config_wrapper,
+        ns_resolver,
+        typevars_map,
+    )
+
+    try:
+        schema = gen_schema.generate_schema(cls)
+    except PydanticUndefinedAnnotation as e:
+        if raise_errors:
+            raise
+        set_model_mocks(cls, f'`{e.name}`')
+        return False
+
+    core_config = config_wrapper.core_config(title=cls.__name__)
+
+    try:
+        schema = gen_schema.clean_schema(schema)
+    except InvalidSchemaError:
+        set_model_mocks(cls)
+        return False
+
+    # This needs to happen *after* model schema generation, as the return types
+    # of the properties are evaluated and the `ComputedFieldInfo` are recreated:
+    cls.__pydantic_computed_fields__ = {k: v.info for k, v in cls.__pydantic_decorators__.computed_fields.items()}
+
+    set_deprecated_descriptors(cls)
+
+    cls.__pydantic_core_schema__ = schema
+
+    cls.__pydantic_validator__ = create_schema_validator(
+        schema,
+        cls,
+        create_model_module or cls.__module__,
+        cls.__qualname__,
+        'create_model' if create_model_module else 'BaseModel',
+        core_config,
+        config_wrapper.plugin_settings,
+        _use_prebuilt=not is_force_rebuild,
+    )
+    cls.__pydantic_serializer__ = SchemaSerializer(schema, core_config, _use_prebuilt=not is_force_rebuild)
+
+    # set __signature__ attr only for model class, but not for its instances
+    # (because instances can define `__call__`, and `inspect.signature` shouldn't
+    # use the `__signature__` attribute and instead generate from `__call__`).
+    cls.__signature__ = LazyClassAttribute(
+        '__signature__',
+        partial(
+            generate_pydantic_signature,
+            init=cls.__init__,
+            fields=cls.__pydantic_fields__,
+            validate_by_name=config_wrapper.validate_by_name,
+            extra=config_wrapper.extra,
+        ),
+    )
+
+    cls.__pydantic_complete__ = True
+
+    if call_on_complete_hook:
+        cls.__pydantic_on_complete__()
+
+    return True
+
+
+def set_deprecated_descriptors(cls: type[BaseModel]) -> None:
+    """Set data descriptors on the class for deprecated fields."""
+    for field, field_info in cls.__pydantic_fields__.items():
+        if (msg := field_info.deprecation_message) is not None:
+            desc = _DeprecatedFieldDescriptor(msg)
+            desc.__set_name__(cls, field)
+            setattr(cls, field, desc)
+
+    for field, computed_field_info in cls.__pydantic_computed_fields__.items():
+        if (
+            (msg := computed_field_info.deprecation_message) is not None
+            # Avoid having two warnings emitted:
+            and not hasattr(unwrap_wrapped_function(computed_field_info.wrapped_property), '__deprecated__')
+        ):
+            desc = _DeprecatedFieldDescriptor(msg, computed_field_info.wrapped_property)
+            desc.__set_name__(cls, field)
+            setattr(cls, field, desc)
+
+
+class _DeprecatedFieldDescriptor:
+    """Read-only data descriptor used to emit a runtime deprecation warning before accessing a deprecated field.
+
+    Attributes:
+        msg: The deprecation message to be emitted.
+        wrapped_property: The property instance if the deprecated field is a computed field, or `None`.
+        field_name: The name of the field being deprecated.
+    """
+
+    field_name: str
+
+    def __init__(self, msg: str, wrapped_property: property | None = None) -> None:
+        self.msg = msg
+        self.wrapped_property = wrapped_property
+
+    def __set_name__(self, cls: type[BaseModel], name: str) -> None:
+        self.field_name = name
+
+    def __get__(self, obj: BaseModel | None, obj_type: type[BaseModel] | None = None) -> Any:
+        if obj is None:
+            if self.wrapped_property is not None:
+                return self.wrapped_property.__get__(None, obj_type)
+            raise AttributeError(self.field_name)
+
+        warnings.warn(self.msg, DeprecationWarning, stacklevel=2)
+
+        if self.wrapped_property is not None:
+            return self.wrapped_property.__get__(obj, obj_type)
+        return obj.__dict__[self.field_name]
+
+    # Defined to make it a data descriptor and take precedence over the instance's dictionary.
+    # Note that it will not be called when setting a value on a model instance
+    # as `BaseModel.__setattr__` is defined and takes priority.
+    def __set__(self, obj: Any, value: Any) -> NoReturn:
+        raise AttributeError(self.field_name)
+
+
+class _PydanticWeakRef:
+    """Wrapper for `weakref.ref` that enables `pickle` serialization.
+
+    Cloudpickle fails to serialize weakref.ref objects due to an arcane error related to
+    to abstract base classes (`abc.ABC`). This class works around the issue by wrapping
+    `weakref.ref` instead of subclassing it.
+
+    See https://github.com/pydantic/pydantic/issues/6763 for context.
+
+    Semantics:
+        - If not pickled, behaves the same as a `weakref.ref`.
+        - If pickled along with the referenced object, the same `weakref.ref` behavior
+          will be maintained between them after unpickling.
+        - If pickled without the referenced object, after unpickling the underlying
+          reference will be cleared (`__call__` will always return `None`).
+    """
+
+    def __init__(self, obj: Any):
+        if obj is None:
+            # The object will be `None` upon deserialization if the serialized weakref
+            # had lost its underlying object.
+            self._wr = None
+        else:
+            self._wr = weakref.ref(obj)
+
+    def __call__(self) -> Any:
+        if self._wr is None:
+            return None
+        else:
+            return self._wr()
+
+    def __reduce__(self) -> tuple[Callable, tuple[weakref.ReferenceType | None]]:
+        return _PydanticWeakRef, (self(),)
+
+
+def build_lenient_weakvaluedict(d: dict[str, Any] | None) -> dict[str, Any] | None:
+    """Takes an input dictionary, and produces a new value that (invertibly) replaces the values with weakrefs.
+
+    We can't just use a WeakValueDictionary because many types (including int, str, etc.) can't be stored as values
+    in a WeakValueDictionary.
+
+    The `unpack_lenient_weakvaluedict` function can be used to reverse this operation.
+    """
+    if d is None:
+        return None
+    result = {}
+    for k, v in d.items():
+        try:
+            proxy = _PydanticWeakRef(v)
+        except TypeError:
+            proxy = v
+        result[k] = proxy
+    return result
+
+
+def unpack_lenient_weakvaluedict(d: dict[str, Any] | None) -> dict[str, Any] | None:
+    """Inverts the transform performed by `build_lenient_weakvaluedict`."""
+    if d is None:
+        return None
+
+    result = {}
+    for k, v in d.items():
+        if isinstance(v, _PydanticWeakRef):
+            v = v()
+            if v is not None:
+                result[k] = v
+        else:
+            result[k] = v
+    return result
+
+
+@cache
+def default_ignored_types() -> tuple[type[Any], ...]:
+    from ..fields import ComputedFieldInfo
+
+    ignored_types = [
+        FunctionType,
+        property,
+        classmethod,
+        staticmethod,
+        PydanticDescriptorProxy,
+        ComputedFieldInfo,
+        TypeAliasType,  # from `typing_extensions`
+    ]
+
+    if sys.version_info >= (3, 12):
+        ignored_types.append(typing.TypeAliasType)
+
+    return tuple(ignored_types)

.mcp-venv/lib/python3.13/site-packages/pydantic/_internal/_namespace_utils.py

@@ -0,0 +1,293 @@
+from __future__ import annotations
+
+import sys
+from collections.abc import Generator, Iterator, Mapping
+from contextlib import contextmanager
+from functools import cached_property
+from typing import Any, Callable, NamedTuple, TypeVar
+
+from typing_extensions import ParamSpec, TypeAlias, TypeAliasType, TypeVarTuple
+
+GlobalsNamespace: TypeAlias = 'dict[str, Any]'
+"""A global namespace.
+
+In most cases, this is a reference to the `__dict__` attribute of a module.
+This namespace type is expected as the `globals` argument during annotations evaluation.
+"""
+
+MappingNamespace: TypeAlias = Mapping[str, Any]
+"""Any kind of namespace.
+
+In most cases, this is a local namespace (e.g. the `__dict__` attribute of a class,
+the [`f_locals`][frame.f_locals] attribute of a frame object, when dealing with types
+defined inside functions).
+This namespace type is expected as the `locals` argument during annotations evaluation.
+"""
+
+_TypeVarLike: TypeAlias = 'TypeVar | ParamSpec | TypeVarTuple'
+
+
+class NamespacesTuple(NamedTuple):
+    """A tuple of globals and locals to be used during annotations evaluation.
+
+    This datastructure is defined as a named tuple so that it can easily be unpacked:
+
+    ```python {lint="skip" test="skip"}
+    def eval_type(typ: type[Any], ns: NamespacesTuple) -> None:
+        return eval(typ, *ns)
+    ```
+    """
+
+    globals: GlobalsNamespace
+    """The namespace to be used as the `globals` argument during annotations evaluation."""
+
+    locals: MappingNamespace
+    """The namespace to be used as the `locals` argument during annotations evaluation."""
+
+
+def get_module_ns_of(obj: Any) -> dict[str, Any]:
+    """Get the namespace of the module where the object is defined.
+
+    Caution: this function does not return a copy of the module namespace, so the result
+    should not be mutated. The burden of enforcing this is on the caller.
+    """
+    module_name = getattr(obj, '__module__', None)
+    if module_name:
+        try:
+            return sys.modules[module_name].__dict__
+        except KeyError:
+            # happens occasionally, see https://github.com/pydantic/pydantic/issues/2363
+            return {}
+    return {}
+
+
+# Note that this class is almost identical to `collections.ChainMap`, but need to enforce
+# immutable mappings here:
+class LazyLocalNamespace(Mapping[str, Any]):
+    """A lazily evaluated mapping, to be used as the `locals` argument during annotations evaluation.
+
+    While the [`eval`][eval] function expects a mapping as the `locals` argument, it only
+    performs `__getitem__` calls. The [`Mapping`][collections.abc.Mapping] abstract base class
+    is fully implemented only for type checking purposes.
+
+    Args:
+        *namespaces: The namespaces to consider, in ascending order of priority.
+
+    Example:
+        ```python {lint="skip" test="skip"}
+        ns = LazyLocalNamespace({'a': 1, 'b': 2}, {'a': 3})
+        ns['a']
+        #> 3
+        ns['b']
+        #> 2
+        ```
+    """
+
+    def __init__(self, *namespaces: MappingNamespace) -> None:
+        self._namespaces = namespaces
+
+    @cached_property
+    def data(self) -> dict[str, Any]:
+        return {k: v for ns in self._namespaces for k, v in ns.items()}
+
+    def __len__(self) -> int:
+        return len(self.data)
+
+    def __getitem__(self, key: str) -> Any:
+        return self.data[key]
+
+    def __contains__(self, key: object) -> bool:
+        return key in self.data
+
+    def __iter__(self) -> Iterator[str]:
+        return iter(self.data)
+
+
+def ns_for_function(obj: Callable[..., Any], parent_namespace: MappingNamespace | None = None) -> NamespacesTuple:
+    """Return the global and local namespaces to be used when evaluating annotations for the provided function.
+
+    The global namespace will be the `__dict__` attribute of the module the function was defined in.
+    The local namespace will contain the `__type_params__` introduced by PEP 695.
+
+    Args:
+        obj: The object to use when building namespaces.
+        parent_namespace: Optional namespace to be added with the lowest priority in the local namespace.
+            If the passed function is a method, the `parent_namespace` will be the namespace of the class
+            the method is defined in. Thus, we also fetch type `__type_params__` from there (i.e. the
+            class-scoped type variables).
+    """
+    locals_list: list[MappingNamespace] = []
+    if parent_namespace is not None:
+        locals_list.append(parent_namespace)
+
+    # Get the `__type_params__` attribute introduced by PEP 695.
+    # Note that the `typing._eval_type` function expects type params to be
+    # passed as a separate argument. However, internally, `_eval_type` calls
+    # `ForwardRef._evaluate` which will merge type params with the localns,
+    # essentially mimicking what we do here.
+    type_params: tuple[_TypeVarLike, ...] = getattr(obj, '__type_params__', ())
+    if parent_namespace is not None:
+        # We also fetch type params from the parent namespace. If present, it probably
+        # means the function was defined in a class. This is to support the following:
+        # https://github.com/python/cpython/issues/124089.
+        type_params += parent_namespace.get('__type_params__', ())
+
+    locals_list.append({t.__name__: t for t in type_params})
+
+    # What about short-circuiting to `obj.__globals__`?
+    globalns = get_module_ns_of(obj)
+
+    return NamespacesTuple(globalns, LazyLocalNamespace(*locals_list))
+
+
+class NsResolver:
+    """A class responsible for the namespaces resolving logic for annotations evaluation.
+
+    This class handles the namespace logic when evaluating annotations mainly for class objects.
+
+    It holds a stack of classes that are being inspected during the core schema building,
+    and the `types_namespace` property exposes the globals and locals to be used for
+    type annotation evaluation. Additionally -- if no class is present in the stack -- a
+    fallback globals and locals can be provided using the `namespaces_tuple` argument
+    (this is useful when generating a schema for a simple annotation, e.g. when using
+    `TypeAdapter`).
+
+    The namespace creation logic is unfortunately flawed in some cases, for backwards
+    compatibility reasons and to better support valid edge cases. See the description
+    for the `parent_namespace` argument and the example for more details.
+
+    Args:
+        namespaces_tuple: The default globals and locals to use if no class is present
+            on the stack. This can be useful when using the `GenerateSchema` class
+            with `TypeAdapter`, where the "type" being analyzed is a simple annotation.
+        parent_namespace: An optional parent namespace that will be added to the locals
+            with the lowest priority. For a given class defined in a function, the locals
+            of this function are usually used as the parent namespace:
+
+            ```python {lint="skip" test="skip"}
+            from pydantic import BaseModel
+
+            def func() -> None:
+                SomeType = int
+
+                class Model(BaseModel):
+                    f: 'SomeType'
+
+                # when collecting fields, an namespace resolver instance will be created
+                # this way:
+                # ns_resolver = NsResolver(parent_namespace={'SomeType': SomeType})
+            ```
+
+            For backwards compatibility reasons and to support valid edge cases, this parent
+            namespace will be used for *every* type being pushed to the stack. In the future,
+            we might want to be smarter by only doing so when the type being pushed is defined
+            in the same module as the parent namespace.
+
+    Example:
+        ```python {lint="skip" test="skip"}
+        ns_resolver = NsResolver(
+            parent_namespace={'fallback': 1},
+        )
+
+        class Sub:
+            m: 'Model'
+
+        class Model:
+            some_local = 1
+            sub: Sub
+
+        ns_resolver = NsResolver()
+
+        # This is roughly what happens when we build a core schema for `Model`:
+        with ns_resolver.push(Model):
+            ns_resolver.types_namespace
+            #> NamespacesTuple({'Sub': Sub}, {'Model': Model, 'some_local': 1})
+            # First thing to notice here, the model being pushed is added to the locals.
+            # Because `NsResolver` is being used during the model definition, it is not
+            # yet added to the globals. This is useful when resolving self-referencing annotations.
+
+            with ns_resolver.push(Sub):
+                ns_resolver.types_namespace
+                #> NamespacesTuple({'Sub': Sub}, {'Sub': Sub, 'Model': Model})
+                # Second thing to notice: `Sub` is present in both the globals and locals.
+                # This is not an issue, just that as described above, the model being pushed
+                # is added to the locals, but it happens to be present in the globals as well
+                # because it is already defined.
+                # Third thing to notice: `Model` is also added in locals. This is a backwards
+                # compatibility workaround that allows for `Sub` to be able to resolve `'Model'`
+                # correctly (as otherwise models would have to be rebuilt even though this
+                # doesn't look necessary).
+        ```
+    """
+
+    def __init__(
+        self,
+        namespaces_tuple: NamespacesTuple | None = None,
+        parent_namespace: MappingNamespace | None = None,
+    ) -> None:
+        self._base_ns_tuple = namespaces_tuple or NamespacesTuple({}, {})
+        self._parent_ns = parent_namespace
+        self._types_stack: list[type[Any] | TypeAliasType] = []
+
+    @cached_property
+    def types_namespace(self) -> NamespacesTuple:
+        """The current global and local namespaces to be used for annotations evaluation."""
+        if not self._types_stack:
+            # TODO: should we merge the parent namespace here?
+            # This is relevant for TypeAdapter, where there are no types on the stack, and we might
+            # need access to the parent_ns. Right now, we sidestep this in `type_adapter.py` by passing
+            # locals to both parent_ns and the base_ns_tuple, but this is a bit hacky.
+            # we might consider something like:
+            # if self._parent_ns is not None:
+            #     # Hacky workarounds, see class docstring:
+            #     # An optional parent namespace that will be added to the locals with the lowest priority
+            #     locals_list: list[MappingNamespace] = [self._parent_ns, self._base_ns_tuple.locals]
+            #     return NamespacesTuple(self._base_ns_tuple.globals, LazyLocalNamespace(*locals_list))
+            return self._base_ns_tuple
+
+        typ = self._types_stack[-1]
+
+        globalns = get_module_ns_of(typ)
+
+        locals_list: list[MappingNamespace] = []
+        # Hacky workarounds, see class docstring:
+        # An optional parent namespace that will be added to the locals with the lowest priority
+        if self._parent_ns is not None:
+            locals_list.append(self._parent_ns)
+        if len(self._types_stack) > 1:
+            first_type = self._types_stack[0]
+            locals_list.append({first_type.__name__: first_type})
+
+        # Adding `__type_params__` *before* `vars(typ)`, as the latter takes priority
+        # (see https://github.com/python/cpython/pull/120272).
+        # TODO `typ.__type_params__` when we drop support for Python 3.11:
+        type_params: tuple[_TypeVarLike, ...] = getattr(typ, '__type_params__', ())
+        if type_params:
+            # Adding `__type_params__` is mostly useful for generic classes defined using
+            # PEP 695 syntax *and* using forward annotations (see the example in
+            # https://github.com/python/cpython/issues/114053). For TypeAliasType instances,
+            # it is way less common, but still required if using a string annotation in the alias
+            # value, e.g. `type A[T] = 'T'` (which is not necessary in most cases).
+            locals_list.append({t.__name__: t for t in type_params})
+
+        # TypeAliasType instances don't have a `__dict__` attribute, so the check
+        # is necessary:
+        if hasattr(typ, '__dict__'):
+            locals_list.append(vars(typ))
+
+        # The `len(self._types_stack) > 1` check above prevents this from being added twice:
+        locals_list.append({typ.__name__: typ})
+
+        return NamespacesTuple(globalns, LazyLocalNamespace(*locals_list))
+
+    @contextmanager
+    def push(self, typ: type[Any] | TypeAliasType, /) -> Generator[None]:
+        """Push a type to the stack."""
+        self._types_stack.append(typ)
+        # Reset the cached property:
+        self.__dict__.pop('types_namespace', None)
+        try:
+            yield
+        finally:
+            self._types_stack.pop()
+            self.__dict__.pop('types_namespace', None)

.mcp-venv/lib/python3.13/site-packages/pydantic/_internal/_repr.py

@@ -0,0 +1,124 @@
+"""Tools to provide pretty/human-readable display of objects."""
+
+from __future__ import annotations as _annotations
+
+import types
+from collections.abc import Callable, Collection, Generator, Iterable
+from typing import TYPE_CHECKING, Any, ForwardRef, cast
+
+import typing_extensions
+from typing_extensions import TypeAlias
+from typing_inspection import typing_objects
+from typing_inspection.introspection import is_union_origin
+
+from . import _typing_extra
+
+if TYPE_CHECKING:
+    # TODO remove type error comments when we drop support for Python 3.9
+    ReprArgs: TypeAlias = Iterable[tuple[str | None, Any]]  # pyright: ignore[reportGeneralTypeIssues]
+    RichReprResult: TypeAlias = Iterable[Any | tuple[Any] | tuple[str, Any] | tuple[str, Any, Any]]  # pyright: ignore[reportGeneralTypeIssues]
+
+
+class PlainRepr(str):
+    """String class where repr doesn't include quotes. Useful with Representation when you want to return a string
+    representation of something that is valid (or pseudo-valid) python.
+    """
+
+    def __repr__(self) -> str:
+        return str(self)
+
+
+class Representation:
+    # Mixin to provide `__str__`, `__repr__`, and `__pretty__` and `__rich_repr__` methods.
+    # `__pretty__` is used by [devtools](https://python-devtools.helpmanual.io/).
+    # `__rich_repr__` is used by [rich](https://rich.readthedocs.io/en/stable/pretty.html).
+    # (this is not a docstring to avoid adding a docstring to classes which inherit from Representation)
+
+    __slots__ = ()
+
+    def __repr_args__(self) -> ReprArgs:
+        """Returns the attributes to show in __str__, __repr__, and __pretty__ this is generally overridden.
+
+        Can either return:
+        * name - value pairs, e.g.: `[('foo_name', 'foo'), ('bar_name', ['b', 'a', 'r'])]`
+        * or, just values, e.g.: `[(None, 'foo'), (None, ['b', 'a', 'r'])]`
+        """
+        attrs_names = cast(Collection[str], self.__slots__)
+        if not attrs_names and hasattr(self, '__dict__'):
+            attrs_names = self.__dict__.keys()
+        attrs = ((s, getattr(self, s)) for s in attrs_names)
+        return [(a, v if v is not self else self.__repr_recursion__(v)) for a, v in attrs if v is not None]
+
+    def __repr_name__(self) -> str:
+        """Name of the instance's class, used in __repr__."""
+        return self.__class__.__name__
+
+    def __repr_recursion__(self, object: Any) -> str:
+        """Returns the string representation of a recursive object."""
+        # This is copied over from the stdlib `pprint` module:
+        return f'<Recursion on {type(object).__name__} with id={id(object)}>'
+
+    def __repr_str__(self, join_str: str) -> str:
+        return join_str.join(repr(v) if a is None else f'{a}={v!r}' for a, v in self.__repr_args__())
+
+    def __pretty__(self, fmt: Callable[[Any], Any], **kwargs: Any) -> Generator[Any]:
+        """Used by devtools (https://python-devtools.helpmanual.io/) to pretty print objects."""
+        yield self.__repr_name__() + '('
+        yield 1
+        for name, value in self.__repr_args__():
+            if name is not None:
+                yield name + '='
+            yield fmt(value)
+            yield ','
+            yield 0
+        yield -1
+        yield ')'
+
+    def __rich_repr__(self) -> RichReprResult:
+        """Used by Rich (https://rich.readthedocs.io/en/stable/pretty.html) to pretty print objects."""
+        for name, field_repr in self.__repr_args__():
+            if name is None:
+                yield field_repr
+            else:
+                yield name, field_repr
+
+    def __str__(self) -> str:
+        return self.__repr_str__(' ')
+
+    def __repr__(self) -> str:
+        return f'{self.__repr_name__()}({self.__repr_str__(", ")})'
+
+
+def display_as_type(obj: Any) -> str:
+    """Pretty representation of a type, should be as close as possible to the original type definition string.
+
+    Takes some logic from `typing._type_repr`.
+    """
+    if isinstance(obj, (types.FunctionType, types.BuiltinFunctionType)):
+        return obj.__name__
+    elif obj is ...:
+        return '...'
+    elif isinstance(obj, Representation):
+        return repr(obj)
+    elif isinstance(obj, ForwardRef) or typing_objects.is_typealiastype(obj):
+        return str(obj)
+
+    if not isinstance(obj, (_typing_extra.typing_base, _typing_extra.WithArgsTypes, type)):
+        obj = obj.__class__
+
+    if is_union_origin(typing_extensions.get_origin(obj)):
+        args = ', '.join(map(display_as_type, typing_extensions.get_args(obj)))
+        return f'Union[{args}]'
+    elif isinstance(obj, _typing_extra.WithArgsTypes):
+        if typing_objects.is_literal(typing_extensions.get_origin(obj)):
+            args = ', '.join(map(repr, typing_extensions.get_args(obj)))
+        else:
+            args = ', '.join(map(display_as_type, typing_extensions.get_args(obj)))
+        try:
+            return f'{obj.__qualname__}[{args}]'
+        except AttributeError:
+            return str(obj).replace('typing.', '').replace('typing_extensions.', '')  # handles TypeAliasType in 3.12
+    elif isinstance(obj, type):
+        return obj.__qualname__
+    else:
+        return repr(obj).replace('typing.', '').replace('typing_extensions.', '')

.mcp-venv/lib/python3.13/site-packages/pydantic/_internal/_schema_gather.py

@@ -0,0 +1,212 @@
+# pyright: reportTypedDictNotRequiredAccess=false, reportGeneralTypeIssues=false, reportArgumentType=false, reportAttributeAccessIssue=false
+from __future__ import annotations
+
+from dataclasses import dataclass, field
+from typing import TypedDict
+
+from pydantic_core.core_schema import (
+    ComputedField,
+    CoreSchema,
+    DefinitionReferenceSchema,
+    SerSchema,
+    iter_union_choices,
+)
+from typing_extensions import TypeAlias
+
+AllSchemas: TypeAlias = 'CoreSchema | SerSchema | ComputedField'
+
+
+class GatherResult(TypedDict):
+    """Schema traversing result."""
+
+    collected_references: dict[str, DefinitionReferenceSchema | None]
+    """The collected definition references.
+
+    If a definition reference schema can be inlined, it means that there is
+    only one in the whole core schema. As such, it is stored as the value.
+    Otherwise, the value is set to `None`.
+    """
+
+    deferred_discriminator_schemas: list[CoreSchema]
+    """The list of core schemas having the discriminator application deferred."""
+
+
+class MissingDefinitionError(LookupError):
+    """A reference was pointing to a non-existing core schema."""
+
+    def __init__(self, schema_reference: str, /) -> None:
+        self.schema_reference = schema_reference
+
+
+@dataclass
+class GatherContext:
+    """The current context used during core schema traversing.
+
+    Context instances should only be used during schema traversing.
+    """
+
+    definitions: dict[str, CoreSchema]
+    """The available definitions."""
+
+    deferred_discriminator_schemas: list[CoreSchema] = field(init=False, default_factory=list)
+    """The list of core schemas having the discriminator application deferred.
+
+    Internally, these core schemas have a specific key set in the core metadata dict.
+    """
+
+    collected_references: dict[str, DefinitionReferenceSchema | None] = field(init=False, default_factory=dict)
+    """The collected definition references.
+
+    If a definition reference schema can be inlined, it means that there is
+    only one in the whole core schema. As such, it is stored as the value.
+    Otherwise, the value is set to `None`.
+
+    During schema traversing, definition reference schemas can be added as candidates, or removed
+    (by setting the value to `None`).
+    """
+
+
+def traverse_metadata(schema: AllSchemas, ctx: GatherContext) -> None:
+    meta = schema.get('metadata')
+    if meta is not None and 'pydantic_internal_union_discriminator' in meta:
+        ctx.deferred_discriminator_schemas.append(schema)  # pyright: ignore[reportArgumentType]
+
+
+def traverse_definition_ref(def_ref_schema: DefinitionReferenceSchema, ctx: GatherContext) -> None:
+    schema_ref = def_ref_schema['schema_ref']
+
+    if schema_ref not in ctx.collected_references:
+        definition = ctx.definitions.get(schema_ref)
+        if definition is None:
+            raise MissingDefinitionError(schema_ref)
+
+        # The `'definition-ref'` schema was only encountered once, make it
+        # a candidate to be inlined:
+        ctx.collected_references[schema_ref] = def_ref_schema
+        traverse_schema(definition, ctx)
+        if 'serialization' in def_ref_schema:
+            traverse_schema(def_ref_schema['serialization'], ctx)
+        traverse_metadata(def_ref_schema, ctx)
+    else:
+        # The `'definition-ref'` schema was already encountered, meaning
+        # the previously encountered schema (and this one) can't be inlined:
+        ctx.collected_references[schema_ref] = None
+
+
+def traverse_schema(schema: AllSchemas, context: GatherContext) -> None:
+    # TODO When we drop 3.9, use a match statement to get better type checking and remove
+    # file-level type ignore.
+    # (the `'type'` could also be fetched in every `if/elif` statement, but this alters performance).
+    schema_type = schema['type']
+
+    if schema_type == 'definition-ref':
+        traverse_definition_ref(schema, context)
+        # `traverse_definition_ref` handles the possible serialization and metadata schemas:
+        return
+    elif schema_type == 'definitions':
+        traverse_schema(schema['schema'], context)
+        for definition in schema['definitions']:
+            traverse_schema(definition, context)
+    elif schema_type in {'list', 'set', 'frozenset', 'generator'}:
+        if 'items_schema' in schema:
+            traverse_schema(schema['items_schema'], context)
+    elif schema_type == 'tuple':
+        if 'items_schema' in schema:
+            for s in schema['items_schema']:
+                traverse_schema(s, context)
+    elif schema_type == 'dict':
+        if 'keys_schema' in schema:
+            traverse_schema(schema['keys_schema'], context)
+        if 'values_schema' in schema:
+            traverse_schema(schema['values_schema'], context)
+    elif schema_type == 'union':
+        for choice in iter_union_choices(schema):
+            traverse_schema(choice, context)
+    elif schema_type == 'tagged-union':
+        for v in schema['choices'].values():
+            traverse_schema(v, context)
+    elif schema_type == 'chain':
+        for step in schema['steps']:
+            traverse_schema(step, context)
+    elif schema_type == 'lax-or-strict':
+        traverse_schema(schema['lax_schema'], context)
+        traverse_schema(schema['strict_schema'], context)
+    elif schema_type == 'json-or-python':
+        traverse_schema(schema['json_schema'], context)
+        traverse_schema(schema['python_schema'], context)
+    elif schema_type in {'model-fields', 'typed-dict'}:
+        if 'extras_schema' in schema:
+            traverse_schema(schema['extras_schema'], context)
+        if 'computed_fields' in schema:
+            for s in schema['computed_fields']:
+                traverse_schema(s, context)
+        for s in schema['fields'].values():
+            traverse_schema(s, context)
+    elif schema_type == 'dataclass-args':
+        if 'computed_fields' in schema:
+            for s in schema['computed_fields']:
+                traverse_schema(s, context)
+        for s in schema['fields']:
+            traverse_schema(s, context)
+    elif schema_type == 'arguments':
+        for s in schema['arguments_schema']:
+            traverse_schema(s['schema'], context)
+        if 'var_args_schema' in schema:
+            traverse_schema(schema['var_args_schema'], context)
+        if 'var_kwargs_schema' in schema:
+            traverse_schema(schema['var_kwargs_schema'], context)
+    elif schema_type == 'arguments-v3':
+        for s in schema['arguments_schema']:
+            traverse_schema(s['schema'], context)
+    elif schema_type == 'call':
+        traverse_schema(schema['arguments_schema'], context)
+        if 'return_schema' in schema:
+            traverse_schema(schema['return_schema'], context)
+    elif schema_type == 'computed-field':
+        traverse_schema(schema['return_schema'], context)
+    elif schema_type == 'function-before':
+        if 'schema' in schema:
+            traverse_schema(schema['schema'], context)
+        if 'json_schema_input_schema' in schema:
+            traverse_schema(schema['json_schema_input_schema'], context)
+    elif schema_type == 'function-plain':
+        # TODO duplicate schema types for serializers and validators, needs to be deduplicated.
+        if 'return_schema' in schema:
+            traverse_schema(schema['return_schema'], context)
+        if 'json_schema_input_schema' in schema:
+            traverse_schema(schema['json_schema_input_schema'], context)
+    elif schema_type == 'function-wrap':
+        # TODO duplicate schema types for serializers and validators, needs to be deduplicated.
+        if 'return_schema' in schema:
+            traverse_schema(schema['return_schema'], context)
+        if 'schema' in schema:
+            traverse_schema(schema['schema'], context)
+        if 'json_schema_input_schema' in schema:
+            traverse_schema(schema['json_schema_input_schema'], context)
+    else:
+        if 'schema' in schema:
+            traverse_schema(schema['schema'], context)
+
+    if 'serialization' in schema:
+        traverse_schema(schema['serialization'], context)
+    traverse_metadata(schema, context)
+
+
+def gather_schemas_for_cleaning(schema: CoreSchema, definitions: dict[str, CoreSchema]) -> GatherResult:
+    """Traverse the core schema and definitions and return the necessary information for schema cleaning.
+
+    During the core schema traversing, any `'definition-ref'` schema is:
+
+    - Validated: the reference must point to an existing definition. If this is not the case, a
+      `MissingDefinitionError` exception is raised.
+    - Stored in the context: the actual reference is stored in the context. Depending on whether
+      the `'definition-ref'` schema is encountered more that once, the schema itself is also
+      saved in the context to be inlined (i.e. replaced by the definition it points to).
+    """
+    context = GatherContext(definitions)
+    traverse_schema(schema, context)
+
+    return {
+        'collected_references': context.collected_references,
+        'deferred_discriminator_schemas': context.deferred_discriminator_schemas,
+    }

.mcp-venv/lib/python3.13/site-packages/pydantic/_internal/_schema_generation_shared.py

@@ -0,0 +1,125 @@
+"""Types and utility functions used by various other internal tools."""
+
+from __future__ import annotations
+
+from typing import TYPE_CHECKING, Any, Callable, Literal
+
+from pydantic_core import core_schema
+
+from ..annotated_handlers import GetCoreSchemaHandler, GetJsonSchemaHandler
+
+if TYPE_CHECKING:
+    from ..json_schema import GenerateJsonSchema, JsonSchemaValue
+    from ._core_utils import CoreSchemaOrField
+    from ._generate_schema import GenerateSchema
+    from ._namespace_utils import NamespacesTuple
+
+    GetJsonSchemaFunction = Callable[[CoreSchemaOrField, GetJsonSchemaHandler], JsonSchemaValue]
+    HandlerOverride = Callable[[CoreSchemaOrField], JsonSchemaValue]
+
+
+class GenerateJsonSchemaHandler(GetJsonSchemaHandler):
+    """JsonSchemaHandler implementation that doesn't do ref unwrapping by default.
+
+    This is used for any Annotated metadata so that we don't end up with conflicting
+    modifications to the definition schema.
+
+    Used internally by Pydantic, please do not rely on this implementation.
+    See `GetJsonSchemaHandler` for the handler API.
+    """
+
+    def __init__(self, generate_json_schema: GenerateJsonSchema, handler_override: HandlerOverride | None) -> None:
+        self.generate_json_schema = generate_json_schema
+        self.handler = handler_override or generate_json_schema.generate_inner
+        self.mode = generate_json_schema.mode
+
+    def __call__(self, core_schema: CoreSchemaOrField, /) -> JsonSchemaValue:
+        return self.handler(core_schema)
+
+    def resolve_ref_schema(self, maybe_ref_json_schema: JsonSchemaValue) -> JsonSchemaValue:
+        """Resolves `$ref` in the json schema.
+
+        This returns the input json schema if there is no `$ref` in json schema.
+
+        Args:
+            maybe_ref_json_schema: The input json schema that may contains `$ref`.
+
+        Returns:
+            Resolved json schema.
+
+        Raises:
+            LookupError: If it can't find the definition for `$ref`.
+        """
+        if '$ref' not in maybe_ref_json_schema:
+            return maybe_ref_json_schema
+        ref = maybe_ref_json_schema['$ref']
+        json_schema = self.generate_json_schema.get_schema_from_definitions(ref)
+        if json_schema is None:
+            raise LookupError(
+                f'Could not find a ref for {ref}.'
+                ' Maybe you tried to call resolve_ref_schema from within a recursive model?'
+            )
+        return json_schema
+
+
+class CallbackGetCoreSchemaHandler(GetCoreSchemaHandler):
+    """Wrapper to use an arbitrary function as a `GetCoreSchemaHandler`.
+
+    Used internally by Pydantic, please do not rely on this implementation.
+    See `GetCoreSchemaHandler` for the handler API.
+    """
+
+    def __init__(
+        self,
+        handler: Callable[[Any], core_schema.CoreSchema],
+        generate_schema: GenerateSchema,
+        ref_mode: Literal['to-def', 'unpack'] = 'to-def',
+    ) -> None:
+        self._handler = handler
+        self._generate_schema = generate_schema
+        self._ref_mode = ref_mode
+
+    def __call__(self, source_type: Any, /) -> core_schema.CoreSchema:
+        schema = self._handler(source_type)
+        if self._ref_mode == 'to-def':
+            ref = schema.get('ref')
+            if ref is not None:
+                return self._generate_schema.defs.create_definition_reference_schema(schema)
+            return schema
+        else:  # ref_mode = 'unpack'
+            return self.resolve_ref_schema(schema)
+
+    def _get_types_namespace(self) -> NamespacesTuple:
+        return self._generate_schema._types_namespace
+
+    def generate_schema(self, source_type: Any, /) -> core_schema.CoreSchema:
+        return self._generate_schema.generate_schema(source_type)
+
+    @property
+    def field_name(self) -> str | None:
+        return self._generate_schema.field_name_stack.get()
+
+    def resolve_ref_schema(self, maybe_ref_schema: core_schema.CoreSchema) -> core_schema.CoreSchema:
+        """Resolves reference in the core schema.
+
+        Args:
+            maybe_ref_schema: The input core schema that may contains reference.
+
+        Returns:
+            Resolved core schema.
+
+        Raises:
+            LookupError: If it can't find the definition for reference.
+        """
+        if maybe_ref_schema['type'] == 'definition-ref':
+            ref = maybe_ref_schema['schema_ref']
+            definition = self._generate_schema.defs.get_schema_from_ref(ref)
+            if definition is None:
+                raise LookupError(
+                    f'Could not find a ref for {ref}.'
+                    ' Maybe you tried to call resolve_ref_schema from within a recursive model?'
+                )
+            return definition
+        elif maybe_ref_schema['type'] == 'definitions':
+            return self.resolve_ref_schema(maybe_ref_schema['schema'])
+        return maybe_ref_schema

.mcp-venv/lib/python3.13/site-packages/pydantic/_internal/_serializers.py

@@ -0,0 +1,53 @@
+from __future__ import annotations
+
+import collections
+import collections.abc
+import typing
+from typing import Any
+
+from pydantic_core import PydanticOmit, core_schema
+
+SEQUENCE_ORIGIN_MAP: dict[Any, Any] = {
+    typing.Deque: collections.deque,  # noqa: UP006
+    collections.deque: collections.deque,
+    list: list,
+    typing.List: list,  # noqa: UP006
+    tuple: tuple,
+    typing.Tuple: tuple,  # noqa: UP006
+    set: set,
+    typing.AbstractSet: set,
+    typing.Set: set,  # noqa: UP006
+    frozenset: frozenset,
+    typing.FrozenSet: frozenset,  # noqa: UP006
+    typing.Sequence: list,
+    typing.MutableSequence: list,
+    typing.MutableSet: set,
+    # this doesn't handle subclasses of these
+    # parametrized typing.Set creates one of these
+    collections.abc.MutableSet: set,
+    collections.abc.Set: frozenset,
+}
+
+
+def serialize_sequence_via_list(
+    v: Any, handler: core_schema.SerializerFunctionWrapHandler, info: core_schema.SerializationInfo
+) -> Any:
+    items: list[Any] = []
+
+    mapped_origin = SEQUENCE_ORIGIN_MAP.get(type(v), None)
+    if mapped_origin is None:
+        # we shouldn't hit this branch, should probably add a serialization error or something
+        return v
+
+    for index, item in enumerate(v):
+        try:
+            v = handler(item, index)
+        except PydanticOmit:  # noqa: PERF203
+            pass
+        else:
+            items.append(v)
+
+    if info.mode_is_json():
+        return items
+    else:
+        return mapped_origin(items)

.mcp-venv/lib/python3.13/site-packages/pydantic/_internal/_signature.py

@@ -0,0 +1,189 @@
+from __future__ import annotations
+
+import dataclasses
+from inspect import Parameter, Signature
+from typing import TYPE_CHECKING, Any, Callable
+
+from pydantic_core import PydanticUndefined
+
+from ._typing_extra import signature_no_eval
+from ._utils import is_valid_identifier
+
+if TYPE_CHECKING:
+    from ..config import ExtraValues
+    from ..fields import FieldInfo
+
+
+# Copied over from stdlib dataclasses
+class _HAS_DEFAULT_FACTORY_CLASS:
+    def __repr__(self):
+        return '<factory>'
+
+
+_HAS_DEFAULT_FACTORY = _HAS_DEFAULT_FACTORY_CLASS()
+
+
+def _field_name_for_signature(field_name: str, field_info: FieldInfo) -> str:
+    """Extract the correct name to use for the field when generating a signature.
+
+    Assuming the field has a valid alias, this will return the alias. Otherwise, it will return the field name.
+    First priority is given to the alias, then the validation_alias, then the field name.
+
+    Args:
+        field_name: The name of the field
+        field_info: The corresponding FieldInfo object.
+
+    Returns:
+        The correct name to use when generating a signature.
+    """
+    if isinstance(field_info.alias, str) and is_valid_identifier(field_info.alias):
+        return field_info.alias
+    if isinstance(field_info.validation_alias, str) and is_valid_identifier(field_info.validation_alias):
+        return field_info.validation_alias
+
+    return field_name
+
+
+def _process_param_defaults(param: Parameter) -> Parameter:
+    """Modify the signature for a parameter in a dataclass where the default value is a FieldInfo instance.
+
+    Args:
+        param (Parameter): The parameter
+
+    Returns:
+        Parameter: The custom processed parameter
+    """
+    from ..fields import FieldInfo
+
+    param_default = param.default
+    if isinstance(param_default, FieldInfo):
+        annotation = param.annotation
+        # Replace the annotation if appropriate
+        # inspect does "clever" things to show annotations as strings because we have
+        # `from __future__ import annotations` in main, we don't want that
+        if annotation == 'Any':
+            annotation = Any
+
+        # Replace the field default
+        default = param_default.default
+        if default is PydanticUndefined:
+            if param_default.default_factory is None:
+                default = Signature.empty
+            else:
+                # this is used by dataclasses to indicate a factory exists:
+                default = dataclasses._HAS_DEFAULT_FACTORY  # type: ignore
+        return param.replace(
+            annotation=annotation, name=_field_name_for_signature(param.name, param_default), default=default
+        )
+    return param
+
+
+def _generate_signature_parameters(  # noqa: C901 (ignore complexity, could use a refactor)
+    init: Callable[..., None],
+    fields: dict[str, FieldInfo],
+    validate_by_name: bool,
+    extra: ExtraValues | None,
+) -> dict[str, Parameter]:
+    """Generate a mapping of parameter names to Parameter objects for a pydantic BaseModel or dataclass."""
+    from itertools import islice
+
+    present_params = signature_no_eval(init).parameters.values()
+    merged_params: dict[str, Parameter] = {}
+    var_kw = None
+    use_var_kw = False
+
+    for param in islice(present_params, 1, None):  # skip self arg
+        # inspect does "clever" things to show annotations as strings because we have
+        # `from __future__ import annotations` in main, we don't want that
+        if fields.get(param.name):
+            # exclude params with init=False
+            if getattr(fields[param.name], 'init', True) is False:
+                continue
+            param = param.replace(name=_field_name_for_signature(param.name, fields[param.name]))
+        if param.annotation == 'Any':
+            param = param.replace(annotation=Any)
+        if param.kind is param.VAR_KEYWORD:
+            var_kw = param
+            continue
+        merged_params[param.name] = param
+
+    if var_kw:  # if custom init has no var_kw, fields which are not declared in it cannot be passed through
+        allow_names = validate_by_name
+        for field_name, field in fields.items():
+            # when alias is a str it should be used for signature generation
+            param_name = _field_name_for_signature(field_name, field)
+
+            if field_name in merged_params or param_name in merged_params:
+                continue
+
+            if not is_valid_identifier(param_name):
+                if allow_names:
+                    param_name = field_name
+                else:
+                    use_var_kw = True
+                    continue
+
+            if field.is_required():
+                default = Parameter.empty
+            elif field.default_factory is not None:
+                # Mimics stdlib dataclasses:
+                default = _HAS_DEFAULT_FACTORY
+            else:
+                default = field.default
+            merged_params[param_name] = Parameter(
+                param_name,
+                Parameter.KEYWORD_ONLY,
+                annotation=field.rebuild_annotation(),
+                default=default,
+            )
+
+    if extra == 'allow':
+        use_var_kw = True
+
+    if var_kw and use_var_kw:
+        # Make sure the parameter for extra kwargs
+        # does not have the same name as a field
+        default_model_signature = [
+            ('self', Parameter.POSITIONAL_ONLY),
+            ('data', Parameter.VAR_KEYWORD),
+        ]
+        if [(p.name, p.kind) for p in present_params] == default_model_signature:
+            # if this is the standard model signature, use extra_data as the extra args name
+            var_kw_name = 'extra_data'
+        else:
+            # else start from var_kw
+            var_kw_name = var_kw.name
+
+        # generate a name that's definitely unique
+        while var_kw_name in fields:
+            var_kw_name += '_'
+        merged_params[var_kw_name] = var_kw.replace(name=var_kw_name)
+
+    return merged_params
+
+
+def generate_pydantic_signature(
+    init: Callable[..., None],
+    fields: dict[str, FieldInfo],
+    validate_by_name: bool,
+    extra: ExtraValues | None,
+    is_dataclass: bool = False,
+) -> Signature:
+    """Generate signature for a pydantic BaseModel or dataclass.
+
+    Args:
+        init: The class init.
+        fields: The model fields.
+        validate_by_name: The `validate_by_name` value of the config.
+        extra: The `extra` value of the config.
+        is_dataclass: Whether the model is a dataclass.
+
+    Returns:
+        The dataclass/BaseModel subclass signature.
+    """
+    merged_params = _generate_signature_parameters(init, fields, validate_by_name, extra)
+
+    if is_dataclass:
+        merged_params = {k: _process_param_defaults(v) for k, v in merged_params.items()}
+
+    return Signature(parameters=list(merged_params.values()), return_annotation=None)

.mcp-venv/lib/python3.13/site-packages/pydantic/_internal/_typing_extra.py

@@ -0,0 +1,785 @@
+"""Logic for interacting with type annotations, mostly extensions, shims and hacks to wrap Python's typing module."""
+
+from __future__ import annotations
+
+import collections.abc
+import re
+import sys
+import types
+import typing
+from functools import partial
+from inspect import Signature, signature
+from typing import TYPE_CHECKING, Any, Callable, cast
+
+import typing_extensions
+from typing_extensions import deprecated, get_args, get_origin
+from typing_inspection import typing_objects
+from typing_inspection.introspection import is_union_origin
+
+from pydantic.version import version_short
+
+from ._namespace_utils import GlobalsNamespace, MappingNamespace, NsResolver, get_module_ns_of
+
+if sys.version_info < (3, 10):
+    NoneType = type(None)
+    EllipsisType = type(Ellipsis)
+else:
+    from types import EllipsisType as EllipsisType
+    from types import NoneType as NoneType
+
+if sys.version_info >= (3, 14):
+    import annotationlib
+
+if TYPE_CHECKING:
+    from pydantic import BaseModel
+    from pydantic.fields import FieldInfo
+
+# As per https://typing-extensions.readthedocs.io/en/latest/#runtime-use-of-types,
+# always check for both `typing` and `typing_extensions` variants of a typing construct.
+# (this is implemented differently than the suggested approach in the `typing_extensions`
+# docs for performance).
+
+
+_t_annotated = typing.Annotated
+_te_annotated = typing_extensions.Annotated
+
+
+def is_annotated(tp: Any, /) -> bool:
+    """Return whether the provided argument is a `Annotated` special form.
+
+    ```python {test="skip" lint="skip"}
+    is_annotated(Annotated[int, ...])
+    #> True
+    ```
+    """
+    origin = get_origin(tp)
+    return origin is _t_annotated or origin is _te_annotated
+
+
+def annotated_type(tp: Any, /) -> Any | None:
+    """Return the type of the `Annotated` special form, or `None`."""
+    return tp.__origin__ if typing_objects.is_annotated(get_origin(tp)) else None
+
+
+def unpack_type(tp: Any, /) -> Any | None:
+    """Return the type wrapped by the `Unpack` special form, or `None`."""
+    return get_args(tp)[0] if typing_objects.is_unpack(get_origin(tp)) else None
+
+
+def is_hashable(tp: Any, /) -> bool:
+    """Return whether the provided argument is the `Hashable` class.
+
+    ```python {test="skip" lint="skip"}
+    is_hashable(Hashable)
+    #> True
+    ```
+    """
+    # `get_origin` is documented as normalizing any typing-module aliases to `collections` classes,
+    # hence the second check:
+    return tp is collections.abc.Hashable or get_origin(tp) is collections.abc.Hashable
+
+
+def is_callable(tp: Any, /) -> bool:
+    """Return whether the provided argument is a `Callable`, parametrized or not.
+
+    ```python {test="skip" lint="skip"}
+    is_callable(Callable[[int], str])
+    #> True
+    is_callable(typing.Callable)
+    #> True
+    is_callable(collections.abc.Callable)
+    #> True
+    ```
+    """
+    # `get_origin` is documented as normalizing any typing-module aliases to `collections` classes,
+    # hence the second check:
+    return tp is collections.abc.Callable or get_origin(tp) is collections.abc.Callable
+
+
+_classvar_re = re.compile(r'((\w+\.)?Annotated\[)?(\w+\.)?ClassVar\[')
+
+
+def is_classvar_annotation(tp: Any, /) -> bool:
+    """Return whether the provided argument represents a class variable annotation.
+
+    Although not explicitly stated by the typing specification, `ClassVar` can be used
+    inside `Annotated` and as such, this function checks for this specific scenario.
+
+    Because this function is used to detect class variables before evaluating forward references
+    (or because evaluation failed), we also implement a naive regex match implementation. This is
+    required because class variables are inspected before fields are collected, so we try to be
+    as accurate as possible.
+    """
+    if typing_objects.is_classvar(tp):
+        return True
+
+    origin = get_origin(tp)
+
+    if typing_objects.is_classvar(origin):
+        return True
+
+    if typing_objects.is_annotated(origin):
+        annotated_type = tp.__origin__
+        if typing_objects.is_classvar(annotated_type) or typing_objects.is_classvar(get_origin(annotated_type)):
+            return True
+
+    str_ann: str | None = None
+    if isinstance(tp, typing.ForwardRef):
+        str_ann = tp.__forward_arg__
+    if isinstance(tp, str):
+        str_ann = tp
+
+    if str_ann is not None and _classvar_re.match(str_ann):
+        # stdlib dataclasses do something similar, although a bit more advanced
+        # (see `dataclass._is_type`).
+        return True
+
+    return False
+
+
+_t_final = typing.Final
+_te_final = typing_extensions.Final
+
+
+# TODO implement `is_finalvar_annotation` as Final can be wrapped with other special forms:
+def is_finalvar(tp: Any, /) -> bool:
+    """Return whether the provided argument is a `Final` special form, parametrized or not.
+
+    ```python {test="skip" lint="skip"}
+    is_finalvar(Final[int])
+    #> True
+    is_finalvar(Final)
+    #> True
+    """
+    # Final is not necessarily parametrized:
+    if tp is _t_final or tp is _te_final:
+        return True
+    origin = get_origin(tp)
+    return origin is _t_final or origin is _te_final
+
+
+_NONE_TYPES: tuple[Any, ...] = (None, NoneType, typing.Literal[None], typing_extensions.Literal[None])
+
+
+def is_none_type(tp: Any, /) -> bool:
+    """Return whether the argument represents the `None` type as part of an annotation.
+
+    ```python {test="skip" lint="skip"}
+    is_none_type(None)
+    #> True
+    is_none_type(NoneType)
+    #> True
+    is_none_type(Literal[None])
+    #> True
+    is_none_type(type[None])
+    #> False
+    """
+    return tp in _NONE_TYPES
+
+
+def is_namedtuple(tp: Any, /) -> bool:
+    """Return whether the provided argument is a named tuple class.
+
+    The class can be created using `typing.NamedTuple` or `collections.namedtuple`.
+    Parametrized generic classes are *not* assumed to be named tuples.
+    """
+    from ._utils import lenient_issubclass  # circ. import
+
+    return lenient_issubclass(tp, tuple) and hasattr(tp, '_fields')
+
+
+# TODO In 2.12, delete this export. It is currently defined only to not break
+# pydantic-settings which relies on it:
+origin_is_union = is_union_origin
+
+
+def is_generic_alias(tp: Any, /) -> bool:
+    return isinstance(tp, (types.GenericAlias, typing._GenericAlias))  # pyright: ignore[reportAttributeAccessIssue]
+
+
+# TODO: Ideally, we should avoid relying on the private `typing` constructs:
+
+if sys.version_info < (3, 10):
+    WithArgsTypes: tuple[Any, ...] = (typing._GenericAlias, types.GenericAlias)  # pyright: ignore[reportAttributeAccessIssue]
+else:
+    WithArgsTypes: tuple[Any, ...] = (typing._GenericAlias, types.GenericAlias, types.UnionType)  # pyright: ignore[reportAttributeAccessIssue]
+
+
+# Similarly, we shouldn't rely on this `_Final` class, which is even more private than `_GenericAlias`:
+typing_base: Any = typing._Final  # pyright: ignore[reportAttributeAccessIssue]
+
+
+### Annotation evaluations functions:
+
+
+def parent_frame_namespace(*, parent_depth: int = 2, force: bool = False) -> dict[str, Any] | None:
+    """Fetch the local namespace of the parent frame where this function is called.
+
+    Using this function is mostly useful to resolve forward annotations pointing to members defined in a local namespace,
+    such as assignments inside a function. Using the standard library tools, it is currently not possible to resolve
+    such annotations:
+
+    ```python {lint="skip" test="skip"}
+    from typing import get_type_hints
+
+    def func() -> None:
+        Alias = int
+
+        class C:
+            a: 'Alias'
+
+        # Raises a `NameError: 'Alias' is not defined`
+        get_type_hints(C)
+    ```
+
+    Pydantic uses this function when a Pydantic model is being defined to fetch the parent frame locals. However,
+    this only allows us to fetch the parent frame namespace and not other parents (e.g. a model defined in a function,
+    itself defined in another function). Inspecting the next outer frames (using `f_back`) is not reliable enough
+    (see https://discuss.python.org/t/20659).
+
+    Because this function is mostly used to better resolve forward annotations, nothing is returned if the parent frame's
+    code object is defined at the module level. In this case, the locals of the frame will be the same as the module
+    globals where the class is defined (see `_namespace_utils.get_module_ns_of`). However, if you still want to fetch
+    the module globals (e.g. when rebuilding a model, where the frame where the rebuild call is performed might contain
+    members that you want to use for forward annotations evaluation), you can use the `force` parameter.
+
+    Args:
+        parent_depth: The depth at which to get the frame. Defaults to 2, meaning the parent frame where this function
+            is called will be used.
+        force: Whether to always return the frame locals, even if the frame's code object is defined at the module level.
+
+    Returns:
+        The locals of the namespace, or `None` if it was skipped as per the described logic.
+    """
+    frame = sys._getframe(parent_depth)
+
+    if frame.f_code.co_name.startswith('<generic parameters of'):
+        # As `parent_frame_namespace` is mostly called in `ModelMetaclass.__new__`,
+        # the parent frame can be the annotation scope if the PEP 695 generic syntax is used.
+        # (see https://docs.python.org/3/reference/executionmodel.html#annotation-scopes,
+        # https://docs.python.org/3/reference/compound_stmts.html#generic-classes).
+        # In this case, the code name is set to `<generic parameters of MyClass>`,
+        # and we need to skip this frame as it is irrelevant.
+        frame = cast(types.FrameType, frame.f_back)  # guaranteed to not be `None`
+
+    # note, we don't copy frame.f_locals here (or during the last return call), because we don't expect the namespace to be
+    # modified down the line if this becomes a problem, we could implement some sort of frozen mapping structure to enforce this.
+    if force:
+        return frame.f_locals
+
+    # If either of the following conditions are true, the class is defined at the top module level.
+    # To better understand why we need both of these checks, see
+    # https://github.com/pydantic/pydantic/pull/10113#discussion_r1714981531.
+    if frame.f_back is None or frame.f_code.co_name == '<module>':
+        return None
+
+    return frame.f_locals
+
+
+def _type_convert(arg: Any) -> Any:
+    """Convert `None` to `NoneType` and strings to `ForwardRef` instances.
+
+    This is a backport of the private `typing._type_convert` function. When
+    evaluating a type, `ForwardRef._evaluate` ends up being called, and is
+    responsible for making this conversion. However, we still have to apply
+    it for the first argument passed to our type evaluation functions, similarly
+    to the `typing.get_type_hints` function.
+    """
+    if arg is None:
+        return NoneType
+    if isinstance(arg, str):
+        # Like `typing.get_type_hints`, assume the arg can be in any context,
+        # hence the proper `is_argument` and `is_class` args:
+        return _make_forward_ref(arg, is_argument=False, is_class=True)
+    return arg
+
+
+def safe_get_annotations(obj: Any) -> dict[str, Any]:
+    """Get the annotations for the provided object, accounting for potential deferred forward references.
+
+    Starting with Python 3.14, accessing the `__annotations__` attribute might raise a `NameError` if
+    a referenced symbol isn't defined yet. In this case, we return the annotation in the *forward ref*
+    format.
+    """
+    if sys.version_info >= (3, 14):
+        return annotationlib.get_annotations(obj, format=annotationlib.Format.FORWARDREF)
+    else:
+        # TODO just do getattr(obj, '__annotations__', {}) when dropping support for Python 3.9:
+        if isinstance(obj, type):
+            return obj.__dict__.get('__annotations__', {})
+        else:
+            return getattr(obj, '__annotations__', {})
+
+
+def get_model_type_hints(
+    model_class: type[BaseModel],
+    *,
+    ns_resolver: NsResolver | None = None,
+) -> dict[str, tuple[Any, bool]]:
+    """Collect annotations from a Pydantic model class, including those from parent classes.
+
+    Args:
+        model_class: The Pydantic model class to inspect.
+        ns_resolver: A namespace resolver instance to use. Defaults to an empty instance.
+
+    Returns:
+        A dictionary mapping annotation names to a two-tuple: the first element is the evaluated
+        type or the original annotation if a `NameError` occurred, the second element is a boolean
+        indicating if whether the evaluation succeeded.
+    """
+    hints: dict[str, Any] | dict[str, tuple[Any, bool]] = {}
+    ns_resolver = ns_resolver or NsResolver()
+
+    for base in reversed(model_class.__mro__[:-1]):
+        # For Python 3.14, we could also use `Format.VALUE` and pass the globals/locals
+        # from the ns_resolver, but we want to be able to know which specific field failed
+        # to evaluate:
+        ann = safe_get_annotations(base)
+
+        if not ann:
+            continue
+
+        with ns_resolver.push(base):
+            base_model_fields: dict[str, FieldInfo] | None = base.__dict__.get('__pydantic_fields__')
+
+            for name, value in ann.items():
+                if name.startswith('_'):
+                    globalns, localns = ns_resolver.types_namespace
+
+                    # For private attributes, we only need the annotation to detect the `ClassVar` special form.
+                    # For this reason, we still try to evaluate it, but we also catch any possible exception (on
+                    # top of the `NameError`s caught in `try_eval_type`) that could happen so that users are free
+                    # to use any kind of forward annotation for private fields (e.g. circular imports, new typing
+                    # syntax, etc).
+                    try:
+                        hints[name] = try_eval_type(value, globalns, localns)
+                    except Exception:
+                        hints[name] = (value, False)
+                else:
+                    if base_model_fields is not None and name in base_model_fields:
+                        # Avoid unnecessarily evaluating annotations from parent models, as we'll end up
+                        # copying the `FieldInfo` instance from it anyway if we need to.
+                        # We use the `annotation` attribute here, but in reality could put anything here,
+                        # As we are guaranteed to not make use of it:
+                        hints[name] = (base_model_fields[name].annotation, True)
+                    else:
+                        globalns, localns = ns_resolver.types_namespace
+
+                        hints[name] = try_eval_type(value, globalns, localns)
+
+    return hints
+
+
+def get_cls_type_hints(
+    obj: type[Any],
+    *,
+    ns_resolver: NsResolver | None = None,
+) -> dict[str, Any]:
+    """Collect annotations from a class, including those from parent classes.
+
+    Args:
+        obj: The class to inspect.
+        ns_resolver: A namespace resolver instance to use. Defaults to an empty instance.
+    """
+    hints: dict[str, Any] = {}
+    ns_resolver = ns_resolver or NsResolver()
+
+    for base in reversed(obj.__mro__):
+        # For Python 3.14, we could also use `Format.VALUE` and pass the globals/locals
+        # from the ns_resolver, but we want to be able to know which specific field failed
+        # to evaluate:
+        ann = safe_get_annotations(base)
+
+        if not ann:
+            continue
+
+        with ns_resolver.push(base):
+            globalns, localns = ns_resolver.types_namespace
+            for name, value in ann.items():
+                hints[name] = eval_type(value, globalns, localns)
+    return hints
+
+
+def try_eval_type(
+    value: Any,
+    globalns: GlobalsNamespace | None = None,
+    localns: MappingNamespace | None = None,
+) -> tuple[Any, bool]:
+    """Try evaluating the annotation using the provided namespaces.
+
+    Args:
+        value: The value to evaluate. If `None`, it will be replaced by `type[None]`. If an instance
+            of `str`, it will be converted to a `ForwardRef`.
+        localns: The global namespace to use during annotation evaluation.
+        globalns: The local namespace to use during annotation evaluation.
+
+    Returns:
+        A two-tuple containing the possibly evaluated type and a boolean indicating
+            whether the evaluation succeeded or not.
+    """
+    value = _type_convert(value)
+
+    try:
+        return eval_type_backport(value, globalns, localns), True
+    except NameError:
+        return value, False
+
+
+def eval_type(
+    value: Any,
+    globalns: GlobalsNamespace | None = None,
+    localns: MappingNamespace | None = None,
+) -> Any:
+    """Evaluate the annotation using the provided namespaces.
+
+    Args:
+        value: The value to evaluate. If `None`, it will be replaced by `type[None]`. If an instance
+            of `str`, it will be converted to a `ForwardRef`.
+        localns: The global namespace to use during annotation evaluation.
+        globalns: The local namespace to use during annotation evaluation.
+    """
+    value = _type_convert(value)
+    return eval_type_backport(value, globalns, localns)
+
+
+@deprecated(
+    '`eval_type_lenient` is deprecated, use `try_eval_type` instead.',
+    category=None,
+)
+def eval_type_lenient(
+    value: Any,
+    globalns: GlobalsNamespace | None = None,
+    localns: MappingNamespace | None = None,
+) -> Any:
+    ev, _ = try_eval_type(value, globalns, localns)
+    return ev
+
+
+def eval_type_backport(
+    value: Any,
+    globalns: GlobalsNamespace | None = None,
+    localns: MappingNamespace | None = None,
+    type_params: tuple[Any, ...] | None = None,
+) -> Any:
+    """An enhanced version of `typing._eval_type` which will fall back to using the `eval_type_backport`
+    package if it's installed to let older Python versions use newer typing constructs.
+
+    Specifically, this transforms `X | Y` into `typing.Union[X, Y]` and `list[X]` into `typing.List[X]`
+    (as well as all the types made generic in PEP 585) if the original syntax is not supported in the
+    current Python version.
+
+    This function will also display a helpful error if the value passed fails to evaluate.
+    """
+    try:
+        return _eval_type_backport(value, globalns, localns, type_params)
+    except TypeError as e:
+        if 'Unable to evaluate type annotation' in str(e):
+            raise
+
+        # If it is a `TypeError` and value isn't a `ForwardRef`, it would have failed during annotation definition.
+        # Thus we assert here for type checking purposes:
+        assert isinstance(value, typing.ForwardRef)
+
+        message = f'Unable to evaluate type annotation {value.__forward_arg__!r}.'
+        if sys.version_info >= (3, 11):
+            e.add_note(message)
+            raise
+        else:
+            raise TypeError(message) from e
+    except RecursionError as e:
+        # TODO ideally recursion errors should be checked in `eval_type` above, but `eval_type_backport`
+        # is used directly in some places.
+        message = (
+            "If you made use of an implicit recursive type alias (e.g. `MyType = list['MyType']), "
+            'consider using PEP 695 type aliases instead. For more details, refer to the documentation: '
+            f'https://docs.pydantic.dev/{version_short()}/concepts/types/#named-recursive-types'
+        )
+        if sys.version_info >= (3, 11):
+            e.add_note(message)
+            raise
+        else:
+            raise RecursionError(f'{e.args[0]}\n{message}')
+
+
+def _eval_type_backport(
+    value: Any,
+    globalns: GlobalsNamespace | None = None,
+    localns: MappingNamespace | None = None,
+    type_params: tuple[Any, ...] | None = None,
+) -> Any:
+    try:
+        return _eval_type(value, globalns, localns, type_params)
+    except TypeError as e:
+        if not (isinstance(value, typing.ForwardRef) and is_backport_fixable_error(e)):
+            raise
+
+        try:
+            from eval_type_backport import eval_type_backport
+        except ImportError:
+            raise TypeError(
+                f'Unable to evaluate type annotation {value.__forward_arg__!r}. If you are making use '
+                'of the new typing syntax (unions using `|` since Python 3.10 or builtins subscripting '
+                'since Python 3.9), you should either replace the use of new syntax with the existing '
+                '`typing` constructs or install the `eval_type_backport` package.'
+            ) from e
+
+        return eval_type_backport(
+            value,
+            globalns,
+            localns,  # pyright: ignore[reportArgumentType], waiting on a new `eval_type_backport` release.
+            try_default=False,
+        )
+
+
+def _eval_type(
+    value: Any,
+    globalns: GlobalsNamespace | None = None,
+    localns: MappingNamespace | None = None,
+    type_params: tuple[Any, ...] | None = None,
+) -> Any:
+    if sys.version_info >= (3, 14):
+        # Starting in 3.14, `_eval_type()` does *not* apply `_type_convert()`
+        # anymore. This means the `None` -> `type(None)` conversion does not apply:
+        evaluated = typing._eval_type(  # type: ignore
+            value,
+            globalns,
+            localns,
+            type_params=type_params,
+            # This is relevant when evaluating types from `TypedDict` classes, where string annotations
+            # are automatically converted to `ForwardRef` instances with a module set. In this case,
+            # Our `globalns` is irrelevant and we need to indicate `typing._eval_type()` that it should
+            # infer it from the `ForwardRef.__forward_module__` attribute instead (`typing.get_type_hints()`
+            # does the same). Note that this would probably be unnecessary if we properly iterated over the
+            # `__orig_bases__` for TypedDicts in `get_cls_type_hints()`:
+            prefer_fwd_module=True,
+        )
+        if evaluated is None:
+            evaluated = type(None)
+        return evaluated
+    elif sys.version_info >= (3, 13):
+        return typing._eval_type(  # type: ignore
+            value, globalns, localns, type_params=type_params
+        )
+    else:
+        return typing._eval_type(  # type: ignore
+            value, globalns, localns
+        )
+
+
+def is_backport_fixable_error(e: TypeError) -> bool:
+    msg = str(e)
+
+    return sys.version_info < (3, 10) and msg.startswith('unsupported operand type(s) for |: ')
+
+
+def signature_no_eval(f: Callable[..., Any]) -> Signature:
+    """Get the signature of a callable without evaluating any annotations."""
+    if sys.version_info >= (3, 14):
+        from annotationlib import Format
+
+        return signature(f, annotation_format=Format.FORWARDREF)
+    else:
+        return signature(f)
+
+
+def get_function_type_hints(
+    function: Callable[..., Any],
+    *,
+    include_keys: set[str] | None = None,
+    globalns: GlobalsNamespace | None = None,
+    localns: MappingNamespace | None = None,
+) -> dict[str, Any]:
+    """Return type hints for a function.
+
+    This is similar to the `typing.get_type_hints` function, with a few differences:
+    - Support `functools.partial` by using the underlying `func` attribute.
+    - Do not wrap type annotation of a parameter with `Optional` if it has a default value of `None`
+      (related bug: https://github.com/python/cpython/issues/90353, only fixed in 3.11+).
+    """
+    if isinstance(function, partial):
+        annotations = safe_get_annotations(function.func)
+    else:
+        annotations = safe_get_annotations(function)
+
+    if globalns is None:
+        globalns = get_module_ns_of(function)
+    type_params: tuple[Any, ...] | None = None
+    if localns is None:
+        # If localns was specified, it is assumed to already contain type params. This is because
+        # Pydantic has more advanced logic to do so (see `_namespace_utils.ns_for_function`).
+        type_params = getattr(function, '__type_params__', ())
+
+    type_hints = {}
+    for name, value in annotations.items():
+        if include_keys is not None and name not in include_keys:
+            continue
+        if value is None:
+            value = NoneType
+        elif isinstance(value, str):
+            value = _make_forward_ref(value)
+
+        type_hints[name] = eval_type_backport(value, globalns, localns, type_params)
+
+    return type_hints
+
+
+# TODO use typing.ForwardRef directly when we stop supporting 3.9:
+if sys.version_info < (3, 9, 8) or (3, 10) <= sys.version_info < (3, 10, 1):
+
+    def _make_forward_ref(
+        arg: Any,
+        is_argument: bool = True,
+        *,
+        is_class: bool = False,
+    ) -> typing.ForwardRef:
+        """Wrapper for ForwardRef that accounts for the `is_class` argument missing in older versions.
+        The `module` argument is omitted as it breaks <3.9.8, =3.10.0 and isn't used in the calls below.
+
+        See https://github.com/python/cpython/pull/28560 for some background.
+        The backport happened on 3.9.8, see:
+        https://github.com/pydantic/pydantic/discussions/6244#discussioncomment-6275458,
+        and on 3.10.1 for the 3.10 branch, see:
+        https://github.com/pydantic/pydantic/issues/6912
+
+        Implemented as EAFP with memory.
+        """
+        return typing.ForwardRef(arg, is_argument)  # pyright: ignore[reportCallIssue]
+
+else:
+    _make_forward_ref = typing.ForwardRef  # pyright: ignore[reportAssignmentType]
+
+
+if sys.version_info >= (3, 10):
+    get_type_hints = typing.get_type_hints
+
+else:
+    """
+    For older versions of python, we have a custom implementation of `get_type_hints` which is a close as possible to
+    the implementation in CPython 3.10.8.
+    """
+
+    @typing.no_type_check
+    def get_type_hints(  # noqa: C901
+        obj: Any,
+        globalns: dict[str, Any] | None = None,
+        localns: dict[str, Any] | None = None,
+        include_extras: bool = False,
+    ) -> dict[str, Any]:  # pragma: no cover
+        """Taken verbatim from python 3.10.8 unchanged, except:
+        * type annotations of the function definition above.
+        * prefixing `typing.` where appropriate
+        * Use `_make_forward_ref` instead of `typing.ForwardRef` to handle the `is_class` argument.
+
+        https://github.com/python/cpython/blob/aaaf5174241496afca7ce4d4584570190ff972fe/Lib/typing.py#L1773-L1875
+
+        DO NOT CHANGE THIS METHOD UNLESS ABSOLUTELY NECESSARY.
+        ======================================================
+
+        Return type hints for an object.
+
+        This is often the same as obj.__annotations__, but it handles
+        forward references encoded as string literals, adds Optional[t] if a
+        default value equal to None is set and recursively replaces all
+        'Annotated[T, ...]' with 'T' (unless 'include_extras=True').
+
+        The argument may be a module, class, method, or function. The annotations
+        are returned as a dictionary. For classes, annotations include also
+        inherited members.
+
+        TypeError is raised if the argument is not of a type that can contain
+        annotations, and an empty dictionary is returned if no annotations are
+        present.
+
+        BEWARE -- the behavior of globalns and localns is counterintuitive
+        (unless you are familiar with how eval() and exec() work).  The
+        search order is locals first, then globals.
+
+        - If no dict arguments are passed, an attempt is made to use the
+          globals from obj (or the respective module's globals for classes),
+          and these are also used as the locals.  If the object does not appear
+          to have globals, an empty dictionary is used.  For classes, the search
+          order is globals first then locals.
+
+        - If one dict argument is passed, it is used for both globals and
+          locals.
+
+        - If two dict arguments are passed, they specify globals and
+          locals, respectively.
+        """
+        if getattr(obj, '__no_type_check__', None):
+            return {}
+        # Classes require a special treatment.
+        if isinstance(obj, type):
+            hints = {}
+            for base in reversed(obj.__mro__):
+                if globalns is None:
+                    base_globals = getattr(sys.modules.get(base.__module__, None), '__dict__', {})
+                else:
+                    base_globals = globalns
+                ann = base.__dict__.get('__annotations__', {})
+                if isinstance(ann, types.GetSetDescriptorType):
+                    ann = {}
+                base_locals = dict(vars(base)) if localns is None else localns
+                if localns is None and globalns is None:
+                    # This is surprising, but required.  Before Python 3.10,
+                    # get_type_hints only evaluated the globalns of
+                    # a class.  To maintain backwards compatibility, we reverse
+                    # the globalns and localns order so that eval() looks into
+                    # *base_globals* first rather than *base_locals*.
+                    # This only affects ForwardRefs.
+                    base_globals, base_locals = base_locals, base_globals
+                for name, value in ann.items():
+                    if value is None:
+                        value = type(None)
+                    if isinstance(value, str):
+                        value = _make_forward_ref(value, is_argument=False, is_class=True)
+
+                    value = eval_type_backport(value, base_globals, base_locals)
+                    hints[name] = value
+            if not include_extras and hasattr(typing, '_strip_annotations'):
+                return {
+                    k: typing._strip_annotations(t)  # type: ignore
+                    for k, t in hints.items()
+                }
+            else:
+                return hints
+
+        if globalns is None:
+            if isinstance(obj, types.ModuleType):
+                globalns = obj.__dict__
+            else:
+                nsobj = obj
+                # Find globalns for the unwrapped object.
+                while hasattr(nsobj, '__wrapped__'):
+                    nsobj = nsobj.__wrapped__
+                globalns = getattr(nsobj, '__globals__', {})
+            if localns is None:
+                localns = globalns
+        elif localns is None:
+            localns = globalns
+        hints = getattr(obj, '__annotations__', None)
+        if hints is None:
+            # Return empty annotations for something that _could_ have them.
+            if isinstance(obj, typing._allowed_types):  # type: ignore
+                return {}
+            else:
+                raise TypeError(f'{obj!r} is not a module, class, method, or function.')
+        defaults = typing._get_defaults(obj)  # type: ignore
+        hints = dict(hints)
+        for name, value in hints.items():
+            if value is None:
+                value = type(None)
+            if isinstance(value, str):
+                # class-level forward refs were handled above, this must be either
+                # a module-level annotation or a function argument annotation
+
+                value = _make_forward_ref(
+                    value,
+                    is_argument=not isinstance(obj, types.ModuleType),
+                    is_class=False,
+                )
+            value = eval_type_backport(value, globalns, localns)
+            if name in defaults and defaults[name] is None:
+                value = typing.Optional[value]
+            hints[name] = value
+        return hints if include_extras else {k: typing._strip_annotations(t) for k, t in hints.items()}  # type: ignore

.mcp-venv/lib/python3.13/site-packages/pydantic/_internal/_utils.py

@@ -0,0 +1,446 @@
+"""Bucket of reusable internal utilities.
+
+This should be reduced as much as possible with functions only used in one place, moved to that place.
+"""
+
+from __future__ import annotations as _annotations
+
+import dataclasses
+import keyword
+import sys
+import warnings
+import weakref
+from collections import OrderedDict, defaultdict, deque
+from collections.abc import Callable, Iterable, Mapping
+from collections.abc import Set as AbstractSet
+from copy import deepcopy
+from functools import cached_property
+from inspect import Parameter
+from itertools import zip_longest
+from types import BuiltinFunctionType, CodeType, FunctionType, GeneratorType, LambdaType, ModuleType
+from typing import TYPE_CHECKING, Any, Generic, TypeVar, overload
+
+from pydantic_core import MISSING, PydanticUndefined
+from typing_extensions import TypeAlias, TypeGuard, deprecated
+
+from pydantic import PydanticDeprecatedSince211
+
+from . import _repr, _typing_extra
+from ._import_utils import import_cached_base_model
+
+if TYPE_CHECKING:
+    # TODO remove type error comments when we drop support for Python 3.9
+    MappingIntStrAny: TypeAlias = Mapping[int, Any] | Mapping[str, Any]  # pyright: ignore[reportGeneralTypeIssues]
+    AbstractSetIntStr: TypeAlias = AbstractSet[int] | AbstractSet[str]  # pyright: ignore[reportGeneralTypeIssues]
+    from ..main import BaseModel
+
+
+# these are types that are returned unchanged by deepcopy
+IMMUTABLE_NON_COLLECTIONS_TYPES: set[type[Any]] = {
+    int,
+    float,
+    complex,
+    str,
+    bool,
+    bytes,
+    type,
+    _typing_extra.NoneType,
+    FunctionType,
+    BuiltinFunctionType,
+    LambdaType,
+    weakref.ref,
+    CodeType,
+    # note: including ModuleType will differ from behaviour of deepcopy by not producing error.
+    # It might be not a good idea in general, but considering that this function used only internally
+    # against default values of fields, this will allow to actually have a field with module as default value
+    ModuleType,
+    NotImplemented.__class__,
+    Ellipsis.__class__,
+}
+
+# these are types that if empty, might be copied with simple copy() instead of deepcopy()
+BUILTIN_COLLECTIONS: set[type[Any]] = {
+    list,
+    set,
+    tuple,
+    frozenset,
+    dict,
+    OrderedDict,
+    defaultdict,
+    deque,
+}
+
+
+def can_be_positional(param: Parameter) -> bool:
+    """Return whether the parameter accepts a positional argument.
+
+    ```python {test="skip" lint="skip"}
+    def func(a, /, b, *, c):
+        pass
+
+    params = inspect.signature(func).parameters
+    can_be_positional(params['a'])
+    #> True
+    can_be_positional(params['b'])
+    #> True
+    can_be_positional(params['c'])
+    #> False
+    ```
+    """
+    return param.kind in (Parameter.POSITIONAL_ONLY, Parameter.POSITIONAL_OR_KEYWORD)
+
+
+def sequence_like(v: Any) -> bool:
+    return isinstance(v, (list, tuple, set, frozenset, GeneratorType, deque))
+
+
+def lenient_isinstance(o: Any, class_or_tuple: type[Any] | tuple[type[Any], ...] | None) -> bool:  # pragma: no cover
+    try:
+        return isinstance(o, class_or_tuple)  # type: ignore[arg-type]
+    except TypeError:
+        return False
+
+
+def lenient_issubclass(cls: Any, class_or_tuple: Any) -> bool:  # pragma: no cover
+    try:
+        return isinstance(cls, type) and issubclass(cls, class_or_tuple)
+    except TypeError:
+        if isinstance(cls, _typing_extra.WithArgsTypes):
+            return False
+        raise  # pragma: no cover
+
+
+def is_model_class(cls: Any) -> TypeGuard[type[BaseModel]]:
+    """Returns true if cls is a _proper_ subclass of BaseModel, and provides proper type-checking,
+    unlike raw calls to lenient_issubclass.
+    """
+    BaseModel = import_cached_base_model()
+
+    return lenient_issubclass(cls, BaseModel) and cls is not BaseModel
+
+
+def is_valid_identifier(identifier: str) -> bool:
+    """Checks that a string is a valid identifier and not a Python keyword.
+    :param identifier: The identifier to test.
+    :return: True if the identifier is valid.
+    """
+    return identifier.isidentifier() and not keyword.iskeyword(identifier)
+
+
+KeyType = TypeVar('KeyType')
+
+
+def deep_update(mapping: dict[KeyType, Any], *updating_mappings: dict[KeyType, Any]) -> dict[KeyType, Any]:
+    updated_mapping = mapping.copy()
+    for updating_mapping in updating_mappings:
+        for k, v in updating_mapping.items():
+            if k in updated_mapping and isinstance(updated_mapping[k], dict) and isinstance(v, dict):
+                updated_mapping[k] = deep_update(updated_mapping[k], v)
+            else:
+                updated_mapping[k] = v
+    return updated_mapping
+
+
+def update_not_none(mapping: dict[Any, Any], **update: Any) -> None:
+    mapping.update({k: v for k, v in update.items() if v is not None})
+
+
+T = TypeVar('T')
+
+
+def unique_list(
+    input_list: list[T] | tuple[T, ...],
+    *,
+    name_factory: Callable[[T], str] = str,
+) -> list[T]:
+    """Make a list unique while maintaining order.
+    We update the list if another one with the same name is set
+    (e.g. model validator overridden in subclass).
+    """
+    result: list[T] = []
+    result_names: list[str] = []
+    for v in input_list:
+        v_name = name_factory(v)
+        if v_name not in result_names:
+            result_names.append(v_name)
+            result.append(v)
+        else:
+            result[result_names.index(v_name)] = v
+
+    return result
+
+
+class ValueItems(_repr.Representation):
+    """Class for more convenient calculation of excluded or included fields on values."""
+
+    __slots__ = ('_items', '_type')
+
+    def __init__(self, value: Any, items: AbstractSetIntStr | MappingIntStrAny) -> None:
+        items = self._coerce_items(items)
+
+        if isinstance(value, (list, tuple)):
+            items = self._normalize_indexes(items, len(value))  # type: ignore
+
+        self._items: MappingIntStrAny = items  # type: ignore
+
+    def is_excluded(self, item: Any) -> bool:
+        """Check if item is fully excluded.
+
+        :param item: key or index of a value
+        """
+        return self.is_true(self._items.get(item))
+
+    def is_included(self, item: Any) -> bool:
+        """Check if value is contained in self._items.
+
+        :param item: key or index of value
+        """
+        return item in self._items
+
+    def for_element(self, e: int | str) -> AbstractSetIntStr | MappingIntStrAny | None:
+        """:param e: key or index of element on value
+        :return: raw values for element if self._items is dict and contain needed element
+        """
+        item = self._items.get(e)  # type: ignore
+        return item if not self.is_true(item) else None
+
+    def _normalize_indexes(self, items: MappingIntStrAny, v_length: int) -> dict[int | str, Any]:
+        """:param items: dict or set of indexes which will be normalized
+        :param v_length: length of sequence indexes of which will be
+
+        >>> self._normalize_indexes({0: True, -2: True, -1: True}, 4)
+        {0: True, 2: True, 3: True}
+        >>> self._normalize_indexes({'__all__': True}, 4)
+        {0: True, 1: True, 2: True, 3: True}
+        """
+        normalized_items: dict[int | str, Any] = {}
+        all_items = None
+        for i, v in items.items():
+            if not (isinstance(v, Mapping) or isinstance(v, AbstractSet) or self.is_true(v)):
+                raise TypeError(f'Unexpected type of exclude value for index "{i}" {v.__class__}')
+            if i == '__all__':
+                all_items = self._coerce_value(v)
+                continue
+            if not isinstance(i, int):
+                raise TypeError(
+                    'Excluding fields from a sequence of sub-models or dicts must be performed index-wise: '
+                    'expected integer keys or keyword "__all__"'
+                )
+            normalized_i = v_length + i if i < 0 else i
+            normalized_items[normalized_i] = self.merge(v, normalized_items.get(normalized_i))
+
+        if not all_items:
+            return normalized_items
+        if self.is_true(all_items):
+            for i in range(v_length):
+                normalized_items.setdefault(i, ...)
+            return normalized_items
+        for i in range(v_length):
+            normalized_item = normalized_items.setdefault(i, {})
+            if not self.is_true(normalized_item):
+                normalized_items[i] = self.merge(all_items, normalized_item)
+        return normalized_items
+
+    @classmethod
+    def merge(cls, base: Any, override: Any, intersect: bool = False) -> Any:
+        """Merge a `base` item with an `override` item.
+
+        Both `base` and `override` are converted to dictionaries if possible.
+        Sets are converted to dictionaries with the sets entries as keys and
+        Ellipsis as values.
+
+        Each key-value pair existing in `base` is merged with `override`,
+        while the rest of the key-value pairs are updated recursively with this function.
+
+        Merging takes place based on the "union" of keys if `intersect` is
+        set to `False` (default) and on the intersection of keys if
+        `intersect` is set to `True`.
+        """
+        override = cls._coerce_value(override)
+        base = cls._coerce_value(base)
+        if override is None:
+            return base
+        if cls.is_true(base) or base is None:
+            return override
+        if cls.is_true(override):
+            return base if intersect else override
+
+        # intersection or union of keys while preserving ordering:
+        if intersect:
+            merge_keys = [k for k in base if k in override] + [k for k in override if k in base]
+        else:
+            merge_keys = list(base) + [k for k in override if k not in base]
+
+        merged: dict[int | str, Any] = {}
+        for k in merge_keys:
+            merged_item = cls.merge(base.get(k), override.get(k), intersect=intersect)
+            if merged_item is not None:
+                merged[k] = merged_item
+
+        return merged
+
+    @staticmethod
+    def _coerce_items(items: AbstractSetIntStr | MappingIntStrAny) -> MappingIntStrAny:
+        if isinstance(items, Mapping):
+            pass
+        elif isinstance(items, AbstractSet):
+            items = dict.fromkeys(items, ...)  # type: ignore
+        else:
+            class_name = getattr(items, '__class__', '???')
+            raise TypeError(f'Unexpected type of exclude value {class_name}')
+        return items  # type: ignore
+
+    @classmethod
+    def _coerce_value(cls, value: Any) -> Any:
+        if value is None or cls.is_true(value):
+            return value
+        return cls._coerce_items(value)
+
+    @staticmethod
+    def is_true(v: Any) -> bool:
+        return v is True or v is ...
+
+    def __repr_args__(self) -> _repr.ReprArgs:
+        return [(None, self._items)]
+
+
+if TYPE_CHECKING:
+
+    def LazyClassAttribute(name: str, get_value: Callable[[], T]) -> T: ...
+
+else:
+
+    class LazyClassAttribute:
+        """A descriptor exposing an attribute only accessible on a class (hidden from instances).
+
+        The attribute is lazily computed and cached during the first access.
+        """
+
+        def __init__(self, name: str, get_value: Callable[[], Any]) -> None:
+            self.name = name
+            self.get_value = get_value
+
+        @cached_property
+        def value(self) -> Any:
+            return self.get_value()
+
+        def __get__(self, instance: Any, owner: type[Any]) -> None:
+            if instance is None:
+                return self.value
+            raise AttributeError(f'{self.name!r} attribute of {owner.__name__!r} is class-only')
+
+
+Obj = TypeVar('Obj')
+
+
+def smart_deepcopy(obj: Obj) -> Obj:
+    """Return type as is for immutable built-in types
+    Use obj.copy() for built-in empty collections
+    Use copy.deepcopy() for non-empty collections and unknown objects.
+    """
+    if obj is MISSING or obj is PydanticUndefined:
+        return obj  # pyright: ignore[reportReturnType]
+    obj_type = obj.__class__
+    if obj_type in IMMUTABLE_NON_COLLECTIONS_TYPES:
+        return obj  # fastest case: obj is immutable and not collection therefore will not be copied anyway
+    try:
+        if not obj and obj_type in BUILTIN_COLLECTIONS:
+            # faster way for empty collections, no need to copy its members
+            return obj if obj_type is tuple else obj.copy()  # tuple doesn't have copy method  # type: ignore
+    except (TypeError, ValueError, RuntimeError):
+        # do we really dare to catch ALL errors? Seems a bit risky
+        pass
+
+    return deepcopy(obj)  # slowest way when we actually might need a deepcopy
+
+
+_SENTINEL = object()
+
+
+def all_identical(left: Iterable[Any], right: Iterable[Any]) -> bool:
+    """Check that the items of `left` are the same objects as those in `right`.
+
+    >>> a, b = object(), object()
+    >>> all_identical([a, b, a], [a, b, a])
+    True
+    >>> all_identical([a, b, [a]], [a, b, [a]])  # new list object, while "equal" is not "identical"
+    False
+    """
+    for left_item, right_item in zip_longest(left, right, fillvalue=_SENTINEL):
+        if left_item is not right_item:
+            return False
+    return True
+
+
+def get_first_not_none(a: Any, b: Any) -> Any:
+    """Return the first argument if it is not `None`, otherwise return the second argument."""
+    return a if a is not None else b
+
+
+@dataclasses.dataclass(frozen=True)
+class SafeGetItemProxy:
+    """Wrapper redirecting `__getitem__` to `get` with a sentinel value as default
+
+    This makes is safe to use in `operator.itemgetter` when some keys may be missing
+    """
+
+    # Define __slots__manually for performances
+    # @dataclasses.dataclass() only support slots=True in python>=3.10
+    __slots__ = ('wrapped',)
+
+    wrapped: Mapping[str, Any]
+
+    def __getitem__(self, key: str, /) -> Any:
+        return self.wrapped.get(key, _SENTINEL)
+
+    # required to pass the object to operator.itemgetter() instances due to a quirk of typeshed
+    # https://github.com/python/mypy/issues/13713
+    # https://github.com/python/typeshed/pull/8785
+    # Since this is typing-only, hide it in a typing.TYPE_CHECKING block
+    if TYPE_CHECKING:
+
+        def __contains__(self, key: str, /) -> bool:
+            return self.wrapped.__contains__(key)
+
+
+_ModelT = TypeVar('_ModelT', bound='BaseModel')
+_RT = TypeVar('_RT')
+
+
+class deprecated_instance_property(Generic[_ModelT, _RT]):
+    """A decorator exposing the decorated class method as a property, with a warning on instance access.
+
+    This decorator takes a class method defined on the `BaseModel` class and transforms it into
+    an attribute. The attribute can be accessed on both the class and instances of the class. If accessed
+    via an instance, a deprecation warning is emitted stating that instance access will be removed in V3.
+    """
+
+    def __init__(self, fget: Callable[[type[_ModelT]], _RT], /) -> None:
+        # Note: fget should be a classmethod:
+        self.fget = fget
+
+    @overload
+    def __get__(self, instance: None, objtype: type[_ModelT]) -> _RT: ...
+    @overload
+    @deprecated(
+        'Accessing this attribute on the instance is deprecated, and will be removed in Pydantic V3. '
+        'Instead, you should access this attribute from the model class.',
+        category=None,
+    )
+    def __get__(self, instance: _ModelT, objtype: type[_ModelT]) -> _RT: ...
+    def __get__(self, instance: _ModelT | None, objtype: type[_ModelT]) -> _RT:
+        if instance is not None:
+            # fmt: off
+            attr_name = (
+                self.fget.__name__
+                if sys.version_info >= (3, 10)
+                else self.fget.__func__.__name__  # pyright: ignore[reportFunctionMemberAccess]
+            )
+            # fmt: on
+            warnings.warn(
+                f'Accessing the {attr_name!r} attribute on the instance is deprecated. '
+                'Instead, you should access this attribute from the model class.',
+                category=PydanticDeprecatedSince211,
+                stacklevel=2,
+            )
+        return self.fget.__get__(instance, objtype)()

.mcp-venv/lib/python3.13/site-packages/pydantic/_internal/_validate_call.py

@@ -0,0 +1,141 @@
+from __future__ import annotations as _annotations
+
+import functools
+import inspect
+from collections.abc import Awaitable
+from functools import partial
+from typing import Any, Callable
+
+import pydantic_core
+
+from ..config import ConfigDict
+from ..plugin._schema_validator import create_schema_validator
+from ._config import ConfigWrapper
+from ._generate_schema import GenerateSchema, ValidateCallSupportedTypes
+from ._namespace_utils import MappingNamespace, NsResolver, ns_for_function
+from ._typing_extra import signature_no_eval
+
+
+def extract_function_name(func: ValidateCallSupportedTypes) -> str:
+    """Extract the name of a `ValidateCallSupportedTypes` object."""
+    return f'partial({func.func.__name__})' if isinstance(func, functools.partial) else func.__name__
+
+
+def extract_function_qualname(func: ValidateCallSupportedTypes) -> str:
+    """Extract the qualname of a `ValidateCallSupportedTypes` object."""
+    return f'partial({func.func.__qualname__})' if isinstance(func, functools.partial) else func.__qualname__
+
+
+def update_wrapper_attributes(wrapped: ValidateCallSupportedTypes, wrapper: Callable[..., Any]):
+    """Update the `wrapper` function with the attributes of the `wrapped` function. Return the updated function."""
+    if inspect.iscoroutinefunction(wrapped):
+
+        @functools.wraps(wrapped)
+        async def wrapper_function(*args, **kwargs):  # type: ignore
+            return await wrapper(*args, **kwargs)
+    else:
+
+        @functools.wraps(wrapped)
+        def wrapper_function(*args, **kwargs):
+            return wrapper(*args, **kwargs)
+
+    # We need to manually update this because `partial` object has no `__name__` and `__qualname__`.
+    wrapper_function.__name__ = extract_function_name(wrapped)
+    wrapper_function.__qualname__ = extract_function_qualname(wrapped)
+    wrapper_function.raw_function = wrapped  # type: ignore
+
+    return wrapper_function
+
+
+class ValidateCallWrapper:
+    """This is a wrapper around a function that validates the arguments passed to it, and optionally the return value."""
+
+    __slots__ = (
+        'function',
+        'validate_return',
+        'schema_type',
+        'module',
+        'qualname',
+        'ns_resolver',
+        'config_wrapper',
+        '__pydantic_complete__',
+        '__pydantic_validator__',
+        '__return_pydantic_validator__',
+    )
+
+    def __init__(
+        self,
+        function: ValidateCallSupportedTypes,
+        config: ConfigDict | None,
+        validate_return: bool,
+        parent_namespace: MappingNamespace | None,
+    ) -> None:
+        self.function = function
+        self.validate_return = validate_return
+        if isinstance(function, partial):
+            self.schema_type = function.func
+            self.module = function.func.__module__
+        else:
+            self.schema_type = function
+            self.module = function.__module__
+        self.qualname = extract_function_qualname(function)
+
+        self.ns_resolver = NsResolver(
+            namespaces_tuple=ns_for_function(self.schema_type, parent_namespace=parent_namespace)
+        )
+        self.config_wrapper = ConfigWrapper(config)
+        if not self.config_wrapper.defer_build:
+            self._create_validators()
+        else:
+            self.__pydantic_complete__ = False
+
+    def _create_validators(self) -> None:
+        gen_schema = GenerateSchema(self.config_wrapper, self.ns_resolver)
+        schema = gen_schema.clean_schema(gen_schema.generate_schema(self.function))
+        core_config = self.config_wrapper.core_config(title=self.qualname)
+
+        self.__pydantic_validator__ = create_schema_validator(
+            schema,
+            self.schema_type,
+            self.module,
+            self.qualname,
+            'validate_call',
+            core_config,
+            self.config_wrapper.plugin_settings,
+        )
+        if self.validate_return:
+            signature = signature_no_eval(self.function)
+            return_type = signature.return_annotation if signature.return_annotation is not signature.empty else Any
+            gen_schema = GenerateSchema(self.config_wrapper, self.ns_resolver)
+            schema = gen_schema.clean_schema(gen_schema.generate_schema(return_type))
+            validator = create_schema_validator(
+                schema,
+                self.schema_type,
+                self.module,
+                self.qualname,
+                'validate_call',
+                core_config,
+                self.config_wrapper.plugin_settings,
+            )
+            if inspect.iscoroutinefunction(self.function):
+
+                async def return_val_wrapper(aw: Awaitable[Any]) -> None:
+                    return validator.validate_python(await aw)
+
+                self.__return_pydantic_validator__ = return_val_wrapper
+            else:
+                self.__return_pydantic_validator__ = validator.validate_python
+        else:
+            self.__return_pydantic_validator__ = None
+
+        self.__pydantic_complete__ = True
+
+    def __call__(self, *args: Any, **kwargs: Any) -> Any:
+        if not self.__pydantic_complete__:
+            self._create_validators()
+
+        res = self.__pydantic_validator__.validate_python(pydantic_core.ArgsKwargs(args, kwargs))
+        if self.__return_pydantic_validator__:
+            return self.__return_pydantic_validator__(res)
+        else:
+            return res

.mcp-venv/lib/python3.13/site-packages/pydantic/_internal/_validators.py

@@ -0,0 +1,534 @@
+"""Validator functions for standard library types.
+
+Import of this module is deferred since it contains imports of many standard library modules.
+"""
+
+from __future__ import annotations as _annotations
+
+import collections.abc
+import math
+import re
+import typing
+from collections.abc import Sequence
+from decimal import Decimal
+from fractions import Fraction
+from ipaddress import IPv4Address, IPv4Interface, IPv4Network, IPv6Address, IPv6Interface, IPv6Network
+from typing import Any, Callable, TypeVar, Union, cast
+from zoneinfo import ZoneInfo, ZoneInfoNotFoundError
+
+import typing_extensions
+from pydantic_core import PydanticCustomError, PydanticKnownError, core_schema
+from typing_extensions import get_args, get_origin
+from typing_inspection import typing_objects
+
+from pydantic._internal._import_utils import import_cached_field_info
+from pydantic.errors import PydanticSchemaGenerationError
+
+
+def sequence_validator(
+    input_value: Sequence[Any],
+    /,
+    validator: core_schema.ValidatorFunctionWrapHandler,
+) -> Sequence[Any]:
+    """Validator for `Sequence` types, isinstance(v, Sequence) has already been called."""
+    value_type = type(input_value)
+
+    # We don't accept any plain string as a sequence
+    # Relevant issue: https://github.com/pydantic/pydantic/issues/5595
+    if issubclass(value_type, (str, bytes)):
+        raise PydanticCustomError(
+            'sequence_str',
+            "'{type_name}' instances are not allowed as a Sequence value",
+            {'type_name': value_type.__name__},
+        )
+
+    # TODO: refactor sequence validation to validate with either a list or a tuple
+    # schema, depending on the type of the value.
+    # Additionally, we should be able to remove one of either this validator or the
+    # SequenceValidator in _std_types_schema.py (preferably this one, while porting over some logic).
+    # Effectively, a refactor for sequence validation is needed.
+    if value_type is tuple:
+        input_value = list(input_value)
+
+    v_list = validator(input_value)
+
+    # the rest of the logic is just re-creating the original type from `v_list`
+    if value_type is list:
+        return v_list
+    elif issubclass(value_type, range):
+        # return the list as we probably can't re-create the range
+        return v_list
+    elif value_type is tuple:
+        return tuple(v_list)
+    else:
+        # best guess at how to re-create the original type, more custom construction logic might be required
+        return value_type(v_list)  # type: ignore[call-arg]
+
+
+def import_string(value: Any) -> Any:
+    if isinstance(value, str):
+        try:
+            return _import_string_logic(value)
+        except ImportError as e:
+            raise PydanticCustomError('import_error', 'Invalid python path: {error}', {'error': str(e)}) from e
+    else:
+        # otherwise we just return the value and let the next validator do the rest of the work
+        return value
+
+
+def _import_string_logic(dotted_path: str) -> Any:
+    """Inspired by uvicorn — dotted paths should include a colon before the final item if that item is not a module.
+    (This is necessary to distinguish between a submodule and an attribute when there is a conflict.).
+
+    If the dotted path does not include a colon and the final item is not a valid module, importing as an attribute
+    rather than a submodule will be attempted automatically.
+
+    So, for example, the following values of `dotted_path` result in the following returned values:
+    * 'collections': <module 'collections'>
+    * 'collections.abc': <module 'collections.abc'>
+    * 'collections.abc:Mapping': <class 'collections.abc.Mapping'>
+    * `collections.abc.Mapping`: <class 'collections.abc.Mapping'> (though this is a bit slower than the previous line)
+
+    An error will be raised under any of the following scenarios:
+    * `dotted_path` contains more than one colon (e.g., 'collections:abc:Mapping')
+    * the substring of `dotted_path` before the colon is not a valid module in the environment (e.g., '123:Mapping')
+    * the substring of `dotted_path` after the colon is not an attribute of the module (e.g., 'collections:abc123')
+    """
+    from importlib import import_module
+
+    components = dotted_path.strip().split(':')
+    if len(components) > 2:
+        raise ImportError(f"Import strings should have at most one ':'; received {dotted_path!r}")
+    attribute = None
+    if len(components) == 2:
+        attribute = components[1]
+    module_path = components[0]
+    if not module_path:
+        raise ImportError(f'Import strings should have a nonempty module name; received {dotted_path!r}')
+
+    try:
+        module = import_module(module_path)
+    except ModuleNotFoundError:
+        if attribute is None and '.' in module_path:
+            # Try interpreting the final dotted segment as an attribute, not a submodule
+            maybe_module_path, maybe_attribute = module_path.rsplit('.', 1)
+
+            try:
+                return _import_string_logic(f'{maybe_module_path}:{maybe_attribute}')
+            except ImportError:
+                pass
+        raise
+
+    if attribute is not None:
+        try:
+            return getattr(module, attribute)
+        except AttributeError as e:
+            raise ImportError(f'cannot import name {attribute!r} from {module_path!r}') from e
+    else:
+        return module
+
+
+def pattern_either_validator(input_value: Any, /) -> re.Pattern[Any]:
+    if isinstance(input_value, re.Pattern):
+        return input_value
+    elif isinstance(input_value, (str, bytes)):
+        # todo strict mode
+        return compile_pattern(input_value)  # type: ignore
+    else:
+        raise PydanticCustomError('pattern_type', 'Input should be a valid pattern')
+
+
+def pattern_str_validator(input_value: Any, /) -> re.Pattern[str]:
+    if isinstance(input_value, re.Pattern):
+        if isinstance(input_value.pattern, str):
+            return input_value
+        else:
+            raise PydanticCustomError('pattern_str_type', 'Input should be a string pattern')
+    elif isinstance(input_value, str):
+        return compile_pattern(input_value)
+    elif isinstance(input_value, bytes):
+        raise PydanticCustomError('pattern_str_type', 'Input should be a string pattern')
+    else:
+        raise PydanticCustomError('pattern_type', 'Input should be a valid pattern')
+
+
+def pattern_bytes_validator(input_value: Any, /) -> re.Pattern[bytes]:
+    if isinstance(input_value, re.Pattern):
+        if isinstance(input_value.pattern, bytes):
+            return input_value
+        else:
+            raise PydanticCustomError('pattern_bytes_type', 'Input should be a bytes pattern')
+    elif isinstance(input_value, bytes):
+        return compile_pattern(input_value)
+    elif isinstance(input_value, str):
+        raise PydanticCustomError('pattern_bytes_type', 'Input should be a bytes pattern')
+    else:
+        raise PydanticCustomError('pattern_type', 'Input should be a valid pattern')
+
+
+PatternType = TypeVar('PatternType', str, bytes)
+
+
+def compile_pattern(pattern: PatternType) -> re.Pattern[PatternType]:
+    try:
+        return re.compile(pattern)
+    except re.error:
+        raise PydanticCustomError('pattern_regex', 'Input should be a valid regular expression')
+
+
+def ip_v4_address_validator(input_value: Any, /) -> IPv4Address:
+    if isinstance(input_value, IPv4Address):
+        return input_value
+
+    try:
+        return IPv4Address(input_value)
+    except ValueError:
+        raise PydanticCustomError('ip_v4_address', 'Input is not a valid IPv4 address')
+
+
+def ip_v6_address_validator(input_value: Any, /) -> IPv6Address:
+    if isinstance(input_value, IPv6Address):
+        return input_value
+
+    try:
+        return IPv6Address(input_value)
+    except ValueError:
+        raise PydanticCustomError('ip_v6_address', 'Input is not a valid IPv6 address')
+
+
+def ip_v4_network_validator(input_value: Any, /) -> IPv4Network:
+    """Assume IPv4Network initialised with a default `strict` argument.
+
+    See more:
+    https://docs.python.org/library/ipaddress.html#ipaddress.IPv4Network
+    """
+    if isinstance(input_value, IPv4Network):
+        return input_value
+
+    try:
+        return IPv4Network(input_value)
+    except ValueError:
+        raise PydanticCustomError('ip_v4_network', 'Input is not a valid IPv4 network')
+
+
+def ip_v6_network_validator(input_value: Any, /) -> IPv6Network:
+    """Assume IPv6Network initialised with a default `strict` argument.
+
+    See more:
+    https://docs.python.org/library/ipaddress.html#ipaddress.IPv6Network
+    """
+    if isinstance(input_value, IPv6Network):
+        return input_value
+
+    try:
+        return IPv6Network(input_value)
+    except ValueError:
+        raise PydanticCustomError('ip_v6_network', 'Input is not a valid IPv6 network')
+
+
+def ip_v4_interface_validator(input_value: Any, /) -> IPv4Interface:
+    if isinstance(input_value, IPv4Interface):
+        return input_value
+
+    try:
+        return IPv4Interface(input_value)
+    except ValueError:
+        raise PydanticCustomError('ip_v4_interface', 'Input is not a valid IPv4 interface')
+
+
+def ip_v6_interface_validator(input_value: Any, /) -> IPv6Interface:
+    if isinstance(input_value, IPv6Interface):
+        return input_value
+
+    try:
+        return IPv6Interface(input_value)
+    except ValueError:
+        raise PydanticCustomError('ip_v6_interface', 'Input is not a valid IPv6 interface')
+
+
+def fraction_validator(input_value: Any, /) -> Fraction:
+    if isinstance(input_value, Fraction):
+        return input_value
+
+    try:
+        return Fraction(input_value)
+    except ValueError:
+        raise PydanticCustomError('fraction_parsing', 'Input is not a valid fraction')
+
+
+def forbid_inf_nan_check(x: Any) -> Any:
+    if not math.isfinite(x):
+        raise PydanticKnownError('finite_number')
+    return x
+
+
+def _safe_repr(v: Any) -> int | float | str:
+    """The context argument for `PydanticKnownError` requires a number or str type, so we do a simple repr() coercion for types like timedelta.
+
+    See tests/test_types.py::test_annotated_metadata_any_order for some context.
+    """
+    if isinstance(v, (int, float, str)):
+        return v
+    return repr(v)
+
+
+def greater_than_validator(x: Any, gt: Any) -> Any:
+    try:
+        if not (x > gt):
+            raise PydanticKnownError('greater_than', {'gt': _safe_repr(gt)})
+        return x
+    except TypeError:
+        raise TypeError(f"Unable to apply constraint 'gt' to supplied value {x}")
+
+
+def greater_than_or_equal_validator(x: Any, ge: Any) -> Any:
+    try:
+        if not (x >= ge):
+            raise PydanticKnownError('greater_than_equal', {'ge': _safe_repr(ge)})
+        return x
+    except TypeError:
+        raise TypeError(f"Unable to apply constraint 'ge' to supplied value {x}")
+
+
+def less_than_validator(x: Any, lt: Any) -> Any:
+    try:
+        if not (x < lt):
+            raise PydanticKnownError('less_than', {'lt': _safe_repr(lt)})
+        return x
+    except TypeError:
+        raise TypeError(f"Unable to apply constraint 'lt' to supplied value {x}")
+
+
+def less_than_or_equal_validator(x: Any, le: Any) -> Any:
+    try:
+        if not (x <= le):
+            raise PydanticKnownError('less_than_equal', {'le': _safe_repr(le)})
+        return x
+    except TypeError:
+        raise TypeError(f"Unable to apply constraint 'le' to supplied value {x}")
+
+
+def multiple_of_validator(x: Any, multiple_of: Any) -> Any:
+    try:
+        if x % multiple_of:
+            raise PydanticKnownError('multiple_of', {'multiple_of': _safe_repr(multiple_of)})
+        return x
+    except TypeError:
+        raise TypeError(f"Unable to apply constraint 'multiple_of' to supplied value {x}")
+
+
+def min_length_validator(x: Any, min_length: Any) -> Any:
+    try:
+        if not (len(x) >= min_length):
+            raise PydanticKnownError(
+                'too_short', {'field_type': 'Value', 'min_length': min_length, 'actual_length': len(x)}
+            )
+        return x
+    except TypeError:
+        raise TypeError(f"Unable to apply constraint 'min_length' to supplied value {x}")
+
+
+def max_length_validator(x: Any, max_length: Any) -> Any:
+    try:
+        if len(x) > max_length:
+            raise PydanticKnownError(
+                'too_long',
+                {'field_type': 'Value', 'max_length': max_length, 'actual_length': len(x)},
+            )
+        return x
+    except TypeError:
+        raise TypeError(f"Unable to apply constraint 'max_length' to supplied value {x}")
+
+
+def _extract_decimal_digits_info(decimal: Decimal) -> tuple[int, int]:
+    """Compute the total number of digits and decimal places for a given [`Decimal`][decimal.Decimal] instance.
+
+    This function handles both normalized and non-normalized Decimal instances.
+    Example: Decimal('1.230') -> 4 digits, 3 decimal places
+
+    Args:
+        decimal (Decimal): The decimal number to analyze.
+
+    Returns:
+        tuple[int, int]: A tuple containing the number of decimal places and total digits.
+
+    Though this could be divided into two separate functions, the logic is easier to follow if we couple the computation
+    of the number of decimals and digits together.
+    """
+    try:
+        decimal_tuple = decimal.as_tuple()
+
+        assert isinstance(decimal_tuple.exponent, int)
+
+        exponent = decimal_tuple.exponent
+        num_digits = len(decimal_tuple.digits)
+
+        if exponent >= 0:
+            # A positive exponent adds that many trailing zeros
+            # Ex: digit_tuple=(1, 2, 3), exponent=2 -> 12300 -> 0 decimal places, 5 digits
+            num_digits += exponent
+            decimal_places = 0
+        else:
+            # If the absolute value of the negative exponent is larger than the
+            # number of digits, then it's the same as the number of digits,
+            # because it'll consume all the digits in digit_tuple and then
+            # add abs(exponent) - len(digit_tuple) leading zeros after the decimal point.
+            # Ex: digit_tuple=(1, 2, 3), exponent=-2 -> 1.23 -> 2 decimal places, 3 digits
+            # Ex: digit_tuple=(1, 2, 3), exponent=-4 -> 0.0123 -> 4 decimal places, 4 digits
+            decimal_places = abs(exponent)
+            num_digits = max(num_digits, decimal_places)
+
+        return decimal_places, num_digits
+    except (AssertionError, AttributeError):
+        raise TypeError(f'Unable to extract decimal digits info from supplied value {decimal}')
+
+
+def max_digits_validator(x: Any, max_digits: Any) -> Any:
+    try:
+        _, num_digits = _extract_decimal_digits_info(x)
+        _, normalized_num_digits = _extract_decimal_digits_info(x.normalize())
+        if (num_digits > max_digits) and (normalized_num_digits > max_digits):
+            raise PydanticKnownError(
+                'decimal_max_digits',
+                {'max_digits': max_digits},
+            )
+        return x
+    except TypeError:
+        raise TypeError(f"Unable to apply constraint 'max_digits' to supplied value {x}")
+
+
+def decimal_places_validator(x: Any, decimal_places: Any) -> Any:
+    try:
+        decimal_places_, _ = _extract_decimal_digits_info(x)
+        if decimal_places_ > decimal_places:
+            normalized_decimal_places, _ = _extract_decimal_digits_info(x.normalize())
+            if normalized_decimal_places > decimal_places:
+                raise PydanticKnownError(
+                    'decimal_max_places',
+                    {'decimal_places': decimal_places},
+                )
+        return x
+    except TypeError:
+        raise TypeError(f"Unable to apply constraint 'decimal_places' to supplied value {x}")
+
+
+def deque_validator(input_value: Any, handler: core_schema.ValidatorFunctionWrapHandler) -> collections.deque[Any]:
+    return collections.deque(handler(input_value), maxlen=getattr(input_value, 'maxlen', None))
+
+
+def defaultdict_validator(
+    input_value: Any, handler: core_schema.ValidatorFunctionWrapHandler, default_default_factory: Callable[[], Any]
+) -> collections.defaultdict[Any, Any]:
+    if isinstance(input_value, collections.defaultdict):
+        default_factory = input_value.default_factory
+        return collections.defaultdict(default_factory, handler(input_value))
+    else:
+        return collections.defaultdict(default_default_factory, handler(input_value))
+
+
+def get_defaultdict_default_default_factory(values_source_type: Any) -> Callable[[], Any]:
+    FieldInfo = import_cached_field_info()
+
+    values_type_origin = get_origin(values_source_type)
+
+    def infer_default() -> Callable[[], Any]:
+        allowed_default_types: dict[Any, Any] = {
+            tuple: tuple,
+            collections.abc.Sequence: tuple,
+            collections.abc.MutableSequence: list,
+            list: list,
+            typing.Sequence: list,
+            set: set,
+            typing.MutableSet: set,
+            collections.abc.MutableSet: set,
+            collections.abc.Set: frozenset,
+            typing.MutableMapping: dict,
+            typing.Mapping: dict,
+            collections.abc.Mapping: dict,
+            collections.abc.MutableMapping: dict,
+            float: float,
+            int: int,
+            str: str,
+            bool: bool,
+        }
+        values_type = values_type_origin or values_source_type
+        instructions = 'set using `DefaultDict[..., Annotated[..., Field(default_factory=...)]]`'
+        if typing_objects.is_typevar(values_type):
+
+            def type_var_default_factory() -> None:
+                raise RuntimeError(
+                    'Generic defaultdict cannot be used without a concrete value type or an'
+                    ' explicit default factory, ' + instructions
+                )
+
+            return type_var_default_factory
+        elif values_type not in allowed_default_types:
+            # a somewhat subjective set of types that have reasonable default values
+            allowed_msg = ', '.join([t.__name__ for t in set(allowed_default_types.values())])
+            raise PydanticSchemaGenerationError(
+                f'Unable to infer a default factory for keys of type {values_source_type}.'
+                f' Only {allowed_msg} are supported, other types require an explicit default factory'
+                ' ' + instructions
+            )
+        return allowed_default_types[values_type]
+
+    # Assume Annotated[..., Field(...)]
+    if typing_objects.is_annotated(values_type_origin):
+        field_info = next((v for v in get_args(values_source_type) if isinstance(v, FieldInfo)), None)
+    else:
+        field_info = None
+    if field_info and field_info.default_factory:
+        # Assume the default factory does not take any argument:
+        default_default_factory = cast(Callable[[], Any], field_info.default_factory)
+    else:
+        default_default_factory = infer_default()
+    return default_default_factory
+
+
+def validate_str_is_valid_iana_tz(value: Any, /) -> ZoneInfo:
+    if isinstance(value, ZoneInfo):
+        return value
+    try:
+        return ZoneInfo(value)
+    except (ZoneInfoNotFoundError, ValueError, TypeError):
+        raise PydanticCustomError('zoneinfo_str', 'invalid timezone: {value}', {'value': value})
+
+
+NUMERIC_VALIDATOR_LOOKUP: dict[str, Callable] = {
+    'gt': greater_than_validator,
+    'ge': greater_than_or_equal_validator,
+    'lt': less_than_validator,
+    'le': less_than_or_equal_validator,
+    'multiple_of': multiple_of_validator,
+    'min_length': min_length_validator,
+    'max_length': max_length_validator,
+    'max_digits': max_digits_validator,
+    'decimal_places': decimal_places_validator,
+}
+
+IpType = Union[IPv4Address, IPv6Address, IPv4Network, IPv6Network, IPv4Interface, IPv6Interface]
+
+IP_VALIDATOR_LOOKUP: dict[type[IpType], Callable] = {
+    IPv4Address: ip_v4_address_validator,
+    IPv6Address: ip_v6_address_validator,
+    IPv4Network: ip_v4_network_validator,
+    IPv6Network: ip_v6_network_validator,
+    IPv4Interface: ip_v4_interface_validator,
+    IPv6Interface: ip_v6_interface_validator,
+}
+
+MAPPING_ORIGIN_MAP: dict[Any, Any] = {
+    typing.DefaultDict: collections.defaultdict,  # noqa: UP006
+    collections.defaultdict: collections.defaultdict,
+    typing.OrderedDict: collections.OrderedDict,  # noqa: UP006
+    collections.OrderedDict: collections.OrderedDict,
+    typing_extensions.OrderedDict: collections.OrderedDict,
+    typing.Counter: collections.Counter,
+    collections.Counter: collections.Counter,
+    # this doesn't handle subclasses of these
+    typing.Mapping: dict,
+    typing.MutableMapping: dict,
+    # parametrized typing.{Mutable}Mapping creates one of these
+    collections.abc.Mapping: dict,
+    collections.abc.MutableMapping: dict,
+}

.mcp-venv/lib/python3.13/site-packages/pydantic/_migration.py

@@ -0,0 +1,316 @@
+import sys
+from typing import Any, Callable
+
+from pydantic.warnings import PydanticDeprecatedSince20
+
+from .version import version_short
+
+MOVED_IN_V2 = {
+    'pydantic.utils:version_info': 'pydantic.version:version_info',
+    'pydantic.error_wrappers:ValidationError': 'pydantic:ValidationError',
+    'pydantic.utils:to_camel': 'pydantic.alias_generators:to_pascal',
+    'pydantic.utils:to_lower_camel': 'pydantic.alias_generators:to_camel',
+    'pydantic:PyObject': 'pydantic.types:ImportString',
+    'pydantic.types:PyObject': 'pydantic.types:ImportString',
+    'pydantic.generics:GenericModel': 'pydantic.BaseModel',
+}
+
+DEPRECATED_MOVED_IN_V2 = {
+    'pydantic.tools:schema_of': 'pydantic.deprecated.tools:schema_of',
+    'pydantic.tools:parse_obj_as': 'pydantic.deprecated.tools:parse_obj_as',
+    'pydantic.tools:schema_json_of': 'pydantic.deprecated.tools:schema_json_of',
+    'pydantic.json:pydantic_encoder': 'pydantic.deprecated.json:pydantic_encoder',
+    'pydantic:validate_arguments': 'pydantic.deprecated.decorator:validate_arguments',
+    'pydantic.json:custom_pydantic_encoder': 'pydantic.deprecated.json:custom_pydantic_encoder',
+    'pydantic.json:timedelta_isoformat': 'pydantic.deprecated.json:timedelta_isoformat',
+    'pydantic.decorator:validate_arguments': 'pydantic.deprecated.decorator:validate_arguments',
+    'pydantic.class_validators:validator': 'pydantic.deprecated.class_validators:validator',
+    'pydantic.class_validators:root_validator': 'pydantic.deprecated.class_validators:root_validator',
+    'pydantic.config:BaseConfig': 'pydantic.deprecated.config:BaseConfig',
+    'pydantic.config:Extra': 'pydantic.deprecated.config:Extra',
+}
+
+REDIRECT_TO_V1 = {
+    f'pydantic.utils:{obj}': f'pydantic.v1.utils:{obj}'
+    for obj in (
+        'deep_update',
+        'GetterDict',
+        'lenient_issubclass',
+        'lenient_isinstance',
+        'is_valid_field',
+        'update_not_none',
+        'import_string',
+        'Representation',
+        'ROOT_KEY',
+        'smart_deepcopy',
+        'sequence_like',
+    )
+}
+
+
+REMOVED_IN_V2 = {
+    'pydantic:ConstrainedBytes',
+    'pydantic:ConstrainedDate',
+    'pydantic:ConstrainedDecimal',
+    'pydantic:ConstrainedFloat',
+    'pydantic:ConstrainedFrozenSet',
+    'pydantic:ConstrainedInt',
+    'pydantic:ConstrainedList',
+    'pydantic:ConstrainedSet',
+    'pydantic:ConstrainedStr',
+    'pydantic:JsonWrapper',
+    'pydantic:NoneBytes',
+    'pydantic:NoneStr',
+    'pydantic:NoneStrBytes',
+    'pydantic:Protocol',
+    'pydantic:Required',
+    'pydantic:StrBytes',
+    'pydantic:compiled',
+    'pydantic.config:get_config',
+    'pydantic.config:inherit_config',
+    'pydantic.config:prepare_config',
+    'pydantic:create_model_from_namedtuple',
+    'pydantic:create_model_from_typeddict',
+    'pydantic.dataclasses:create_pydantic_model_from_dataclass',
+    'pydantic.dataclasses:make_dataclass_validator',
+    'pydantic.dataclasses:set_validation',
+    'pydantic.datetime_parse:parse_date',
+    'pydantic.datetime_parse:parse_time',
+    'pydantic.datetime_parse:parse_datetime',
+    'pydantic.datetime_parse:parse_duration',
+    'pydantic.error_wrappers:ErrorWrapper',
+    'pydantic.errors:AnyStrMaxLengthError',
+    'pydantic.errors:AnyStrMinLengthError',
+    'pydantic.errors:ArbitraryTypeError',
+    'pydantic.errors:BoolError',
+    'pydantic.errors:BytesError',
+    'pydantic.errors:CallableError',
+    'pydantic.errors:ClassError',
+    'pydantic.errors:ColorError',
+    'pydantic.errors:ConfigError',
+    'pydantic.errors:DataclassTypeError',
+    'pydantic.errors:DateError',
+    'pydantic.errors:DateNotInTheFutureError',
+    'pydantic.errors:DateNotInThePastError',
+    'pydantic.errors:DateTimeError',
+    'pydantic.errors:DecimalError',
+    'pydantic.errors:DecimalIsNotFiniteError',
+    'pydantic.errors:DecimalMaxDigitsError',
+    'pydantic.errors:DecimalMaxPlacesError',
+    'pydantic.errors:DecimalWholeDigitsError',
+    'pydantic.errors:DictError',
+    'pydantic.errors:DurationError',
+    'pydantic.errors:EmailError',
+    'pydantic.errors:EnumError',
+    'pydantic.errors:EnumMemberError',
+    'pydantic.errors:ExtraError',
+    'pydantic.errors:FloatError',
+    'pydantic.errors:FrozenSetError',
+    'pydantic.errors:FrozenSetMaxLengthError',
+    'pydantic.errors:FrozenSetMinLengthError',
+    'pydantic.errors:HashableError',
+    'pydantic.errors:IPv4AddressError',
+    'pydantic.errors:IPv4InterfaceError',
+    'pydantic.errors:IPv4NetworkError',
+    'pydantic.errors:IPv6AddressError',
+    'pydantic.errors:IPv6InterfaceError',
+    'pydantic.errors:IPv6NetworkError',
+    'pydantic.errors:IPvAnyAddressError',
+    'pydantic.errors:IPvAnyInterfaceError',
+    'pydantic.errors:IPvAnyNetworkError',
+    'pydantic.errors:IntEnumError',
+    'pydantic.errors:IntegerError',
+    'pydantic.errors:InvalidByteSize',
+    'pydantic.errors:InvalidByteSizeUnit',
+    'pydantic.errors:InvalidDiscriminator',
+    'pydantic.errors:InvalidLengthForBrand',
+    'pydantic.errors:JsonError',
+    'pydantic.errors:JsonTypeError',
+    'pydantic.errors:ListError',
+    'pydantic.errors:ListMaxLengthError',
+    'pydantic.errors:ListMinLengthError',
+    'pydantic.errors:ListUniqueItemsError',
+    'pydantic.errors:LuhnValidationError',
+    'pydantic.errors:MissingDiscriminator',
+    'pydantic.errors:MissingError',
+    'pydantic.errors:NoneIsAllowedError',
+    'pydantic.errors:NoneIsNotAllowedError',
+    'pydantic.errors:NotDigitError',
+    'pydantic.errors:NotNoneError',
+    'pydantic.errors:NumberNotGeError',
+    'pydantic.errors:NumberNotGtError',
+    'pydantic.errors:NumberNotLeError',
+    'pydantic.errors:NumberNotLtError',
+    'pydantic.errors:NumberNotMultipleError',
+    'pydantic.errors:PathError',
+    'pydantic.errors:PathNotADirectoryError',
+    'pydantic.errors:PathNotAFileError',
+    'pydantic.errors:PathNotExistsError',
+    'pydantic.errors:PatternError',
+    'pydantic.errors:PyObjectError',
+    'pydantic.errors:PydanticTypeError',
+    'pydantic.errors:PydanticValueError',
+    'pydantic.errors:SequenceError',
+    'pydantic.errors:SetError',
+    'pydantic.errors:SetMaxLengthError',
+    'pydantic.errors:SetMinLengthError',
+    'pydantic.errors:StrError',
+    'pydantic.errors:StrRegexError',
+    'pydantic.errors:StrictBoolError',
+    'pydantic.errors:SubclassError',
+    'pydantic.errors:TimeError',
+    'pydantic.errors:TupleError',
+    'pydantic.errors:TupleLengthError',
+    'pydantic.errors:UUIDError',
+    'pydantic.errors:UUIDVersionError',
+    'pydantic.errors:UrlError',
+    'pydantic.errors:UrlExtraError',
+    'pydantic.errors:UrlHostError',
+    'pydantic.errors:UrlHostTldError',
+    'pydantic.errors:UrlPortError',
+    'pydantic.errors:UrlSchemeError',
+    'pydantic.errors:UrlSchemePermittedError',
+    'pydantic.errors:UrlUserInfoError',
+    'pydantic.errors:WrongConstantError',
+    'pydantic.main:validate_model',
+    'pydantic.networks:stricturl',
+    'pydantic:parse_file_as',
+    'pydantic:parse_raw_as',
+    'pydantic:stricturl',
+    'pydantic.tools:parse_file_as',
+    'pydantic.tools:parse_raw_as',
+    'pydantic.types:ConstrainedBytes',
+    'pydantic.types:ConstrainedDate',
+    'pydantic.types:ConstrainedDecimal',
+    'pydantic.types:ConstrainedFloat',
+    'pydantic.types:ConstrainedFrozenSet',
+    'pydantic.types:ConstrainedInt',
+    'pydantic.types:ConstrainedList',
+    'pydantic.types:ConstrainedSet',
+    'pydantic.types:ConstrainedStr',
+    'pydantic.types:JsonWrapper',
+    'pydantic.types:NoneBytes',
+    'pydantic.types:NoneStr',
+    'pydantic.types:NoneStrBytes',
+    'pydantic.types:StrBytes',
+    'pydantic.typing:evaluate_forwardref',
+    'pydantic.typing:AbstractSetIntStr',
+    'pydantic.typing:AnyCallable',
+    'pydantic.typing:AnyClassMethod',
+    'pydantic.typing:CallableGenerator',
+    'pydantic.typing:DictAny',
+    'pydantic.typing:DictIntStrAny',
+    'pydantic.typing:DictStrAny',
+    'pydantic.typing:IntStr',
+    'pydantic.typing:ListStr',
+    'pydantic.typing:MappingIntStrAny',
+    'pydantic.typing:NoArgAnyCallable',
+    'pydantic.typing:NoneType',
+    'pydantic.typing:ReprArgs',
+    'pydantic.typing:SetStr',
+    'pydantic.typing:StrPath',
+    'pydantic.typing:TupleGenerator',
+    'pydantic.typing:WithArgsTypes',
+    'pydantic.typing:all_literal_values',
+    'pydantic.typing:display_as_type',
+    'pydantic.typing:get_all_type_hints',
+    'pydantic.typing:get_args',
+    'pydantic.typing:get_origin',
+    'pydantic.typing:get_sub_types',
+    'pydantic.typing:is_callable_type',
+    'pydantic.typing:is_classvar',
+    'pydantic.typing:is_finalvar',
+    'pydantic.typing:is_literal_type',
+    'pydantic.typing:is_namedtuple',
+    'pydantic.typing:is_new_type',
+    'pydantic.typing:is_none_type',
+    'pydantic.typing:is_typeddict',
+    'pydantic.typing:is_typeddict_special',
+    'pydantic.typing:is_union',
+    'pydantic.typing:new_type_supertype',
+    'pydantic.typing:resolve_annotations',
+    'pydantic.typing:typing_base',
+    'pydantic.typing:update_field_forward_refs',
+    'pydantic.typing:update_model_forward_refs',
+    'pydantic.utils:ClassAttribute',
+    'pydantic.utils:DUNDER_ATTRIBUTES',
+    'pydantic.utils:PyObjectStr',
+    'pydantic.utils:ValueItems',
+    'pydantic.utils:almost_equal_floats',
+    'pydantic.utils:get_discriminator_alias_and_values',
+    'pydantic.utils:get_model',
+    'pydantic.utils:get_unique_discriminator_alias',
+    'pydantic.utils:in_ipython',
+    'pydantic.utils:is_valid_identifier',
+    'pydantic.utils:path_type',
+    'pydantic.utils:validate_field_name',
+    'pydantic:validate_model',
+}
+
+
+def getattr_migration(module: str) -> Callable[[str], Any]:
+    """Implement PEP 562 for objects that were either moved or removed on the migration
+    to V2.
+
+    Args:
+        module: The module name.
+
+    Returns:
+        A callable that will raise an error if the object is not found.
+    """
+    # This avoids circular import with errors.py.
+    from .errors import PydanticImportError
+
+    def wrapper(name: str) -> object:
+        """Raise an error if the object is not found, or warn if it was moved.
+
+        In case it was moved, it still returns the object.
+
+        Args:
+            name: The object name.
+
+        Returns:
+            The object.
+        """
+        if name == '__path__':
+            raise AttributeError(f'module {module!r} has no attribute {name!r}')
+
+        import warnings
+
+        from ._internal._validators import import_string
+
+        import_path = f'{module}:{name}'
+        if import_path in MOVED_IN_V2.keys():
+            new_location = MOVED_IN_V2[import_path]
+            warnings.warn(
+                f'`{import_path}` has been moved to `{new_location}`.',
+                category=PydanticDeprecatedSince20,
+                stacklevel=2,
+            )
+            return import_string(MOVED_IN_V2[import_path])
+        if import_path in DEPRECATED_MOVED_IN_V2:
+            # skip the warning here because a deprecation warning will be raised elsewhere
+            return import_string(DEPRECATED_MOVED_IN_V2[import_path])
+        if import_path in REDIRECT_TO_V1:
+            new_location = REDIRECT_TO_V1[import_path]
+            warnings.warn(
+                f'`{import_path}` has been removed. We are importing from `{new_location}` instead.'
+                'See the migration guide for more details: https://docs.pydantic.dev/latest/migration/',
+                category=PydanticDeprecatedSince20,
+                stacklevel=2,
+            )
+            return import_string(REDIRECT_TO_V1[import_path])
+        if import_path == 'pydantic:BaseSettings':
+            raise PydanticImportError(
+                '`BaseSettings` has been moved to the `pydantic-settings` package. '
+                f'See https://docs.pydantic.dev/{version_short()}/migration/#basesettings-has-moved-to-pydantic-settings '
+                'for more details.'
+            )
+        if import_path in REMOVED_IN_V2:
+            raise PydanticImportError(f'`{import_path}` has been removed in V2.')
+        globals: dict[str, Any] = sys.modules[module].__dict__
+        if name in globals:
+            return globals[name]
+        raise AttributeError(f'module {module!r} has no attribute {name!r}')
+
+    return wrapper

.mcp-venv/lib/python3.13/site-packages/pydantic/alias_generators.py

@@ -0,0 +1,62 @@
+"""Alias generators for converting between different capitalization conventions."""
+
+import re
+
+__all__ = ('to_pascal', 'to_camel', 'to_snake')
+
+# TODO: in V3, change the argument names to be more descriptive
+# Generally, don't only convert from snake_case, or name the functions
+# more specifically like snake_to_camel.
+
+
+def to_pascal(snake: str) -> str:
+    """Convert a snake_case string to PascalCase.
+
+    Args:
+        snake: The string to convert.
+
+    Returns:
+        The PascalCase string.
+    """
+    camel = snake.title()
+    return re.sub('([0-9A-Za-z])_(?=[0-9A-Z])', lambda m: m.group(1), camel)
+
+
+def to_camel(snake: str) -> str:
+    """Convert a snake_case string to camelCase.
+
+    Args:
+        snake: The string to convert.
+
+    Returns:
+        The converted camelCase string.
+    """
+    # If the string is already in camelCase and does not contain a digit followed
+    # by a lowercase letter, return it as it is
+    if re.match('^[a-z]+[A-Za-z0-9]*$', snake) and not re.search(r'\d[a-z]', snake):
+        return snake
+
+    camel = to_pascal(snake)
+    return re.sub('(^_*[A-Z])', lambda m: m.group(1).lower(), camel)
+
+
+def to_snake(camel: str) -> str:
+    """Convert a PascalCase, camelCase, or kebab-case string to snake_case.
+
+    Args:
+        camel: The string to convert.
+
+    Returns:
+        The converted string in snake_case.
+    """
+    # Handle the sequence of uppercase letters followed by a lowercase letter
+    snake = re.sub(r'([A-Z]+)([A-Z][a-z])', lambda m: f'{m.group(1)}_{m.group(2)}', camel)
+    # Insert an underscore between a lowercase letter and an uppercase letter
+    snake = re.sub(r'([a-z])([A-Z])', lambda m: f'{m.group(1)}_{m.group(2)}', snake)
+    # Insert an underscore between a digit and an uppercase letter
+    snake = re.sub(r'([0-9])([A-Z])', lambda m: f'{m.group(1)}_{m.group(2)}', snake)
+    # Insert an underscore between a lowercase letter and a digit
+    snake = re.sub(r'([a-z])([0-9])', lambda m: f'{m.group(1)}_{m.group(2)}', snake)
+    # Replace hyphens with underscores to handle kebab-case
+    snake = snake.replace('-', '_')
+    return snake.lower()

.mcp-venv/lib/python3.13/site-packages/pydantic/aliases.py

@@ -0,0 +1,135 @@
+"""Support for alias configurations."""
+
+from __future__ import annotations
+
+import dataclasses
+from typing import Any, Callable, Literal
+
+from pydantic_core import PydanticUndefined
+
+from ._internal import _internal_dataclass
+
+__all__ = ('AliasGenerator', 'AliasPath', 'AliasChoices')
+
+
+@dataclasses.dataclass(**_internal_dataclass.slots_true)
+class AliasPath:
+    """!!! abstract "Usage Documentation"
+        [`AliasPath` and `AliasChoices`](../concepts/alias.md#aliaspath-and-aliaschoices)
+
+    A data class used by `validation_alias` as a convenience to create aliases.
+
+    Attributes:
+        path: A list of string or integer aliases.
+    """
+
+    path: list[int | str]
+
+    def __init__(self, first_arg: str, *args: str | int) -> None:
+        self.path = [first_arg] + list(args)
+
+    def convert_to_aliases(self) -> list[str | int]:
+        """Converts arguments to a list of string or integer aliases.
+
+        Returns:
+            The list of aliases.
+        """
+        return self.path
+
+    def search_dict_for_path(self, d: dict) -> Any:
+        """Searches a dictionary for the path specified by the alias.
+
+        Returns:
+            The value at the specified path, or `PydanticUndefined` if the path is not found.
+        """
+        v = d
+        for k in self.path:
+            if isinstance(v, str):
+                # disallow indexing into a str, like for AliasPath('x', 0) and x='abc'
+                return PydanticUndefined
+            try:
+                v = v[k]
+            except (KeyError, IndexError, TypeError):
+                return PydanticUndefined
+        return v
+
+
+@dataclasses.dataclass(**_internal_dataclass.slots_true)
+class AliasChoices:
+    """!!! abstract "Usage Documentation"
+        [`AliasPath` and `AliasChoices`](../concepts/alias.md#aliaspath-and-aliaschoices)
+
+    A data class used by `validation_alias` as a convenience to create aliases.
+
+    Attributes:
+        choices: A list containing a string or `AliasPath`.
+    """
+
+    choices: list[str | AliasPath]
+
+    def __init__(self, first_choice: str | AliasPath, *choices: str | AliasPath) -> None:
+        self.choices = [first_choice] + list(choices)
+
+    def convert_to_aliases(self) -> list[list[str | int]]:
+        """Converts arguments to a list of lists containing string or integer aliases.
+
+        Returns:
+            The list of aliases.
+        """
+        aliases: list[list[str | int]] = []
+        for c in self.choices:
+            if isinstance(c, AliasPath):
+                aliases.append(c.convert_to_aliases())
+            else:
+                aliases.append([c])
+        return aliases
+
+
+@dataclasses.dataclass(**_internal_dataclass.slots_true)
+class AliasGenerator:
+    """!!! abstract "Usage Documentation"
+        [Using an `AliasGenerator`](../concepts/alias.md#using-an-aliasgenerator)
+
+    A data class used by `alias_generator` as a convenience to create various aliases.
+
+    Attributes:
+        alias: A callable that takes a field name and returns an alias for it.
+        validation_alias: A callable that takes a field name and returns a validation alias for it.
+        serialization_alias: A callable that takes a field name and returns a serialization alias for it.
+    """
+
+    alias: Callable[[str], str] | None = None
+    validation_alias: Callable[[str], str | AliasPath | AliasChoices] | None = None
+    serialization_alias: Callable[[str], str] | None = None
+
+    def _generate_alias(
+        self,
+        alias_kind: Literal['alias', 'validation_alias', 'serialization_alias'],
+        allowed_types: tuple[type[str] | type[AliasPath] | type[AliasChoices], ...],
+        field_name: str,
+    ) -> str | AliasPath | AliasChoices | None:
+        """Generate an alias of the specified kind. Returns None if the alias generator is None.
+
+        Raises:
+            TypeError: If the alias generator produces an invalid type.
+        """
+        alias = None
+        if alias_generator := getattr(self, alias_kind):
+            alias = alias_generator(field_name)
+            if alias and not isinstance(alias, allowed_types):
+                raise TypeError(
+                    f'Invalid `{alias_kind}` type. `{alias_kind}` generator must produce one of `{allowed_types}`'
+                )
+        return alias
+
+    def generate_aliases(self, field_name: str) -> tuple[str | None, str | AliasPath | AliasChoices | None, str | None]:
+        """Generate `alias`, `validation_alias`, and `serialization_alias` for a field.
+
+        Returns:
+            A tuple of three aliases - validation, alias, and serialization.
+        """
+        alias = self._generate_alias('alias', (str,), field_name)
+        validation_alias = self._generate_alias('validation_alias', (str, AliasChoices, AliasPath), field_name)
+        serialization_alias = self._generate_alias('serialization_alias', (str,), field_name)
+
+        return alias, validation_alias, serialization_alias  # type: ignore

.mcp-venv/lib/python3.13/site-packages/pydantic/annotated_handlers.py

@@ -0,0 +1,122 @@
+"""Type annotations to use with `__get_pydantic_core_schema__` and `__get_pydantic_json_schema__`."""
+
+from __future__ import annotations as _annotations
+
+from typing import TYPE_CHECKING, Any, Union
+
+from pydantic_core import core_schema
+
+if TYPE_CHECKING:
+    from ._internal._namespace_utils import NamespacesTuple
+    from .json_schema import JsonSchemaMode, JsonSchemaValue
+
+    CoreSchemaOrField = Union[
+        core_schema.CoreSchema,
+        core_schema.ModelField,
+        core_schema.DataclassField,
+        core_schema.TypedDictField,
+        core_schema.ComputedField,
+    ]
+
+__all__ = 'GetJsonSchemaHandler', 'GetCoreSchemaHandler'
+
+
+class GetJsonSchemaHandler:
+    """Handler to call into the next JSON schema generation function.
+
+    Attributes:
+        mode: Json schema mode, can be `validation` or `serialization`.
+    """
+
+    mode: JsonSchemaMode
+
+    def __call__(self, core_schema: CoreSchemaOrField, /) -> JsonSchemaValue:
+        """Call the inner handler and get the JsonSchemaValue it returns.
+        This will call the next JSON schema modifying function up until it calls
+        into `pydantic.json_schema.GenerateJsonSchema`, which will raise a
+        `pydantic.errors.PydanticInvalidForJsonSchema` error if it cannot generate
+        a JSON schema.
+
+        Args:
+            core_schema: A `pydantic_core.core_schema.CoreSchema`.
+
+        Returns:
+            JsonSchemaValue: The JSON schema generated by the inner JSON schema modify
+            functions.
+        """
+        raise NotImplementedError
+
+    def resolve_ref_schema(self, maybe_ref_json_schema: JsonSchemaValue, /) -> JsonSchemaValue:
+        """Get the real schema for a `{"$ref": ...}` schema.
+        If the schema given is not a `$ref` schema, it will be returned as is.
+        This means you don't have to check before calling this function.
+
+        Args:
+            maybe_ref_json_schema: A JsonSchemaValue which may be a `$ref` schema.
+
+        Raises:
+            LookupError: If the ref is not found.
+
+        Returns:
+            JsonSchemaValue: A JsonSchemaValue that has no `$ref`.
+        """
+        raise NotImplementedError
+
+
+class GetCoreSchemaHandler:
+    """Handler to call into the next CoreSchema schema generation function."""
+
+    def __call__(self, source_type: Any, /) -> core_schema.CoreSchema:
+        """Call the inner handler and get the CoreSchema it returns.
+        This will call the next CoreSchema modifying function up until it calls
+        into Pydantic's internal schema generation machinery, which will raise a
+        `pydantic.errors.PydanticSchemaGenerationError` error if it cannot generate
+        a CoreSchema for the given source type.
+
+        Args:
+            source_type: The input type.
+
+        Returns:
+            CoreSchema: The `pydantic-core` CoreSchema generated.
+        """
+        raise NotImplementedError
+
+    def generate_schema(self, source_type: Any, /) -> core_schema.CoreSchema:
+        """Generate a schema unrelated to the current context.
+        Use this function if e.g. you are handling schema generation for a sequence
+        and want to generate a schema for its items.
+        Otherwise, you may end up doing something like applying a `min_length` constraint
+        that was intended for the sequence itself to its items!
+
+        Args:
+            source_type: The input type.
+
+        Returns:
+            CoreSchema: The `pydantic-core` CoreSchema generated.
+        """
+        raise NotImplementedError
+
+    def resolve_ref_schema(self, maybe_ref_schema: core_schema.CoreSchema, /) -> core_schema.CoreSchema:
+        """Get the real schema for a `definition-ref` schema.
+        If the schema given is not a `definition-ref` schema, it will be returned as is.
+        This means you don't have to check before calling this function.
+
+        Args:
+            maybe_ref_schema: A `CoreSchema`, `ref`-based or not.
+
+        Raises:
+            LookupError: If the `ref` is not found.
+
+        Returns:
+            A concrete `CoreSchema`.
+        """
+        raise NotImplementedError
+
+    @property
+    def field_name(self) -> str | None:
+        """Get the name of the closest field to this validator."""
+        raise NotImplementedError
+
+    def _get_types_namespace(self) -> NamespacesTuple:
+        """Internal method used during type resolution for serializer annotations."""
+        raise NotImplementedError

.mcp-venv/lib/python3.13/site-packages/pydantic/class_validators.py

@@ -0,0 +1,5 @@
+"""`class_validators` module is a backport module from V1."""
+
+from ._migration import getattr_migration
+
+__getattr__ = getattr_migration(__name__)

.mcp-venv/lib/python3.13/site-packages/pydantic/color.py

@@ -0,0 +1,604 @@
+"""Color definitions are used as per the CSS3
+[CSS Color Module Level 3](http://www.w3.org/TR/css3-color/#svg-color) specification.
+
+A few colors have multiple names referring to the sames colors, eg. `grey` and `gray` or `aqua` and `cyan`.
+
+In these cases the _last_ color when sorted alphabetically takes preferences,
+eg. `Color((0, 255, 255)).as_named() == 'cyan'` because "cyan" comes after "aqua".
+
+Warning: Deprecated
+    The `Color` class is deprecated, use `pydantic_extra_types` instead.
+    See [`pydantic-extra-types.Color`](../usage/types/extra_types/color_types.md)
+    for more information.
+"""
+
+import math
+import re
+from colorsys import hls_to_rgb, rgb_to_hls
+from typing import Any, Callable, Optional, Union, cast
+
+from pydantic_core import CoreSchema, PydanticCustomError, core_schema
+from typing_extensions import deprecated
+
+from ._internal import _repr
+from ._internal._schema_generation_shared import GetJsonSchemaHandler as _GetJsonSchemaHandler
+from .json_schema import JsonSchemaValue
+from .warnings import PydanticDeprecatedSince20
+
+ColorTuple = Union[tuple[int, int, int], tuple[int, int, int, float]]
+ColorType = Union[ColorTuple, str]
+HslColorTuple = Union[tuple[float, float, float], tuple[float, float, float, float]]
+
+
+class RGBA:
+    """Internal use only as a representation of a color."""
+
+    __slots__ = 'r', 'g', 'b', 'alpha', '_tuple'
+
+    def __init__(self, r: float, g: float, b: float, alpha: Optional[float]):
+        self.r = r
+        self.g = g
+        self.b = b
+        self.alpha = alpha
+
+        self._tuple: tuple[float, float, float, Optional[float]] = (r, g, b, alpha)
+
+    def __getitem__(self, item: Any) -> Any:
+        return self._tuple[item]
+
+
+# these are not compiled here to avoid import slowdown, they'll be compiled the first time they're used, then cached
+_r_255 = r'(\d{1,3}(?:\.\d+)?)'
+_r_comma = r'\s*,\s*'
+_r_alpha = r'(\d(?:\.\d+)?|\.\d+|\d{1,2}%)'
+_r_h = r'(-?\d+(?:\.\d+)?|-?\.\d+)(deg|rad|turn)?'
+_r_sl = r'(\d{1,3}(?:\.\d+)?)%'
+r_hex_short = r'\s*(?:#|0x)?([0-9a-f])([0-9a-f])([0-9a-f])([0-9a-f])?\s*'
+r_hex_long = r'\s*(?:#|0x)?([0-9a-f]{2})([0-9a-f]{2})([0-9a-f]{2})([0-9a-f]{2})?\s*'
+# CSS3 RGB examples: rgb(0, 0, 0), rgba(0, 0, 0, 0.5), rgba(0, 0, 0, 50%)
+r_rgb = rf'\s*rgba?\(\s*{_r_255}{_r_comma}{_r_255}{_r_comma}{_r_255}(?:{_r_comma}{_r_alpha})?\s*\)\s*'
+# CSS3 HSL examples: hsl(270, 60%, 50%), hsla(270, 60%, 50%, 0.5), hsla(270, 60%, 50%, 50%)
+r_hsl = rf'\s*hsla?\(\s*{_r_h}{_r_comma}{_r_sl}{_r_comma}{_r_sl}(?:{_r_comma}{_r_alpha})?\s*\)\s*'
+# CSS4 RGB examples: rgb(0 0 0), rgb(0 0 0 / 0.5), rgb(0 0 0 / 50%), rgba(0 0 0 / 50%)
+r_rgb_v4_style = rf'\s*rgba?\(\s*{_r_255}\s+{_r_255}\s+{_r_255}(?:\s*/\s*{_r_alpha})?\s*\)\s*'
+# CSS4 HSL examples: hsl(270 60% 50%), hsl(270 60% 50% / 0.5), hsl(270 60% 50% / 50%), hsla(270 60% 50% / 50%)
+r_hsl_v4_style = rf'\s*hsla?\(\s*{_r_h}\s+{_r_sl}\s+{_r_sl}(?:\s*/\s*{_r_alpha})?\s*\)\s*'
+
+# colors where the two hex characters are the same, if all colors match this the short version of hex colors can be used
+repeat_colors = {int(c * 2, 16) for c in '0123456789abcdef'}
+rads = 2 * math.pi
+
+
+@deprecated(
+    'The `Color` class is deprecated, use `pydantic_extra_types` instead. '
+    'See https://docs.pydantic.dev/latest/api/pydantic_extra_types_color/.',
+    category=PydanticDeprecatedSince20,
+)
+class Color(_repr.Representation):
+    """Represents a color."""
+
+    __slots__ = '_original', '_rgba'
+
+    def __init__(self, value: ColorType) -> None:
+        self._rgba: RGBA
+        self._original: ColorType
+        if isinstance(value, (tuple, list)):
+            self._rgba = parse_tuple(value)
+        elif isinstance(value, str):
+            self._rgba = parse_str(value)
+        elif isinstance(value, Color):
+            self._rgba = value._rgba
+            value = value._original
+        else:
+            raise PydanticCustomError(
+                'color_error', 'value is not a valid color: value must be a tuple, list or string'
+            )
+
+        # if we've got here value must be a valid color
+        self._original = value
+
+    @classmethod
+    def __get_pydantic_json_schema__(
+        cls, core_schema: core_schema.CoreSchema, handler: _GetJsonSchemaHandler
+    ) -> JsonSchemaValue:
+        field_schema = {}
+        field_schema.update(type='string', format='color')
+        return field_schema
+
+    def original(self) -> ColorType:
+        """Original value passed to `Color`."""
+        return self._original
+
+    def as_named(self, *, fallback: bool = False) -> str:
+        """Returns the name of the color if it can be found in `COLORS_BY_VALUE` dictionary,
+        otherwise returns the hexadecimal representation of the color or raises `ValueError`.
+
+        Args:
+            fallback: If True, falls back to returning the hexadecimal representation of
+                the color instead of raising a ValueError when no named color is found.
+
+        Returns:
+            The name of the color, or the hexadecimal representation of the color.
+
+        Raises:
+            ValueError: When no named color is found and fallback is `False`.
+        """
+        if self._rgba.alpha is None:
+            rgb = cast(tuple[int, int, int], self.as_rgb_tuple())
+            try:
+                return COLORS_BY_VALUE[rgb]
+            except KeyError as e:
+                if fallback:
+                    return self.as_hex()
+                else:
+                    raise ValueError('no named color found, use fallback=True, as_hex() or as_rgb()') from e
+        else:
+            return self.as_hex()
+
+    def as_hex(self) -> str:
+        """Returns the hexadecimal representation of the color.
+
+        Hex string representing the color can be 3, 4, 6, or 8 characters depending on whether the string
+        a "short" representation of the color is possible and whether there's an alpha channel.
+
+        Returns:
+            The hexadecimal representation of the color.
+        """
+        values = [float_to_255(c) for c in self._rgba[:3]]
+        if self._rgba.alpha is not None:
+            values.append(float_to_255(self._rgba.alpha))
+
+        as_hex = ''.join(f'{v:02x}' for v in values)
+        if all(c in repeat_colors for c in values):
+            as_hex = ''.join(as_hex[c] for c in range(0, len(as_hex), 2))
+        return '#' + as_hex
+
+    def as_rgb(self) -> str:
+        """Color as an `rgb(<r>, <g>, <b>)` or `rgba(<r>, <g>, <b>, <a>)` string."""
+        if self._rgba.alpha is None:
+            return f'rgb({float_to_255(self._rgba.r)}, {float_to_255(self._rgba.g)}, {float_to_255(self._rgba.b)})'
+        else:
+            return (
+                f'rgba({float_to_255(self._rgba.r)}, {float_to_255(self._rgba.g)}, {float_to_255(self._rgba.b)}, '
+                f'{round(self._alpha_float(), 2)})'
+            )
+
+    def as_rgb_tuple(self, *, alpha: Optional[bool] = None) -> ColorTuple:
+        """Returns the color as an RGB or RGBA tuple.
+
+        Args:
+            alpha: Whether to include the alpha channel. There are three options for this input:
+
+                - `None` (default): Include alpha only if it's set. (e.g. not `None`)
+                - `True`: Always include alpha.
+                - `False`: Always omit alpha.
+
+        Returns:
+            A tuple that contains the values of the red, green, and blue channels in the range 0 to 255.
+                If alpha is included, it is in the range 0 to 1.
+        """
+        r, g, b = (float_to_255(c) for c in self._rgba[:3])
+        if alpha is None:
+            if self._rgba.alpha is None:
+                return r, g, b
+            else:
+                return r, g, b, self._alpha_float()
+        elif alpha:
+            return r, g, b, self._alpha_float()
+        else:
+            # alpha is False
+            return r, g, b
+
+    def as_hsl(self) -> str:
+        """Color as an `hsl(<h>, <s>, <l>)` or `hsl(<h>, <s>, <l>, <a>)` string."""
+        if self._rgba.alpha is None:
+            h, s, li = self.as_hsl_tuple(alpha=False)  # type: ignore
+            return f'hsl({h * 360:0.0f}, {s:0.0%}, {li:0.0%})'
+        else:
+            h, s, li, a = self.as_hsl_tuple(alpha=True)  # type: ignore
+            return f'hsl({h * 360:0.0f}, {s:0.0%}, {li:0.0%}, {round(a, 2)})'
+
+    def as_hsl_tuple(self, *, alpha: Optional[bool] = None) -> HslColorTuple:
+        """Returns the color as an HSL or HSLA tuple.
+
+        Args:
+            alpha: Whether to include the alpha channel.
+
+                - `None` (default): Include the alpha channel only if it's set (e.g. not `None`).
+                - `True`: Always include alpha.
+                - `False`: Always omit alpha.
+
+        Returns:
+            The color as a tuple of hue, saturation, lightness, and alpha (if included).
+                All elements are in the range 0 to 1.
+
+        Note:
+            This is HSL as used in HTML and most other places, not HLS as used in Python's `colorsys`.
+        """
+        h, l, s = rgb_to_hls(self._rgba.r, self._rgba.g, self._rgba.b)  # noqa: E741
+        if alpha is None:
+            if self._rgba.alpha is None:
+                return h, s, l
+            else:
+                return h, s, l, self._alpha_float()
+        if alpha:
+            return h, s, l, self._alpha_float()
+        else:
+            # alpha is False
+            return h, s, l
+
+    def _alpha_float(self) -> float:
+        return 1 if self._rgba.alpha is None else self._rgba.alpha
+
+    @classmethod
+    def __get_pydantic_core_schema__(
+        cls, source: type[Any], handler: Callable[[Any], CoreSchema]
+    ) -> core_schema.CoreSchema:
+        return core_schema.with_info_plain_validator_function(
+            cls._validate, serialization=core_schema.to_string_ser_schema()
+        )
+
+    @classmethod
+    def _validate(cls, __input_value: Any, _: Any) -> 'Color':
+        return cls(__input_value)
+
+    def __str__(self) -> str:
+        return self.as_named(fallback=True)
+
+    def __repr_args__(self) -> '_repr.ReprArgs':
+        return [(None, self.as_named(fallback=True))] + [('rgb', self.as_rgb_tuple())]
+
+    def __eq__(self, other: Any) -> bool:
+        return isinstance(other, Color) and self.as_rgb_tuple() == other.as_rgb_tuple()
+
+    def __hash__(self) -> int:
+        return hash(self.as_rgb_tuple())
+
+
+def parse_tuple(value: tuple[Any, ...]) -> RGBA:
+    """Parse a tuple or list to get RGBA values.
+
+    Args:
+        value: A tuple or list.
+
+    Returns:
+        An `RGBA` tuple parsed from the input tuple.
+
+    Raises:
+        PydanticCustomError: If tuple is not valid.
+    """
+    if len(value) == 3:
+        r, g, b = (parse_color_value(v) for v in value)
+        return RGBA(r, g, b, None)
+    elif len(value) == 4:
+        r, g, b = (parse_color_value(v) for v in value[:3])
+        return RGBA(r, g, b, parse_float_alpha(value[3]))
+    else:
+        raise PydanticCustomError('color_error', 'value is not a valid color: tuples must have length 3 or 4')
+
+
+def parse_str(value: str) -> RGBA:
+    """Parse a string representing a color to an RGBA tuple.
+
+    Possible formats for the input string include:
+
+    * named color, see `COLORS_BY_NAME`
+    * hex short eg. `<prefix>fff` (prefix can be `#`, `0x` or nothing)
+    * hex long eg. `<prefix>ffffff` (prefix can be `#`, `0x` or nothing)
+    * `rgb(<r>, <g>, <b>)`
+    * `rgba(<r>, <g>, <b>, <a>)`
+
+    Args:
+        value: A string representing a color.
+
+    Returns:
+        An `RGBA` tuple parsed from the input string.
+
+    Raises:
+        ValueError: If the input string cannot be parsed to an RGBA tuple.
+    """
+    value_lower = value.lower()
+    try:
+        r, g, b = COLORS_BY_NAME[value_lower]
+    except KeyError:
+        pass
+    else:
+        return ints_to_rgba(r, g, b, None)
+
+    m = re.fullmatch(r_hex_short, value_lower)
+    if m:
+        *rgb, a = m.groups()
+        r, g, b = (int(v * 2, 16) for v in rgb)
+        if a:
+            alpha: Optional[float] = int(a * 2, 16) / 255
+        else:
+            alpha = None
+        return ints_to_rgba(r, g, b, alpha)
+
+    m = re.fullmatch(r_hex_long, value_lower)
+    if m:
+        *rgb, a = m.groups()
+        r, g, b = (int(v, 16) for v in rgb)
+        if a:
+            alpha = int(a, 16) / 255
+        else:
+            alpha = None
+        return ints_to_rgba(r, g, b, alpha)
+
+    m = re.fullmatch(r_rgb, value_lower) or re.fullmatch(r_rgb_v4_style, value_lower)
+    if m:
+        return ints_to_rgba(*m.groups())  # type: ignore
+
+    m = re.fullmatch(r_hsl, value_lower) or re.fullmatch(r_hsl_v4_style, value_lower)
+    if m:
+        return parse_hsl(*m.groups())  # type: ignore
+
+    raise PydanticCustomError('color_error', 'value is not a valid color: string not recognised as a valid color')
+
+
+def ints_to_rgba(r: Union[int, str], g: Union[int, str], b: Union[int, str], alpha: Optional[float] = None) -> RGBA:
+    """Converts integer or string values for RGB color and an optional alpha value to an `RGBA` object.
+
+    Args:
+        r: An integer or string representing the red color value.
+        g: An integer or string representing the green color value.
+        b: An integer or string representing the blue color value.
+        alpha: A float representing the alpha value. Defaults to None.
+
+    Returns:
+        An instance of the `RGBA` class with the corresponding color and alpha values.
+    """
+    return RGBA(parse_color_value(r), parse_color_value(g), parse_color_value(b), parse_float_alpha(alpha))
+
+
+def parse_color_value(value: Union[int, str], max_val: int = 255) -> float:
+    """Parse the color value provided and return a number between 0 and 1.
+
+    Args:
+        value: An integer or string color value.
+        max_val: Maximum range value. Defaults to 255.
+
+    Raises:
+        PydanticCustomError: If the value is not a valid color.
+
+    Returns:
+        A number between 0 and 1.
+    """
+    try:
+        color = float(value)
+    except ValueError:
+        raise PydanticCustomError('color_error', 'value is not a valid color: color values must be a valid number')
+    if 0 <= color <= max_val:
+        return color / max_val
+    else:
+        raise PydanticCustomError(
+            'color_error',
+            'value is not a valid color: color values must be in the range 0 to {max_val}',
+            {'max_val': max_val},
+        )
+
+
+def parse_float_alpha(value: Union[None, str, float, int]) -> Optional[float]:
+    """Parse an alpha value checking it's a valid float in the range 0 to 1.
+
+    Args:
+        value: The input value to parse.
+
+    Returns:
+        The parsed value as a float, or `None` if the value was None or equal 1.
+
+    Raises:
+        PydanticCustomError: If the input value cannot be successfully parsed as a float in the expected range.
+    """
+    if value is None:
+        return None
+    try:
+        if isinstance(value, str) and value.endswith('%'):
+            alpha = float(value[:-1]) / 100
+        else:
+            alpha = float(value)
+    except ValueError:
+        raise PydanticCustomError('color_error', 'value is not a valid color: alpha values must be a valid float')
+
+    if math.isclose(alpha, 1):
+        return None
+    elif 0 <= alpha <= 1:
+        return alpha
+    else:
+        raise PydanticCustomError('color_error', 'value is not a valid color: alpha values must be in the range 0 to 1')
+
+
+def parse_hsl(h: str, h_units: str, sat: str, light: str, alpha: Optional[float] = None) -> RGBA:
+    """Parse raw hue, saturation, lightness, and alpha values and convert to RGBA.
+
+    Args:
+        h: The hue value.
+        h_units: The unit for hue value.
+        sat: The saturation value.
+        light: The lightness value.
+        alpha: Alpha value.
+
+    Returns:
+        An instance of `RGBA`.
+    """
+    s_value, l_value = parse_color_value(sat, 100), parse_color_value(light, 100)
+
+    h_value = float(h)
+    if h_units in {None, 'deg'}:
+        h_value = h_value % 360 / 360
+    elif h_units == 'rad':
+        h_value = h_value % rads / rads
+    else:
+        # turns
+        h_value = h_value % 1
+
+    r, g, b = hls_to_rgb(h_value, l_value, s_value)
+    return RGBA(r, g, b, parse_float_alpha(alpha))
+
+
+def float_to_255(c: float) -> int:
+    """Converts a float value between 0 and 1 (inclusive) to an integer between 0 and 255 (inclusive).
+
+    Args:
+        c: The float value to be converted. Must be between 0 and 1 (inclusive).
+
+    Returns:
+        The integer equivalent of the given float value rounded to the nearest whole number.
+
+    Raises:
+        ValueError: If the given float value is outside the acceptable range of 0 to 1 (inclusive).
+    """
+    return int(round(c * 255))
+
+
+COLORS_BY_NAME = {
+    'aliceblue': (240, 248, 255),
+    'antiquewhite': (250, 235, 215),
+    'aqua': (0, 255, 255),
+    'aquamarine': (127, 255, 212),
+    'azure': (240, 255, 255),
+    'beige': (245, 245, 220),
+    'bisque': (255, 228, 196),
+    'black': (0, 0, 0),
+    'blanchedalmond': (255, 235, 205),
+    'blue': (0, 0, 255),
+    'blueviolet': (138, 43, 226),
+    'brown': (165, 42, 42),
+    'burlywood': (222, 184, 135),
+    'cadetblue': (95, 158, 160),
+    'chartreuse': (127, 255, 0),
+    'chocolate': (210, 105, 30),
+    'coral': (255, 127, 80),
+    'cornflowerblue': (100, 149, 237),
+    'cornsilk': (255, 248, 220),
+    'crimson': (220, 20, 60),
+    'cyan': (0, 255, 255),
+    'darkblue': (0, 0, 139),
+    'darkcyan': (0, 139, 139),
+    'darkgoldenrod': (184, 134, 11),
+    'darkgray': (169, 169, 169),
+    'darkgreen': (0, 100, 0),
+    'darkgrey': (169, 169, 169),
+    'darkkhaki': (189, 183, 107),
+    'darkmagenta': (139, 0, 139),
+    'darkolivegreen': (85, 107, 47),
+    'darkorange': (255, 140, 0),
+    'darkorchid': (153, 50, 204),
+    'darkred': (139, 0, 0),
+    'darksalmon': (233, 150, 122),
+    'darkseagreen': (143, 188, 143),
+    'darkslateblue': (72, 61, 139),
+    'darkslategray': (47, 79, 79),
+    'darkslategrey': (47, 79, 79),
+    'darkturquoise': (0, 206, 209),
+    'darkviolet': (148, 0, 211),
+    'deeppink': (255, 20, 147),
+    'deepskyblue': (0, 191, 255),
+    'dimgray': (105, 105, 105),
+    'dimgrey': (105, 105, 105),
+    'dodgerblue': (30, 144, 255),
+    'firebrick': (178, 34, 34),
+    'floralwhite': (255, 250, 240),
+    'forestgreen': (34, 139, 34),
+    'fuchsia': (255, 0, 255),
+    'gainsboro': (220, 220, 220),
+    'ghostwhite': (248, 248, 255),
+    'gold': (255, 215, 0),
+    'goldenrod': (218, 165, 32),
+    'gray': (128, 128, 128),
+    'green': (0, 128, 0),
+    'greenyellow': (173, 255, 47),
+    'grey': (128, 128, 128),
+    'honeydew': (240, 255, 240),
+    'hotpink': (255, 105, 180),
+    'indianred': (205, 92, 92),
+    'indigo': (75, 0, 130),
+    'ivory': (255, 255, 240),
+    'khaki': (240, 230, 140),
+    'lavender': (230, 230, 250),
+    'lavenderblush': (255, 240, 245),
+    'lawngreen': (124, 252, 0),
+    'lemonchiffon': (255, 250, 205),
+    'lightblue': (173, 216, 230),
+    'lightcoral': (240, 128, 128),
+    'lightcyan': (224, 255, 255),
+    'lightgoldenrodyellow': (250, 250, 210),
+    'lightgray': (211, 211, 211),
+    'lightgreen': (144, 238, 144),
+    'lightgrey': (211, 211, 211),
+    'lightpink': (255, 182, 193),
+    'lightsalmon': (255, 160, 122),
+    'lightseagreen': (32, 178, 170),
+    'lightskyblue': (135, 206, 250),
+    'lightslategray': (119, 136, 153),
+    'lightslategrey': (119, 136, 153),
+    'lightsteelblue': (176, 196, 222),
+    'lightyellow': (255, 255, 224),
+    'lime': (0, 255, 0),
+    'limegreen': (50, 205, 50),
+    'linen': (250, 240, 230),
+    'magenta': (255, 0, 255),
+    'maroon': (128, 0, 0),
+    'mediumaquamarine': (102, 205, 170),
+    'mediumblue': (0, 0, 205),
+    'mediumorchid': (186, 85, 211),
+    'mediumpurple': (147, 112, 219),
+    'mediumseagreen': (60, 179, 113),
+    'mediumslateblue': (123, 104, 238),
+    'mediumspringgreen': (0, 250, 154),
+    'mediumturquoise': (72, 209, 204),
+    'mediumvioletred': (199, 21, 133),
+    'midnightblue': (25, 25, 112),
+    'mintcream': (245, 255, 250),
+    'mistyrose': (255, 228, 225),
+    'moccasin': (255, 228, 181),
+    'navajowhite': (255, 222, 173),
+    'navy': (0, 0, 128),
+    'oldlace': (253, 245, 230),
+    'olive': (128, 128, 0),
+    'olivedrab': (107, 142, 35),
+    'orange': (255, 165, 0),
+    'orangered': (255, 69, 0),
+    'orchid': (218, 112, 214),
+    'palegoldenrod': (238, 232, 170),
+    'palegreen': (152, 251, 152),
+    'paleturquoise': (175, 238, 238),
+    'palevioletred': (219, 112, 147),
+    'papayawhip': (255, 239, 213),
+    'peachpuff': (255, 218, 185),
+    'peru': (205, 133, 63),
+    'pink': (255, 192, 203),
+    'plum': (221, 160, 221),
+    'powderblue': (176, 224, 230),
+    'purple': (128, 0, 128),
+    'red': (255, 0, 0),
+    'rosybrown': (188, 143, 143),
+    'royalblue': (65, 105, 225),
+    'saddlebrown': (139, 69, 19),
+    'salmon': (250, 128, 114),
+    'sandybrown': (244, 164, 96),
+    'seagreen': (46, 139, 87),
+    'seashell': (255, 245, 238),
+    'sienna': (160, 82, 45),
+    'silver': (192, 192, 192),
+    'skyblue': (135, 206, 235),
+    'slateblue': (106, 90, 205),
+    'slategray': (112, 128, 144),
+    'slategrey': (112, 128, 144),
+    'snow': (255, 250, 250),
+    'springgreen': (0, 255, 127),
+    'steelblue': (70, 130, 180),
+    'tan': (210, 180, 140),
+    'teal': (0, 128, 128),
+    'thistle': (216, 191, 216),
+    'tomato': (255, 99, 71),
+    'turquoise': (64, 224, 208),
+    'violet': (238, 130, 238),
+    'wheat': (245, 222, 179),
+    'white': (255, 255, 255),
+    'whitesmoke': (245, 245, 245),
+    'yellow': (255, 255, 0),
+    'yellowgreen': (154, 205, 50),
+}
+
+COLORS_BY_VALUE = {v: k for k, v in COLORS_BY_NAME.items()}

.mcp-venv/lib/python3.13/site-packages/pydantic/config.py

@@ -0,0 +1,1296 @@
+"""Configuration for Pydantic models."""
+
+from __future__ import annotations as _annotations
+
+import warnings
+from re import Pattern
+from typing import TYPE_CHECKING, Any, Callable, Literal, TypeVar, Union, cast, overload
+
+from typing_extensions import TypeAlias, TypedDict, Unpack, deprecated
+
+from ._migration import getattr_migration
+from .aliases import AliasGenerator
+from .errors import PydanticUserError
+from .warnings import PydanticDeprecatedSince211
+
+if TYPE_CHECKING:
+    from ._internal._generate_schema import GenerateSchema as _GenerateSchema
+    from .fields import ComputedFieldInfo, FieldInfo
+
+__all__ = ('ConfigDict', 'with_config')
+
+
+JsonValue: TypeAlias = Union[int, float, str, bool, None, list['JsonValue'], 'JsonDict']
+JsonDict: TypeAlias = dict[str, JsonValue]
+
+JsonEncoder = Callable[[Any], Any]
+
+JsonSchemaExtraCallable: TypeAlias = Union[
+    Callable[[JsonDict], None],
+    Callable[[JsonDict, type[Any]], None],
+]
+
+ExtraValues = Literal['allow', 'ignore', 'forbid']
+
+
+class ConfigDict(TypedDict, total=False):
+    """A TypedDict for configuring Pydantic behaviour."""
+
+    title: str | None
+    """The title for the generated JSON schema, defaults to the model's name"""
+
+    model_title_generator: Callable[[type], str] | None
+    """A callable that takes a model class and returns the title for it. Defaults to `None`."""
+
+    field_title_generator: Callable[[str, FieldInfo | ComputedFieldInfo], str] | None
+    """A callable that takes a field's name and info and returns title for it. Defaults to `None`."""
+
+    str_to_lower: bool
+    """Whether to convert all characters to lowercase for str types. Defaults to `False`."""
+
+    str_to_upper: bool
+    """Whether to convert all characters to uppercase for str types. Defaults to `False`."""
+
+    str_strip_whitespace: bool
+    """Whether to strip leading and trailing whitespace for str types."""
+
+    str_min_length: int
+    """The minimum length for str types. Defaults to `None`."""
+
+    str_max_length: int | None
+    """The maximum length for str types. Defaults to `None`."""
+
+    extra: ExtraValues | None
+    '''
+    Whether to ignore, allow, or forbid extra data during model initialization. Defaults to `'ignore'`.
+
+    Three configuration values are available:
+
+    - `'ignore'`: Providing extra data is ignored (the default):
+      ```python
+      from pydantic import BaseModel, ConfigDict
+
+      class User(BaseModel):
+          model_config = ConfigDict(extra='ignore')  # (1)!
+
+          name: str
+
+      user = User(name='John Doe', age=20)  # (2)!
+      print(user)
+      #> name='John Doe'
+      ```
+
+        1. This is the default behaviour.
+        2. The `age` argument is ignored.
+
+    - `'forbid'`: Providing extra data is not permitted, and a [`ValidationError`][pydantic_core.ValidationError]
+      will be raised if this is the case:
+      ```python
+      from pydantic import BaseModel, ConfigDict, ValidationError
+
+
+      class Model(BaseModel):
+          x: int
+
+          model_config = ConfigDict(extra='forbid')
+
+
+      try:
+          Model(x=1, y='a')
+      except ValidationError as exc:
+          print(exc)
+          """
+          1 validation error for Model
+          y
+            Extra inputs are not permitted [type=extra_forbidden, input_value='a', input_type=str]
+          """
+      ```
+
+    - `'allow'`: Providing extra data is allowed and stored in the `__pydantic_extra__` dictionary attribute:
+      ```python
+      from pydantic import BaseModel, ConfigDict
+
+
+      class Model(BaseModel):
+          x: int
+
+          model_config = ConfigDict(extra='allow')
+
+
+      m = Model(x=1, y='a')
+      assert m.__pydantic_extra__ == {'y': 'a'}
+      ```
+      By default, no validation will be applied to these extra items, but you can set a type for the values by overriding
+      the type annotation for `__pydantic_extra__`:
+      ```python
+      from pydantic import BaseModel, ConfigDict, Field, ValidationError
+
+
+      class Model(BaseModel):
+          __pydantic_extra__: dict[str, int] = Field(init=False)  # (1)!
+
+          x: int
+
+          model_config = ConfigDict(extra='allow')
+
+
+      try:
+          Model(x=1, y='a')
+      except ValidationError as exc:
+          print(exc)
+          """
+          1 validation error for Model
+          y
+            Input should be a valid integer, unable to parse string as an integer [type=int_parsing, input_value='a', input_type=str]
+          """
+
+      m = Model(x=1, y='2')
+      assert m.x == 1
+      assert m.y == 2
+      assert m.model_dump() == {'x': 1, 'y': 2}
+      assert m.__pydantic_extra__ == {'y': 2}
+      ```
+
+        1. The `= Field(init=False)` does not have any effect at runtime, but prevents the `__pydantic_extra__` field from
+           being included as a parameter to the model's `__init__` method by type checkers.
+
+    As well as specifying an `extra` configuration value on the model, you can also provide it as an argument to the validation methods.
+    This will override any `extra` configuration value set on the model:
+    ```python
+    from pydantic import BaseModel, ConfigDict, ValidationError
+
+    class Model(BaseModel):
+        x: int
+        model_config = ConfigDict(extra="allow")
+
+    try:
+        # Override model config and forbid extra fields just this time
+        Model.model_validate({"x": 1, "y": 2}, extra="forbid")
+    except ValidationError as exc:
+        print(exc)
+        """
+        1 validation error for Model
+        y
+          Extra inputs are not permitted [type=extra_forbidden, input_value=2, input_type=int]
+        """
+    ```
+    '''
+
+    frozen: bool
+    """
+    Whether models are faux-immutable, i.e. whether `__setattr__` is allowed, and also generates
+    a `__hash__()` method for the model. This makes instances of the model potentially hashable if all the
+    attributes are hashable. Defaults to `False`.
+
+    Note:
+        On V1, the inverse of this setting was called `allow_mutation`, and was `True` by default.
+    """
+
+    populate_by_name: bool
+    """
+    Whether an aliased field may be populated by its name as given by the model
+    attribute, as well as the alias. Defaults to `False`.
+
+    !!! warning
+        `populate_by_name` usage is not recommended in v2.11+ and will be deprecated in v3.
+        Instead, you should use the [`validate_by_name`][pydantic.config.ConfigDict.validate_by_name] configuration setting.
+
+        When `validate_by_name=True` and `validate_by_alias=True`, this is strictly equivalent to the
+        previous behavior of `populate_by_name=True`.
+
+        In v2.11, we also introduced a [`validate_by_alias`][pydantic.config.ConfigDict.validate_by_alias] setting that introduces more fine grained
+        control for validation behavior.
+
+        Here's how you might go about using the new settings to achieve the same behavior:
+
+        ```python
+        from pydantic import BaseModel, ConfigDict, Field
+
+        class Model(BaseModel):
+            model_config = ConfigDict(validate_by_name=True, validate_by_alias=True)
+
+            my_field: str = Field(alias='my_alias')  # (1)!
+
+        m = Model(my_alias='foo')  # (2)!
+        print(m)
+        #> my_field='foo'
+
+        m = Model(my_field='foo')  # (3)!
+        print(m)
+        #> my_field='foo'
+        ```
+
+        1. The field `'my_field'` has an alias `'my_alias'`.
+        2. The model is populated by the alias `'my_alias'`.
+        3. The model is populated by the attribute name `'my_field'`.
+    """
+
+    use_enum_values: bool
+    """
+    Whether to populate models with the `value` property of enums, rather than the raw enum.
+    This may be useful if you want to serialize `model.model_dump()` later. Defaults to `False`.
+
+    !!! note
+        If you have an `Optional[Enum]` value that you set a default for, you need to use `validate_default=True`
+        for said Field to ensure that the `use_enum_values` flag takes effect on the default, as extracting an
+        enum's value occurs during validation, not serialization.
+
+    ```python
+    from enum import Enum
+    from typing import Optional
+
+    from pydantic import BaseModel, ConfigDict, Field
+
+    class SomeEnum(Enum):
+        FOO = 'foo'
+        BAR = 'bar'
+        BAZ = 'baz'
+
+    class SomeModel(BaseModel):
+        model_config = ConfigDict(use_enum_values=True)
+
+        some_enum: SomeEnum
+        another_enum: Optional[SomeEnum] = Field(
+            default=SomeEnum.FOO, validate_default=True
+        )
+
+    model1 = SomeModel(some_enum=SomeEnum.BAR)
+    print(model1.model_dump())
+    #> {'some_enum': 'bar', 'another_enum': 'foo'}
+
+    model2 = SomeModel(some_enum=SomeEnum.BAR, another_enum=SomeEnum.BAZ)
+    print(model2.model_dump())
+    #> {'some_enum': 'bar', 'another_enum': 'baz'}
+    ```
+    """
+
+    validate_assignment: bool
+    """
+    Whether to validate the data when the model is changed. Defaults to `False`.
+
+    The default behavior of Pydantic is to validate the data when the model is created.
+
+    In case the user changes the data after the model is created, the model is _not_ revalidated.
+
+    ```python
+    from pydantic import BaseModel
+
+    class User(BaseModel):
+        name: str
+
+    user = User(name='John Doe')  # (1)!
+    print(user)
+    #> name='John Doe'
+    user.name = 123  # (1)!
+    print(user)
+    #> name=123
+    ```
+
+    1. The validation happens only when the model is created.
+    2. The validation does not happen when the data is changed.
+
+    In case you want to revalidate the model when the data is changed, you can use `validate_assignment=True`:
+
+    ```python
+    from pydantic import BaseModel, ValidationError
+
+    class User(BaseModel, validate_assignment=True):  # (1)!
+        name: str
+
+    user = User(name='John Doe')  # (2)!
+    print(user)
+    #> name='John Doe'
+    try:
+        user.name = 123  # (3)!
+    except ValidationError as e:
+        print(e)
+        '''
+        1 validation error for User
+        name
+          Input should be a valid string [type=string_type, input_value=123, input_type=int]
+        '''
+    ```
+
+    1. You can either use class keyword arguments, or `model_config` to set `validate_assignment=True`.
+    2. The validation happens when the model is created.
+    3. The validation _also_ happens when the data is changed.
+    """
+
+    arbitrary_types_allowed: bool
+    """
+    Whether arbitrary types are allowed for field types. Defaults to `False`.
+
+    ```python
+    from pydantic import BaseModel, ConfigDict, ValidationError
+
+    # This is not a pydantic model, it's an arbitrary class
+    class Pet:
+        def __init__(self, name: str):
+            self.name = name
+
+    class Model(BaseModel):
+        model_config = ConfigDict(arbitrary_types_allowed=True)
+
+        pet: Pet
+        owner: str
+
+    pet = Pet(name='Hedwig')
+    # A simple check of instance type is used to validate the data
+    model = Model(owner='Harry', pet=pet)
+    print(model)
+    #> pet=<__main__.Pet object at 0x0123456789ab> owner='Harry'
+    print(model.pet)
+    #> <__main__.Pet object at 0x0123456789ab>
+    print(model.pet.name)
+    #> Hedwig
+    print(type(model.pet))
+    #> <class '__main__.Pet'>
+    try:
+        # If the value is not an instance of the type, it's invalid
+        Model(owner='Harry', pet='Hedwig')
+    except ValidationError as e:
+        print(e)
+        '''
+        1 validation error for Model
+        pet
+          Input should be an instance of Pet [type=is_instance_of, input_value='Hedwig', input_type=str]
+        '''
+
+    # Nothing in the instance of the arbitrary type is checked
+    # Here name probably should have been a str, but it's not validated
+    pet2 = Pet(name=42)
+    model2 = Model(owner='Harry', pet=pet2)
+    print(model2)
+    #> pet=<__main__.Pet object at 0x0123456789ab> owner='Harry'
+    print(model2.pet)
+    #> <__main__.Pet object at 0x0123456789ab>
+    print(model2.pet.name)
+    #> 42
+    print(type(model2.pet))
+    #> <class '__main__.Pet'>
+    ```
+    """
+
+    from_attributes: bool
+    """
+    Whether to build models and look up discriminators of tagged unions using python object attributes.
+    """
+
+    loc_by_alias: bool
+    """Whether to use the actual key provided in the data (e.g. alias) for error `loc`s rather than the field's name. Defaults to `True`."""
+
+    alias_generator: Callable[[str], str] | AliasGenerator | None
+    """
+    A callable that takes a field name and returns an alias for it
+    or an instance of [`AliasGenerator`][pydantic.aliases.AliasGenerator]. Defaults to `None`.
+
+    When using a callable, the alias generator is used for both validation and serialization.
+    If you want to use different alias generators for validation and serialization, you can use
+    [`AliasGenerator`][pydantic.aliases.AliasGenerator] instead.
+
+    If data source field names do not match your code style (e.g. CamelCase fields),
+    you can automatically generate aliases using `alias_generator`. Here's an example with
+    a basic callable:
+
+    ```python
+    from pydantic import BaseModel, ConfigDict
+    from pydantic.alias_generators import to_pascal
+
+    class Voice(BaseModel):
+        model_config = ConfigDict(alias_generator=to_pascal)
+
+        name: str
+        language_code: str
+
+    voice = Voice(Name='Filiz', LanguageCode='tr-TR')
+    print(voice.language_code)
+    #> tr-TR
+    print(voice.model_dump(by_alias=True))
+    #> {'Name': 'Filiz', 'LanguageCode': 'tr-TR'}
+    ```
+
+    If you want to use different alias generators for validation and serialization, you can use
+    [`AliasGenerator`][pydantic.aliases.AliasGenerator].
+
+    ```python
+    from pydantic import AliasGenerator, BaseModel, ConfigDict
+    from pydantic.alias_generators import to_camel, to_pascal
+
+    class Athlete(BaseModel):
+        first_name: str
+        last_name: str
+        sport: str
+
+        model_config = ConfigDict(
+            alias_generator=AliasGenerator(
+                validation_alias=to_camel,
+                serialization_alias=to_pascal,
+            )
+        )
+
+    athlete = Athlete(firstName='John', lastName='Doe', sport='track')
+    print(athlete.model_dump(by_alias=True))
+    #> {'FirstName': 'John', 'LastName': 'Doe', 'Sport': 'track'}
+    ```
+
+    Note:
+        Pydantic offers three built-in alias generators: [`to_pascal`][pydantic.alias_generators.to_pascal],
+        [`to_camel`][pydantic.alias_generators.to_camel], and [`to_snake`][pydantic.alias_generators.to_snake].
+    """
+
+    ignored_types: tuple[type, ...]
+    """A tuple of types that may occur as values of class attributes without annotations. This is
+    typically used for custom descriptors (classes that behave like `property`). If an attribute is set on a
+    class without an annotation and has a type that is not in this tuple (or otherwise recognized by
+    _pydantic_), an error will be raised. Defaults to `()`.
+    """
+
+    allow_inf_nan: bool
+    """Whether to allow infinity (`+inf` an `-inf`) and NaN values to float and decimal fields. Defaults to `True`."""
+
+    json_schema_extra: JsonDict | JsonSchemaExtraCallable | None
+    """A dict or callable to provide extra JSON schema properties. Defaults to `None`."""
+
+    json_encoders: dict[type[object], JsonEncoder] | None
+    """
+    A `dict` of custom JSON encoders for specific types. Defaults to `None`.
+
+    /// version-deprecated | v2
+    This configuration option is a carryover from v1. We originally planned to remove it in v2 but didn't have a 1:1 replacement
+    so we are keeping it for now. It is still deprecated and will likely be removed in the future.
+    ///
+    """
+
+    # new in V2
+    strict: bool
+    """
+    Whether strict validation is applied to all fields on the model.
+
+    By default, Pydantic attempts to coerce values to the correct type, when possible.
+
+    There are situations in which you may want to disable this behavior, and instead raise an error if a value's type
+    does not match the field's type annotation.
+
+    To configure strict mode for all fields on a model, you can set `strict=True` on the model.
+
+    ```python
+    from pydantic import BaseModel, ConfigDict
+
+    class Model(BaseModel):
+        model_config = ConfigDict(strict=True)
+
+        name: str
+        age: int
+    ```
+
+    See [Strict Mode](../concepts/strict_mode.md) for more details.
+
+    See the [Conversion Table](../concepts/conversion_table.md) for more details on how Pydantic converts data in both
+    strict and lax modes.
+
+    /// version-added | v2
+    ///
+    """
+    # whether instances of models and dataclasses (including subclass instances) should re-validate, default 'never'
+    revalidate_instances: Literal['always', 'never', 'subclass-instances']
+    """
+    When and how to revalidate models and dataclasses during validation. Can be one of:
+
+    - `'never'`: will *not* revalidate models and dataclasses during validation
+    - `'always'`: will revalidate models and dataclasses during validation
+    - `'subclass-instances'`: will revalidate models and dataclasses during validation if the instance is a
+        subclass of the model or dataclass
+
+    The default is `'never'` (no revalidation).
+
+    This configuration only affects *the current model* it is applied on, and does *not* propagate to the models
+    referenced in fields.
+
+    ```python
+    from pydantic import BaseModel
+
+    class User(BaseModel, revalidate_instances='never'):  # (1)!
+        name: str
+
+    class Transaction(BaseModel):
+        user: User
+
+    my_user = User(name='John')
+    t = Transaction(user=my_user)
+
+    my_user.name = 1  # (2)!
+    t = Transaction(user=my_user)  # (3)!
+    print(t)
+    #> user=User(name=1)
+    ```
+
+    1. This is the default behavior.
+    2. The assignment is *not* validated, unless you set [`validate_assignment`][pydantic.ConfigDict.validate_assignment] in the configuration.
+    3. Since `revalidate_instances` is set to `'never'`, the user instance is not revalidated.
+
+    Here is an example demonstrating the behavior of `'subclass-instances'`:
+
+    ```python
+    from pydantic import BaseModel
+
+    class User(BaseModel, revalidate_instances='subclass-instances'):
+        name: str
+
+    class SubUser(User):
+        age: int
+
+    class Transaction(BaseModel):
+        user: User
+
+    my_user = User(name='John')
+    my_user.name = 1  # (1)!
+    t = Transaction(user=my_user)  # (2)!
+    print(t)
+    #> user=User(name=1)
+
+    my_sub_user = SubUser(name='John', age=20)
+    t = Transaction(user=my_sub_user)
+    print(t)  # (3)!
+    #> user=User(name='John')
+    ```
+
+    1. The assignment is *not* validated, unless you set [`validate_assignment`][pydantic.ConfigDict.validate_assignment] in the configuration.
+    2. Because `my_user` is a "direct" instance of `User`, it is *not* being revalidated. It would have been the case if
+      `revalidate_instances` was set to `'always'`.
+    3. Because `my_sub_user` is an instance of a `User` subclass, it is being revalidated. In this case, Pydantic coerces `my_sub_user` to the defined
+       `User` class defined on `Transaction`. If one of its fields had an invalid value, a validation error would have been raised.
+
+    /// version-added | v2
+    ///
+    """
+
+    ser_json_timedelta: Literal['iso8601', 'float']
+    """
+    The format of JSON serialized timedeltas. Accepts the string values of `'iso8601'` and
+    `'float'`. Defaults to `'iso8601'`.
+
+    - `'iso8601'` will serialize timedeltas to [ISO 8601 text format](https://en.wikipedia.org/wiki/ISO_8601#Durations).
+    - `'float'` will serialize timedeltas to the total number of seconds.
+
+    /// version-changed | v2.12
+    It is now recommended to use the [`ser_json_temporal`][pydantic.config.ConfigDict.ser_json_temporal]
+    setting. `ser_json_timedelta` will be deprecated in v3.
+    ///
+    """
+
+    ser_json_temporal: Literal['iso8601', 'seconds', 'milliseconds']
+    """
+    The format of JSON serialized temporal types from the [`datetime`][] module. This includes:
+
+    - [`datetime.datetime`][]
+    - [`datetime.date`][]
+    - [`datetime.time`][]
+    - [`datetime.timedelta`][]
+
+    Can be one of:
+
+    - `'iso8601'` will serialize date-like types to [ISO 8601 text format](https://en.wikipedia.org/wiki/ISO_8601#Durations).
+    - `'milliseconds'` will serialize date-like types to a floating point number of milliseconds since the epoch.
+    - `'seconds'` will serialize date-like types to a floating point number of seconds since the epoch.
+
+    Defaults to `'iso8601'`.
+
+    /// version-added | v2.12
+    This setting replaces [`ser_json_timedelta`][pydantic.config.ConfigDict.ser_json_timedelta],
+    which will be deprecated in v3. `ser_json_temporal` adds more configurability for the other temporal types.
+    ///
+    """
+
+    val_temporal_unit: Literal['seconds', 'milliseconds', 'infer']
+    """
+    The unit to assume for validating numeric input for datetime-like types ([`datetime.datetime`][] and [`datetime.date`][]). Can be one of:
+
+    - `'seconds'` will validate date or time numeric inputs as seconds since the [epoch].
+    - `'milliseconds'` will validate date or time numeric inputs as milliseconds since the [epoch].
+    - `'infer'` will infer the unit from the string numeric input on unix time as:
+
+        * seconds since the [epoch] if $-2^{10} <= v <= 2^{10}$
+        * milliseconds since the [epoch] (if $v < -2^{10}$ or $v > 2^{10}$).
+
+    Defaults to `'infer'`.
+
+    /// version-added | v2.12
+    ///
+
+    [epoch]: https://en.wikipedia.org/wiki/Unix_time
+    """
+
+    ser_json_bytes: Literal['utf8', 'base64', 'hex']
+    """
+    The encoding of JSON serialized bytes. Defaults to `'utf8'`.
+    Set equal to `val_json_bytes` to get back an equal value after serialization round trip.
+
+    - `'utf8'` will serialize bytes to UTF-8 strings.
+    - `'base64'` will serialize bytes to URL safe base64 strings.
+    - `'hex'` will serialize bytes to hexadecimal strings.
+    """
+
+    val_json_bytes: Literal['utf8', 'base64', 'hex']
+    """
+    /// version-added | v2.9
+    ///
+
+    The encoding of JSON serialized bytes to decode. Defaults to `'utf8'`.
+    Set equal to `ser_json_bytes` to get back an equal value after serialization round trip.
+
+    - `'utf8'` will deserialize UTF-8 strings to bytes.
+    - `'base64'` will deserialize URL safe base64 strings to bytes.
+    - `'hex'` will deserialize hexadecimal strings to bytes.
+    """
+
+    ser_json_inf_nan: Literal['null', 'constants', 'strings']
+    """
+    The encoding of JSON serialized infinity and NaN float values. Defaults to `'null'`.
+
+    - `'null'` will serialize infinity and NaN values as `null`.
+    - `'constants'` will serialize infinity and NaN values as `Infinity` and `NaN`.
+    - `'strings'` will serialize infinity as string `"Infinity"` and NaN as string `"NaN"`.
+    """
+
+    # whether to validate default values during validation, default False
+    validate_default: bool
+    """Whether to validate default values during validation. Defaults to `False`."""
+
+    validate_return: bool
+    """Whether to validate the return value from call validators. Defaults to `False`."""
+
+    protected_namespaces: tuple[str | Pattern[str], ...]
+    """
+    A tuple of strings and/or regex patterns that prevent models from having fields with names that conflict with its existing members/methods.
+
+    Strings are matched on a prefix basis. For instance, with `'dog'`, having a field named `'dog_name'` will be disallowed.
+
+    Regex patterns are matched on the entire field name. For instance, with the pattern `'^dog$'`, having a field named `'dog'` will be disallowed,
+    but `'dog_name'` will be accepted.
+
+    Defaults to `('model_validate', 'model_dump')`. This default is used to prevent collisions with the existing (and possibly future)
+    [validation](../concepts/models.md#validating-data) and [serialization](../concepts/serialization.md#serializing-data) methods.
+
+    ```python
+    import warnings
+
+    from pydantic import BaseModel
+
+    warnings.filterwarnings('error')  # Raise warnings as errors
+
+    try:
+
+        class Model(BaseModel):
+            model_dump_something: str
+
+    except UserWarning as e:
+        print(e)
+        '''
+        Field 'model_dump_something' in 'Model' conflicts with protected namespace 'model_dump'.
+
+        You may be able to solve this by setting the 'protected_namespaces' configuration to ('model_validate',).
+        '''
+    ```
+
+    You can customize this behavior using the `protected_namespaces` setting:
+
+    ```python {test="skip"}
+    import re
+    import warnings
+
+    from pydantic import BaseModel, ConfigDict
+
+    with warnings.catch_warnings(record=True) as caught_warnings:
+        warnings.simplefilter('always')  # Catch all warnings
+
+        class Model(BaseModel):
+            safe_field: str
+            also_protect_field: str
+            protect_this: str
+
+            model_config = ConfigDict(
+                protected_namespaces=(
+                    'protect_me_',
+                    'also_protect_',
+                    re.compile('^protect_this$'),
+                )
+            )
+
+    for warning in caught_warnings:
+        print(f'{warning.message}')
+        '''
+        Field 'also_protect_field' in 'Model' conflicts with protected namespace 'also_protect_'.
+        You may be able to solve this by setting the 'protected_namespaces' configuration to ('protect_me_', re.compile('^protect_this$'))`.
+
+        Field 'protect_this' in 'Model' conflicts with protected namespace 're.compile('^protect_this$')'.
+        You may be able to solve this by setting the 'protected_namespaces' configuration to ('protect_me_', 'also_protect_')`.
+        '''
+    ```
+
+    While Pydantic will only emit a warning when an item is in a protected namespace but does not actually have a collision,
+    an error _is_ raised if there is an actual collision with an existing attribute:
+
+    ```python
+    from pydantic import BaseModel, ConfigDict
+
+    try:
+
+        class Model(BaseModel):
+            model_validate: str
+
+            model_config = ConfigDict(protected_namespaces=('model_',))
+
+    except ValueError as e:
+        print(e)
+        '''
+        Field 'model_validate' conflicts with member <bound method BaseModel.model_validate of <class 'pydantic.main.BaseModel'>> of protected namespace 'model_'.
+        '''
+    ```
+
+    /// version-changed | v2.10
+    The default protected namespaces was changed from `('model_',)` to `('model_validate', 'model_dump')`, to allow
+    for fields like `model_id`, `model_name` to be used.
+    ///
+    """
+
+    hide_input_in_errors: bool
+    """
+    Whether to hide inputs when printing errors. Defaults to `False`.
+
+    Pydantic shows the input value and type when it raises `ValidationError` during the validation.
+
+    ```python
+    from pydantic import BaseModel, ValidationError
+
+    class Model(BaseModel):
+        a: str
+
+    try:
+        Model(a=123)
+    except ValidationError as e:
+        print(e)
+        '''
+        1 validation error for Model
+        a
+          Input should be a valid string [type=string_type, input_value=123, input_type=int]
+        '''
+    ```
+
+    You can hide the input value and type by setting the `hide_input_in_errors` config to `True`.
+
+    ```python
+    from pydantic import BaseModel, ConfigDict, ValidationError
+
+    class Model(BaseModel):
+        a: str
+        model_config = ConfigDict(hide_input_in_errors=True)
+
+    try:
+        Model(a=123)
+    except ValidationError as e:
+        print(e)
+        '''
+        1 validation error for Model
+        a
+          Input should be a valid string [type=string_type]
+        '''
+    ```
+    """
+
+    defer_build: bool
+    """
+    Whether to defer model validator and serializer construction until the first model validation. Defaults to False.
+
+    This can be useful to avoid the overhead of building models which are only
+    used nested within other models, or when you want to manually define type namespace via
+    [`Model.model_rebuild(_types_namespace=...)`][pydantic.BaseModel.model_rebuild].
+
+    /// version-changed | v2.10
+    The setting also applies to [Pydantic dataclasses](../concepts/dataclasses.md) and [type adapters](../concepts/type_adapter.md).
+    ///
+    """
+
+    plugin_settings: dict[str, object] | None
+    """A `dict` of settings for plugins. Defaults to `None`."""
+
+    schema_generator: type[_GenerateSchema] | None
+    """
+    The `GenerateSchema` class to use during core schema generation.
+
+    /// version-deprecated | v2.10
+    The `GenerateSchema` class is private and highly subject to change.
+    ///
+    """
+
+    json_schema_serialization_defaults_required: bool
+    """
+    Whether fields with default values should be marked as required in the serialization schema. Defaults to `False`.
+
+    This ensures that the serialization schema will reflect the fact a field with a default will always be present
+    when serializing the model, even though it is not required for validation.
+
+    However, there are scenarios where this may be undesirable — in particular, if you want to share the schema
+    between validation and serialization, and don't mind fields with defaults being marked as not required during
+    serialization. See [#7209](https://github.com/pydantic/pydantic/issues/7209) for more details.
+
+    ```python
+    from pydantic import BaseModel, ConfigDict
+
+    class Model(BaseModel):
+        a: str = 'a'
+
+        model_config = ConfigDict(json_schema_serialization_defaults_required=True)
+
+    print(Model.model_json_schema(mode='validation'))
+    '''
+    {
+        'properties': {'a': {'default': 'a', 'title': 'A', 'type': 'string'}},
+        'title': 'Model',
+        'type': 'object',
+    }
+    '''
+    print(Model.model_json_schema(mode='serialization'))
+    '''
+    {
+        'properties': {'a': {'default': 'a', 'title': 'A', 'type': 'string'}},
+        'required': ['a'],
+        'title': 'Model',
+        'type': 'object',
+    }
+    '''
+    ```
+
+    /// version-added | v2.4
+    ///
+    """
+
+    json_schema_mode_override: Literal['validation', 'serialization', None]
+    """
+    If not `None`, the specified mode will be used to generate the JSON schema regardless of what `mode` was passed to
+    the function call. Defaults to `None`.
+
+    This provides a way to force the JSON schema generation to reflect a specific mode, e.g., to always use the
+    validation schema.
+
+    It can be useful when using frameworks (such as FastAPI) that may generate different schemas for validation
+    and serialization that must both be referenced from the same schema; when this happens, we automatically append
+    `-Input` to the definition reference for the validation schema and `-Output` to the definition reference for the
+    serialization schema. By specifying a `json_schema_mode_override` though, this prevents the conflict between
+    the validation and serialization schemas (since both will use the specified schema), and so prevents the suffixes
+    from being added to the definition references.
+
+    ```python
+    from pydantic import BaseModel, ConfigDict, Json
+
+    class Model(BaseModel):
+        a: Json[int]  # requires a string to validate, but will dump an int
+
+    print(Model.model_json_schema(mode='serialization'))
+    '''
+    {
+        'properties': {'a': {'title': 'A', 'type': 'integer'}},
+        'required': ['a'],
+        'title': 'Model',
+        'type': 'object',
+    }
+    '''
+
+    class ForceInputModel(Model):
+        # the following ensures that even with mode='serialization', we
+        # will get the schema that would be generated for validation.
+        model_config = ConfigDict(json_schema_mode_override='validation')
+
+    print(ForceInputModel.model_json_schema(mode='serialization'))
+    '''
+    {
+        'properties': {
+            'a': {
+                'contentMediaType': 'application/json',
+                'contentSchema': {'type': 'integer'},
+                'title': 'A',
+                'type': 'string',
+            }
+        },
+        'required': ['a'],
+        'title': 'ForceInputModel',
+        'type': 'object',
+    }
+    '''
+    ```
+
+    /// version-added | v2.4
+    ///
+    """
+
+    coerce_numbers_to_str: bool
+    """
+    If `True`, enables automatic coercion of any `Number` type to `str` in "lax" (non-strict) mode. Defaults to `False`.
+
+    Pydantic doesn't allow number types (`int`, `float`, `Decimal`) to be coerced as type `str` by default.
+
+    ```python
+    from decimal import Decimal
+
+    from pydantic import BaseModel, ConfigDict, ValidationError
+
+    class Model(BaseModel):
+        value: str
+
+    try:
+        print(Model(value=42))
+    except ValidationError as e:
+        print(e)
+        '''
+        1 validation error for Model
+        value
+          Input should be a valid string [type=string_type, input_value=42, input_type=int]
+        '''
+
+    class Model(BaseModel):
+        model_config = ConfigDict(coerce_numbers_to_str=True)
+
+        value: str
+
+    repr(Model(value=42).value)
+    #> "42"
+    repr(Model(value=42.13).value)
+    #> "42.13"
+    repr(Model(value=Decimal('42.13')).value)
+    #> "42.13"
+    ```
+    """
+
+    regex_engine: Literal['rust-regex', 'python-re']
+    """
+    The regex engine to be used for pattern validation.
+    Defaults to `'rust-regex'`.
+
+    - `'rust-regex'` uses the [`regex`](https://docs.rs/regex) Rust crate,
+      which is non-backtracking and therefore more DDoS resistant, but does not support all regex features.
+    - `'python-re'` use the [`re`][] module, which supports all regex features, but may be slower.
+
+    !!! note
+        If you use a compiled regex pattern, the `'python-re'` engine will be used regardless of this setting.
+        This is so that flags such as [`re.IGNORECASE`][] are respected.
+
+    ```python
+    from pydantic import BaseModel, ConfigDict, Field, ValidationError
+
+    class Model(BaseModel):
+        model_config = ConfigDict(regex_engine='python-re')
+
+        value: str = Field(pattern=r'^abc(?=def)')
+
+    print(Model(value='abcdef').value)
+    #> abcdef
+
+    try:
+        print(Model(value='abxyzcdef'))
+    except ValidationError as e:
+        print(e)
+        '''
+        1 validation error for Model
+        value
+          String should match pattern '^abc(?=def)' [type=string_pattern_mismatch, input_value='abxyzcdef', input_type=str]
+        '''
+    ```
+
+    /// version-added | v2.5
+    ///
+    """
+
+    validation_error_cause: bool
+    """
+    If `True`, Python exceptions that were part of a validation failure will be shown as an exception group as a cause. Can be useful for debugging. Defaults to `False`.
+
+    Note:
+        Python 3.10 and older don't support exception groups natively. <=3.10, backport must be installed: `pip install exceptiongroup`.
+
+    Note:
+        The structure of validation errors are likely to change in future Pydantic versions. Pydantic offers no guarantees about their structure. Should be used for visual traceback debugging only.
+
+    /// version-added | v2.5
+    ///
+    """
+
+    use_attribute_docstrings: bool
+    '''
+    Whether docstrings of attributes (bare string literals immediately following the attribute declaration)
+    should be used for field descriptions. Defaults to `False`.
+
+    ```python
+    from pydantic import BaseModel, ConfigDict, Field
+
+
+    class Model(BaseModel):
+        model_config = ConfigDict(use_attribute_docstrings=True)
+
+        x: str
+        """
+        Example of an attribute docstring
+        """
+
+        y: int = Field(description="Description in Field")
+        """
+        Description in Field overrides attribute docstring
+        """
+
+
+    print(Model.model_fields["x"].description)
+    # > Example of an attribute docstring
+    print(Model.model_fields["y"].description)
+    # > Description in Field
+    ```
+    This requires the source code of the class to be available at runtime (and so won't work in the interactive interpreter shell).
+
+    !!! warning "Usage with `TypedDict` and stdlib dataclasses"
+        Due to current limitations, attribute docstrings detection may not work as expected when using
+        [`TypedDict`][typing.TypedDict] and stdlib dataclasses, in particular when:
+
+        - inheritance is being used.
+        - multiple classes have the same name in the same source file (unless Python 3.13 or greater is used).
+
+    /// version-added | v2.7
+    ///
+    '''
+
+    cache_strings: bool | Literal['all', 'keys', 'none']
+    """
+    Whether to cache strings to avoid constructing new Python objects. Defaults to True.
+
+    Enabling this setting should significantly improve validation performance while increasing memory usage slightly.
+
+    - `True` or `'all'` (the default): cache all strings
+    - `'keys'`: cache only dictionary keys
+    - `False` or `'none'`: no caching
+
+    !!! note
+        `True` or `'all'` is required to cache strings during general validation because
+        validators don't know if they're in a key or a value.
+
+    !!! tip
+        If repeated strings are rare, it's recommended to use `'keys'` or `'none'` to reduce memory usage,
+        as the performance difference is minimal if repeated strings are rare.
+
+    /// version-added | v2.7
+    ///
+    """
+
+    validate_by_alias: bool
+    """
+    Whether an aliased field may be populated by its alias. Defaults to `True`.
+
+    Here's an example of disabling validation by alias:
+
+    ```py
+    from pydantic import BaseModel, ConfigDict, Field
+
+    class Model(BaseModel):
+        model_config = ConfigDict(validate_by_name=True, validate_by_alias=False)
+
+        my_field: str = Field(validation_alias='my_alias')  # (1)!
+
+    m = Model(my_field='foo')  # (2)!
+    print(m)
+    #> my_field='foo'
+    ```
+
+    1. The field `'my_field'` has an alias `'my_alias'`.
+    2. The model can only be populated by the attribute name `'my_field'`.
+
+    !!! warning
+        You cannot set both `validate_by_alias` and `validate_by_name` to `False`.
+        This would make it impossible to populate an attribute.
+
+        See [usage errors](../errors/usage_errors.md#validate-by-alias-and-name-false) for an example.
+
+        If you set `validate_by_alias` to `False`, under the hood, Pydantic dynamically sets
+        `validate_by_name` to `True` to ensure that validation can still occur.
+
+    /// version-added | v2.11
+    This setting was introduced in conjunction with [`validate_by_name`][pydantic.ConfigDict.validate_by_name]
+    to empower users with more fine grained validation control.
+    ///
+    """
+
+    validate_by_name: bool
+    """
+    Whether an aliased field may be populated by its name as given by the model
+    attribute. Defaults to `False`.
+
+    ```python
+    from pydantic import BaseModel, ConfigDict, Field
+
+    class Model(BaseModel):
+        model_config = ConfigDict(validate_by_name=True, validate_by_alias=True)
+
+        my_field: str = Field(validation_alias='my_alias')  # (1)!
+
+    m = Model(my_alias='foo')  # (2)!
+    print(m)
+    #> my_field='foo'
+
+    m = Model(my_field='foo')  # (3)!
+    print(m)
+    #> my_field='foo'
+    ```
+
+    1. The field `'my_field'` has an alias `'my_alias'`.
+    2. The model is populated by the alias `'my_alias'`.
+    3. The model is populated by the attribute name `'my_field'`.
+
+    !!! warning
+        You cannot set both `validate_by_alias` and `validate_by_name` to `False`.
+        This would make it impossible to populate an attribute.
+
+        See [usage errors](../errors/usage_errors.md#validate-by-alias-and-name-false) for an example.
+
+    /// version-added | v2.11
+    This setting was introduced in conjunction with [`validate_by_alias`][pydantic.ConfigDict.validate_by_alias]
+    to empower users with more fine grained validation control. It is an alternative to [`populate_by_name`][pydantic.ConfigDict.populate_by_name],
+    that enables validation by name **and** by alias.
+    ///
+    """
+
+    serialize_by_alias: bool
+    """
+    Whether an aliased field should be serialized by its alias. Defaults to `False`.
+
+    Note: In v2.11, `serialize_by_alias` was introduced to address the
+    [popular request](https://github.com/pydantic/pydantic/issues/8379)
+    for consistency with alias behavior for validation and serialization settings.
+    In v3, the default value is expected to change to `True` for consistency with the validation default.
+
+    ```python
+    from pydantic import BaseModel, ConfigDict, Field
+
+    class Model(BaseModel):
+        model_config = ConfigDict(serialize_by_alias=True)
+
+        my_field: str = Field(serialization_alias='my_alias')  # (1)!
+
+    m = Model(my_field='foo')
+    print(m.model_dump())  # (2)!
+    #> {'my_alias': 'foo'}
+    ```
+
+    1. The field `'my_field'` has an alias `'my_alias'`.
+    2. The model is serialized using the alias `'my_alias'` for the `'my_field'` attribute.
+
+
+    /// version-added | v2.11
+    This setting was introduced to address the [popular request](https://github.com/pydantic/pydantic/issues/8379)
+    for consistency with alias behavior for validation and serialization.
+
+    In v3, the default value is expected to change to `True` for consistency with the validation default.
+    ///
+    """
+
+    url_preserve_empty_path: bool
+    """
+    Whether to preserve empty URL paths when validating values for a URL type. Defaults to `False`.
+
+    ```python
+    from pydantic import AnyUrl, BaseModel, ConfigDict
+
+    class Model(BaseModel):
+        model_config = ConfigDict(url_preserve_empty_path=True)
+
+        url: AnyUrl
+
+    m = Model(url='http://example.com')
+    print(m.url)
+    #> http://example.com
+    ```
+
+    /// version-added | v2.12
+    ///
+    """
+
+    polymorphic_serialization: bool
+    """
+    Whether to use polymorphic serialization for subclasses of the model or Pydantic dataclass. Defaults to `False`.
+    """
+
+
+_TypeT = TypeVar('_TypeT', bound=type)
+
+
+@overload
+@deprecated('Passing `config` as a keyword argument is deprecated. Pass `config` as a positional argument instead.')
+def with_config(*, config: ConfigDict) -> Callable[[_TypeT], _TypeT]: ...
+
+
+@overload
+def with_config(config: ConfigDict, /) -> Callable[[_TypeT], _TypeT]: ...
+
+
+@overload
+def with_config(**config: Unpack[ConfigDict]) -> Callable[[_TypeT], _TypeT]: ...
+
+
+def with_config(config: ConfigDict | None = None, /, **kwargs: Any) -> Callable[[_TypeT], _TypeT]:
+    """!!! abstract "Usage Documentation"
+        [Configuration with other types](../concepts/config.md#configuration-on-other-supported-types)
+
+    A convenience decorator to set a [Pydantic configuration](config.md) on a `TypedDict` or a `dataclass` from the standard library.
+
+    Although the configuration can be set using the `__pydantic_config__` attribute, it does not play well with type checkers,
+    especially with `TypedDict`.
+
+    !!! example "Usage"
+
+        ```python
+        from typing_extensions import TypedDict
+
+        from pydantic import ConfigDict, TypeAdapter, with_config
+
+        @with_config(ConfigDict(str_to_lower=True))
+        class TD(TypedDict):
+            x: str
+
+        ta = TypeAdapter(TD)
+
+        print(ta.validate_python({'x': 'ABC'}))
+        #> {'x': 'abc'}
+        ```
+
+    /// deprecated-removed | v2.11 v3
+    Passing `config` as a keyword argument.
+    ///
+
+    /// version-changed | v2.11
+    Keyword arguments can be provided directly instead of a config dictionary.
+    ///
+    """
+    if config is not None and kwargs:
+        raise ValueError('Cannot specify both `config` and keyword arguments')
+
+    if len(kwargs) == 1 and (kwargs_conf := kwargs.get('config')) is not None:
+        warnings.warn(
+            'Passing `config` as a keyword argument is deprecated. Pass `config` as a positional argument instead',
+            category=PydanticDeprecatedSince211,
+            stacklevel=2,
+        )
+        final_config = cast(ConfigDict, kwargs_conf)
+    else:
+        final_config = config if config is not None else cast(ConfigDict, kwargs)
+
+    def inner(class_: _TypeT, /) -> _TypeT:
+        # Ideally, we would check for `class_` to either be a `TypedDict` or a stdlib dataclass.
+        # However, the `@with_config` decorator can be applied *after* `@dataclass`. To avoid
+        # common mistakes, we at least check for `class_` to not be a Pydantic model.
+        from ._internal._utils import is_model_class
+
+        if is_model_class(class_):
+            raise PydanticUserError(
+                f'Cannot use `with_config` on {class_.__name__} as it is a Pydantic model',
+                code='with-config-on-model',
+            )
+        class_.__pydantic_config__ = final_config
+        return class_
+
+    return inner
+
+
+__getattr__ = getattr_migration(__name__)

.mcp-venv/lib/python3.13/site-packages/pydantic/dataclasses.py

@@ -0,0 +1,413 @@
+"""Provide an enhanced dataclass that performs validation."""
+
+from __future__ import annotations as _annotations
+
+import dataclasses
+import functools
+import sys
+import types
+from typing import TYPE_CHECKING, Any, Callable, Generic, Literal, NoReturn, TypeVar, overload
+from warnings import warn
+
+from typing_extensions import TypeGuard, dataclass_transform
+
+from ._internal import _config, _decorators, _mock_val_ser, _namespace_utils, _typing_extra
+from ._internal import _dataclasses as _pydantic_dataclasses
+from ._migration import getattr_migration
+from .config import ConfigDict
+from .errors import PydanticUserError
+from .fields import Field, FieldInfo, PrivateAttr
+
+if TYPE_CHECKING:
+    from ._internal._dataclasses import PydanticDataclass
+    from ._internal._namespace_utils import MappingNamespace
+
+__all__ = 'dataclass', 'rebuild_dataclass'
+
+_T = TypeVar('_T')
+
+if sys.version_info >= (3, 10):
+
+    @dataclass_transform(field_specifiers=(dataclasses.field, Field, PrivateAttr))
+    @overload
+    def dataclass(
+        *,
+        init: Literal[False] = False,
+        repr: bool = True,
+        eq: bool = True,
+        order: bool = False,
+        unsafe_hash: bool = False,
+        frozen: bool = False,
+        config: ConfigDict | type[object] | None = None,
+        validate_on_init: bool | None = None,
+        kw_only: bool = ...,
+        slots: bool = ...,
+    ) -> Callable[[type[_T]], type[PydanticDataclass]]:  # type: ignore
+        ...
+
+    @dataclass_transform(field_specifiers=(dataclasses.field, Field, PrivateAttr))
+    @overload
+    def dataclass(
+        _cls: type[_T],  # type: ignore
+        *,
+        init: Literal[False] = False,
+        repr: bool = True,
+        eq: bool = True,
+        order: bool = False,
+        unsafe_hash: bool = False,
+        frozen: bool | None = None,
+        config: ConfigDict | type[object] | None = None,
+        validate_on_init: bool | None = None,
+        kw_only: bool = ...,
+        slots: bool = ...,
+    ) -> type[PydanticDataclass]: ...
+
+else:
+
+    @dataclass_transform(field_specifiers=(dataclasses.field, Field, PrivateAttr))
+    @overload
+    def dataclass(
+        *,
+        init: Literal[False] = False,
+        repr: bool = True,
+        eq: bool = True,
+        order: bool = False,
+        unsafe_hash: bool = False,
+        frozen: bool | None = None,
+        config: ConfigDict | type[object] | None = None,
+        validate_on_init: bool | None = None,
+    ) -> Callable[[type[_T]], type[PydanticDataclass]]:  # type: ignore
+        ...
+
+    @dataclass_transform(field_specifiers=(dataclasses.field, Field, PrivateAttr))
+    @overload
+    def dataclass(
+        _cls: type[_T],  # type: ignore
+        *,
+        init: Literal[False] = False,
+        repr: bool = True,
+        eq: bool = True,
+        order: bool = False,
+        unsafe_hash: bool = False,
+        frozen: bool | None = None,
+        config: ConfigDict | type[object] | None = None,
+        validate_on_init: bool | None = None,
+    ) -> type[PydanticDataclass]: ...
+
+
+@dataclass_transform(field_specifiers=(dataclasses.field, Field, PrivateAttr))
+def dataclass(
+    _cls: type[_T] | None = None,
+    *,
+    init: Literal[False] = False,
+    repr: bool = True,
+    eq: bool = True,
+    order: bool = False,
+    unsafe_hash: bool = False,
+    frozen: bool | None = None,
+    config: ConfigDict | type[object] | None = None,
+    validate_on_init: bool | None = None,
+    kw_only: bool = False,
+    slots: bool = False,
+) -> Callable[[type[_T]], type[PydanticDataclass]] | type[PydanticDataclass]:
+    """!!! abstract "Usage Documentation"
+        [`dataclasses`](../concepts/dataclasses.md)
+
+    A decorator used to create a Pydantic-enhanced dataclass, similar to the standard Python `dataclass`,
+    but with added validation.
+
+    This function should be used similarly to `dataclasses.dataclass`.
+
+    Args:
+        _cls: The target `dataclass`.
+        init: Included for signature compatibility with `dataclasses.dataclass`, and is passed through to
+            `dataclasses.dataclass` when appropriate. If specified, must be set to `False`, as pydantic inserts its
+            own  `__init__` function.
+        repr: A boolean indicating whether to include the field in the `__repr__` output.
+        eq: Determines if a `__eq__` method should be generated for the class.
+        order: Determines if comparison magic methods should be generated, such as `__lt__`, but not `__eq__`.
+        unsafe_hash: Determines if a `__hash__` method should be included in the class, as in `dataclasses.dataclass`.
+        frozen: Determines if the generated class should be a 'frozen' `dataclass`, which does not allow its
+            attributes to be modified after it has been initialized. If not set, the value from the provided `config` argument will be used (and will default to `False` otherwise).
+        config: The Pydantic config to use for the `dataclass`.
+        validate_on_init: A deprecated parameter included for backwards compatibility; in V2, all Pydantic dataclasses
+            are validated on init.
+        kw_only: Determines if `__init__` method parameters must be specified by keyword only. Defaults to `False`.
+        slots: Determines if the generated class should be a 'slots' `dataclass`, which does not allow the addition of
+            new attributes after instantiation.
+
+    Returns:
+        A decorator that accepts a class as its argument and returns a Pydantic `dataclass`.
+
+    Raises:
+        AssertionError: Raised if `init` is not `False` or `validate_on_init` is `False`.
+    """
+    assert init is False, 'pydantic.dataclasses.dataclass only supports init=False'
+    assert validate_on_init is not False, 'validate_on_init=False is no longer supported'
+
+    if sys.version_info >= (3, 10):
+        kwargs = {'kw_only': kw_only, 'slots': slots}
+    else:
+        kwargs = {}
+
+    def create_dataclass(cls: type[Any]) -> type[PydanticDataclass]:
+        """Create a Pydantic dataclass from a regular dataclass.
+
+        Args:
+            cls: The class to create the Pydantic dataclass from.
+
+        Returns:
+            A Pydantic dataclass.
+        """
+        from ._internal._utils import is_model_class
+
+        if is_model_class(cls):
+            raise PydanticUserError(
+                f'Cannot create a Pydantic dataclass from {cls.__name__} as it is already a Pydantic model',
+                code='dataclass-on-model',
+            )
+
+        original_cls = cls
+
+        # we warn on conflicting config specifications, but only if the class doesn't have a dataclass base
+        # because a dataclass base might provide a __pydantic_config__ attribute that we don't want to warn about
+        has_dataclass_base = any(dataclasses.is_dataclass(base) for base in cls.__bases__)
+        if not has_dataclass_base and config is not None and hasattr(cls, '__pydantic_config__'):
+            warn(
+                f'`config` is set via both the `dataclass` decorator and `__pydantic_config__` for dataclass {cls.__name__}. '
+                f'The `config` specification from `dataclass` decorator will take priority.',
+                category=UserWarning,
+                stacklevel=2,
+            )
+
+        # if config is not explicitly provided, try to read it from the type
+        config_dict = config if config is not None else getattr(cls, '__pydantic_config__', None)
+        config_wrapper = _config.ConfigWrapper(config_dict)
+        decorators = _decorators.DecoratorInfos.build(cls, replace_wrapped_methods=True)
+        decorators.update_from_config(config_wrapper)
+
+        # Keep track of the original __doc__ so that we can restore it after applying the dataclasses decorator
+        # Otherwise, classes with no __doc__ will have their signature added into the JSON schema description,
+        # since dataclasses.dataclass will set this as the __doc__
+        original_doc = cls.__doc__
+
+        if _pydantic_dataclasses.is_stdlib_dataclass(cls):
+            # Vanilla dataclasses include a default docstring (representing the class signature),
+            # which we don't want to preserve.
+            original_doc = None
+
+            # We don't want to add validation to the existing std lib dataclass, so we will subclass it
+            #   If the class is generic, we need to make sure the subclass also inherits from Generic
+            #   with all the same parameters.
+            bases = (cls,)
+            if issubclass(cls, Generic):
+                generic_base = Generic[cls.__parameters__]  # type: ignore
+                bases = bases + (generic_base,)
+            cls = types.new_class(cls.__name__, bases)
+
+        # Respect frozen setting from dataclass constructor and fallback to config setting if not provided
+        if frozen is not None:
+            frozen_ = frozen
+            if config_wrapper.frozen:
+                # It's not recommended to define both, as the setting from the dataclass decorator will take priority.
+                warn(
+                    f'`frozen` is set via both the `dataclass` decorator and `config` for dataclass {cls.__name__!r}.'
+                    'This is not recommended. The `frozen` specification on `dataclass` will take priority.',
+                    category=UserWarning,
+                    stacklevel=2,
+                )
+        else:
+            frozen_ = config_wrapper.frozen or False
+
+        # Make Pydantic's `Field()` function compatible with stdlib dataclasses. As we'll decorate
+        # `cls` with the stdlib `@dataclass` decorator first, there are two attributes, `kw_only` and
+        # `repr` that need to be understood *during* the stdlib creation. We do so in two steps:
+
+        # 1. On the decorated class, wrap `Field()` assignment with `dataclass.field()`, with the
+        # two attributes set (done in `as_dataclass_field()`)
+        cls_anns = _typing_extra.safe_get_annotations(cls)
+        for field_name in cls_anns:
+            # We should look for assignments in `__dict__` instead, but for now we follow
+            # the same behavior as stdlib dataclasses (see https://github.com/python/cpython/issues/88609)
+            field_value = getattr(cls, field_name, None)
+            if isinstance(field_value, FieldInfo):
+                setattr(cls, field_name, _pydantic_dataclasses.as_dataclass_field(field_value))
+
+        # 2. For bases of `cls` that are stdlib dataclasses, we temporarily patch their fields
+        # (see the docstring of the context manager):
+        with _pydantic_dataclasses.patch_base_fields(cls):
+            cls = dataclasses.dataclass(  # pyright: ignore[reportCallIssue]
+                cls,
+                # the value of init here doesn't affect anything except that it makes it easier to generate a signature
+                init=True,
+                repr=repr,
+                eq=eq,
+                order=order,
+                unsafe_hash=unsafe_hash,
+                frozen=frozen_,
+                **kwargs,
+            )
+
+        if config_wrapper.validate_assignment:
+            original_setattr = cls.__setattr__
+
+            @functools.wraps(cls.__setattr__)
+            def validated_setattr(instance: PydanticDataclass, name: str, value: Any, /) -> None:
+                if frozen_:
+                    return original_setattr(instance, name, value)  # pyright: ignore[reportCallIssue]
+                inst_cls = type(instance)
+                attr = getattr(inst_cls, name, None)
+
+                if isinstance(attr, property):
+                    attr.__set__(instance, value)
+                elif isinstance(attr, functools.cached_property):
+                    instance.__dict__.__setitem__(name, value)
+                else:
+                    inst_cls.__pydantic_validator__.validate_assignment(instance, name, value)
+
+            cls.__setattr__ = validated_setattr.__get__(None, cls)  # type: ignore
+
+            if slots and not hasattr(cls, '__setstate__'):
+                # If slots is set, `pickle` (relied on by `copy.copy()`) will use
+                # `__setattr__()` to reconstruct the dataclass. However, the custom
+                # `__setattr__()` set above relies on `validate_assignment()`, which
+                # in turn expects all the field values to be already present on the
+                # instance, resulting in attribute errors.
+                # As such, we make use of `object.__setattr__()` instead.
+                # Note that we do so only if `__setstate__()` isn't already set (this is the
+                # case if on top of `slots`, `frozen` is used).
+
+                # Taken from `dataclasses._dataclass_get/setstate()`:
+                def _dataclass_getstate(self: Any) -> list[Any]:
+                    return [getattr(self, f.name) for f in dataclasses.fields(self)]
+
+                def _dataclass_setstate(self: Any, state: list[Any]) -> None:
+                    for field, value in zip(dataclasses.fields(self), state):
+                        object.__setattr__(self, field.name, value)
+
+                cls.__getstate__ = _dataclass_getstate  # pyright: ignore[reportAttributeAccessIssue]
+                cls.__setstate__ = _dataclass_setstate  # pyright: ignore[reportAttributeAccessIssue]
+
+        # This is an undocumented attribute to distinguish stdlib/Pydantic dataclasses.
+        # It should be set as early as possible:
+        cls.__is_pydantic_dataclass__ = True
+        cls.__pydantic_decorators__ = decorators  # type: ignore
+        cls.__doc__ = original_doc
+        # Can be non-existent for dynamically created classes:
+        firstlineno = getattr(original_cls, '__firstlineno__', None)
+        cls.__module__ = original_cls.__module__
+        if sys.version_info >= (3, 13) and firstlineno is not None:
+            # As per https://docs.python.org/3/reference/datamodel.html#type.__firstlineno__:
+            # Setting the `__module__` attribute removes the `__firstlineno__` item from the type’s dictionary.
+            original_cls.__firstlineno__ = firstlineno
+            cls.__firstlineno__ = firstlineno
+        cls.__qualname__ = original_cls.__qualname__
+        cls.__pydantic_fields_complete__ = classmethod(_pydantic_fields_complete)
+        cls.__pydantic_complete__ = False  # `complete_dataclass` will set it to `True` if successful.
+        # TODO `parent_namespace` is currently None, but we could do the same thing as Pydantic models:
+        # fetch the parent ns using `parent_frame_namespace` (if the dataclass was defined in a function),
+        # and possibly cache it (see the `__pydantic_parent_namespace__` logic for models).
+        _pydantic_dataclasses.complete_dataclass(cls, config_wrapper, raise_errors=False)
+        return cls
+
+    return create_dataclass if _cls is None else create_dataclass(_cls)
+
+
+def _pydantic_fields_complete(cls: type[PydanticDataclass]) -> bool:
+    """Return whether the fields were successfully collected (i.e. type hints were successfully resolved).
+
+    This is a private helper, not meant to be used outside Pydantic.
+    """
+    return all(field_info._complete for field_info in cls.__pydantic_fields__.values())
+
+
+__getattr__ = getattr_migration(__name__)
+
+if sys.version_info < (3, 11):
+    # Monkeypatch dataclasses.InitVar so that typing doesn't error if it occurs as a type when evaluating type hints
+    # Starting in 3.11, typing.get_type_hints will not raise an error if the retrieved type hints are not callable.
+
+    def _call_initvar(*args: Any, **kwargs: Any) -> NoReturn:
+        """This function does nothing but raise an error that is as similar as possible to what you'd get
+        if you were to try calling `InitVar[int]()` without this monkeypatch. The whole purpose is just
+        to ensure typing._type_check does not error if the type hint evaluates to `InitVar[<parameter>]`.
+        """
+        raise TypeError("'InitVar' object is not callable")
+
+    dataclasses.InitVar.__call__ = _call_initvar
+
+
+def rebuild_dataclass(
+    cls: type[PydanticDataclass],
+    *,
+    force: bool = False,
+    raise_errors: bool = True,
+    _parent_namespace_depth: int = 2,
+    _types_namespace: MappingNamespace | None = None,
+) -> bool | None:
+    """Try to rebuild the pydantic-core schema for the dataclass.
+
+    This may be necessary when one of the annotations is a ForwardRef which could not be resolved during
+    the initial attempt to build the schema, and automatic rebuilding fails.
+
+    This is analogous to `BaseModel.model_rebuild`.
+
+    Args:
+        cls: The class to rebuild the pydantic-core schema for.
+        force: Whether to force the rebuilding of the schema, defaults to `False`.
+        raise_errors: Whether to raise errors, defaults to `True`.
+        _parent_namespace_depth: The depth level of the parent namespace, defaults to 2.
+        _types_namespace: The types namespace, defaults to `None`.
+
+    Returns:
+        Returns `None` if the schema is already "complete" and rebuilding was not required.
+        If rebuilding _was_ required, returns `True` if rebuilding was successful, otherwise `False`.
+    """
+    if not force and cls.__pydantic_complete__:
+        return None
+
+    for attr in ('__pydantic_core_schema__', '__pydantic_validator__', '__pydantic_serializer__'):
+        if attr in cls.__dict__ and not isinstance(getattr(cls, attr), _mock_val_ser.MockValSer):
+            # Deleting the validator/serializer is necessary as otherwise they can get reused in
+            # pydantic-core. Same applies for the core schema that can be reused in schema generation.
+            delattr(cls, attr)
+
+    cls.__pydantic_complete__ = False
+
+    if _types_namespace is not None:
+        rebuild_ns = _types_namespace
+    elif _parent_namespace_depth > 0:
+        rebuild_ns = _typing_extra.parent_frame_namespace(parent_depth=_parent_namespace_depth, force=True) or {}
+    else:
+        rebuild_ns = {}
+
+    ns_resolver = _namespace_utils.NsResolver(
+        parent_namespace=rebuild_ns,
+    )
+
+    return _pydantic_dataclasses.complete_dataclass(
+        cls,
+        _config.ConfigWrapper(cls.__pydantic_config__, check=False),
+        raise_errors=raise_errors,
+        ns_resolver=ns_resolver,
+        # We could provide a different config instead (with `'defer_build'` set to `True`)
+        # of this explicit `_force_build` argument, but because config can come from the
+        # decorator parameter or the `__pydantic_config__` attribute, `complete_dataclass`
+        # will overwrite `__pydantic_config__` with the provided config above:
+        _force_build=True,
+    )
+
+
+def is_pydantic_dataclass(class_: type[Any], /) -> TypeGuard[type[PydanticDataclass]]:
+    """Whether a class is a pydantic dataclass.
+
+    Args:
+        class_: The class.
+
+    Returns:
+        `True` if the class is a pydantic dataclass, `False` otherwise.
+    """
+    try:
+        return '__is_pydantic_dataclass__' in class_.__dict__ and dataclasses.is_dataclass(class_)
+    except AttributeError:
+        return False

.mcp-venv/lib/python3.13/site-packages/pydantic/datetime_parse.py

@@ -0,0 +1,5 @@
+"""The `datetime_parse` module is a backport module from V1."""
+
+from ._migration import getattr_migration
+
+__getattr__ = getattr_migration(__name__)

.mcp-venv/lib/python3.13/site-packages/pydantic/decorator.py

@@ -0,0 +1,5 @@
+"""The `decorator` module is a backport module from V1."""
+
+from ._migration import getattr_migration
+
+__getattr__ = getattr_migration(__name__)

.mcp-venv/lib/python3.13/site-packages/pydantic/deprecated/class_validators.py

@@ -0,0 +1,256 @@
+"""Old `@validator` and `@root_validator` function validators from V1."""
+
+from __future__ import annotations as _annotations
+
+from functools import partial, partialmethod
+from types import FunctionType
+from typing import TYPE_CHECKING, Any, Callable, Literal, TypeVar, Union, overload
+from warnings import warn
+
+from typing_extensions import Protocol, TypeAlias, deprecated
+
+from .._internal import _decorators, _decorators_v1
+from ..errors import PydanticUserError
+from ..warnings import PydanticDeprecatedSince20
+
+_ALLOW_REUSE_WARNING_MESSAGE = '`allow_reuse` is deprecated and will be ignored; it should no longer be necessary'
+
+
+if TYPE_CHECKING:
+
+    class _OnlyValueValidatorClsMethod(Protocol):
+        def __call__(self, __cls: Any, __value: Any) -> Any: ...
+
+    class _V1ValidatorWithValuesClsMethod(Protocol):
+        def __call__(self, __cls: Any, __value: Any, values: dict[str, Any]) -> Any: ...
+
+    class _V1ValidatorWithValuesKwOnlyClsMethod(Protocol):
+        def __call__(self, __cls: Any, __value: Any, *, values: dict[str, Any]) -> Any: ...
+
+    class _V1ValidatorWithKwargsClsMethod(Protocol):
+        def __call__(self, __cls: Any, **kwargs: Any) -> Any: ...
+
+    class _V1ValidatorWithValuesAndKwargsClsMethod(Protocol):
+        def __call__(self, __cls: Any, values: dict[str, Any], **kwargs: Any) -> Any: ...
+
+    class _V1RootValidatorClsMethod(Protocol):
+        def __call__(
+            self, __cls: Any, __values: _decorators_v1.RootValidatorValues
+        ) -> _decorators_v1.RootValidatorValues: ...
+
+    V1Validator = Union[
+        _OnlyValueValidatorClsMethod,
+        _V1ValidatorWithValuesClsMethod,
+        _V1ValidatorWithValuesKwOnlyClsMethod,
+        _V1ValidatorWithKwargsClsMethod,
+        _V1ValidatorWithValuesAndKwargsClsMethod,
+        _decorators_v1.V1ValidatorWithValues,
+        _decorators_v1.V1ValidatorWithValuesKwOnly,
+        _decorators_v1.V1ValidatorWithKwargs,
+        _decorators_v1.V1ValidatorWithValuesAndKwargs,
+    ]
+
+    V1RootValidator = Union[
+        _V1RootValidatorClsMethod,
+        _decorators_v1.V1RootValidatorFunction,
+    ]
+
+    _PartialClsOrStaticMethod: TypeAlias = Union[classmethod[Any, Any, Any], staticmethod[Any, Any], partialmethod[Any]]
+
+    # Allow both a V1 (assumed pre=False) or V2 (assumed mode='after') validator
+    # We lie to type checkers and say we return the same thing we get
+    # but in reality we return a proxy object that _mostly_ behaves like the wrapped thing
+    _V1ValidatorType = TypeVar('_V1ValidatorType', V1Validator, _PartialClsOrStaticMethod)
+    _V1RootValidatorFunctionType = TypeVar(
+        '_V1RootValidatorFunctionType',
+        _decorators_v1.V1RootValidatorFunction,
+        _V1RootValidatorClsMethod,
+        _PartialClsOrStaticMethod,
+    )
+else:
+    # See PyCharm issues https://youtrack.jetbrains.com/issue/PY-21915
+    # and https://youtrack.jetbrains.com/issue/PY-51428
+    DeprecationWarning = PydanticDeprecatedSince20
+
+
+@deprecated(
+    'Pydantic V1 style `@validator` validators are deprecated.'
+    ' You should migrate to Pydantic V2 style `@field_validator` validators,'
+    ' see the migration guide for more details',
+    category=None,
+)
+def validator(
+    __field: str,
+    *fields: str,
+    pre: bool = False,
+    each_item: bool = False,
+    always: bool = False,
+    check_fields: bool | None = None,
+    allow_reuse: bool = False,
+) -> Callable[[_V1ValidatorType], _V1ValidatorType]:
+    """Decorate methods on the class indicating that they should be used to validate fields.
+
+    Args:
+        __field (str): The first field the validator should be called on; this is separate
+            from `fields` to ensure an error is raised if you don't pass at least one.
+        *fields (str): Additional field(s) the validator should be called on.
+        pre (bool, optional): Whether this validator should be called before the standard
+            validators (else after). Defaults to False.
+        each_item (bool, optional): For complex objects (sets, lists etc.) whether to validate
+            individual elements rather than the whole object. Defaults to False.
+        always (bool, optional): Whether this method and other validators should be called even if
+            the value is missing. Defaults to False.
+        check_fields (bool | None, optional): Whether to check that the fields actually exist on the model.
+            Defaults to None.
+        allow_reuse (bool, optional): Whether to track and raise an error if another validator refers to
+            the decorated function. Defaults to False.
+
+    Returns:
+        Callable: A decorator that can be used to decorate a
+            function to be used as a validator.
+    """
+    warn(
+        'Pydantic V1 style `@validator` validators are deprecated.'
+        ' You should migrate to Pydantic V2 style `@field_validator` validators,'
+        ' see the migration guide for more details',
+        DeprecationWarning,
+        stacklevel=2,
+    )
+
+    if allow_reuse is True:  # pragma: no cover
+        warn(_ALLOW_REUSE_WARNING_MESSAGE, DeprecationWarning, stacklevel=2)
+    fields = __field, *fields
+    if isinstance(fields[0], FunctionType):
+        raise PydanticUserError(
+            '`@validator` should be used with fields and keyword arguments, not bare. '
+            "E.g. usage should be `@validator('<field_name>', ...)`",
+            code='decorator-missing-arguments',
+        )
+    elif not all(isinstance(field, str) for field in fields):
+        raise PydanticUserError(
+            '`@validator` fields should be passed as separate string args. '
+            "E.g. usage should be `@validator('<field_name_1>', '<field_name_2>', ...)`",
+            code='decorator-invalid-fields',
+        )
+
+    mode: Literal['before', 'after'] = 'before' if pre is True else 'after'
+
+    def dec(f: Any) -> _decorators.PydanticDescriptorProxy[Any]:
+        if _decorators.is_instance_method_from_sig(f):
+            raise PydanticUserError(
+                '`@validator` cannot be applied to instance methods', code='validator-instance-method'
+            )
+        # auto apply the @classmethod decorator
+        f = _decorators.ensure_classmethod_based_on_signature(f)
+        wrap = _decorators_v1.make_generic_v1_field_validator
+        validator_wrapper_info = _decorators.ValidatorDecoratorInfo(
+            fields=fields,
+            mode=mode,
+            each_item=each_item,
+            always=always,
+            check_fields=check_fields,
+        )
+        return _decorators.PydanticDescriptorProxy(f, validator_wrapper_info, shim=wrap)
+
+    return dec  # type: ignore[return-value]
+
+
+@overload
+def root_validator(
+    *,
+    # if you don't specify `pre` the default is `pre=False`
+    # which means you need to specify `skip_on_failure=True`
+    skip_on_failure: Literal[True],
+    allow_reuse: bool = ...,
+) -> Callable[
+    [_V1RootValidatorFunctionType],
+    _V1RootValidatorFunctionType,
+]: ...
+
+
+@overload
+def root_validator(
+    *,
+    # if you specify `pre=True` then you don't need to specify
+    # `skip_on_failure`, in fact it is not allowed as an argument!
+    pre: Literal[True],
+    allow_reuse: bool = ...,
+) -> Callable[
+    [_V1RootValidatorFunctionType],
+    _V1RootValidatorFunctionType,
+]: ...
+
+
+@overload
+def root_validator(
+    *,
+    # if you explicitly specify `pre=False` then you
+    # MUST specify `skip_on_failure=True`
+    pre: Literal[False],
+    skip_on_failure: Literal[True],
+    allow_reuse: bool = ...,
+) -> Callable[
+    [_V1RootValidatorFunctionType],
+    _V1RootValidatorFunctionType,
+]: ...
+
+
+@deprecated(
+    'Pydantic V1 style `@root_validator` validators are deprecated.'
+    ' You should migrate to Pydantic V2 style `@model_validator` validators,'
+    ' see the migration guide for more details',
+    category=None,
+)
+def root_validator(
+    *__args,
+    pre: bool = False,
+    skip_on_failure: bool = False,
+    allow_reuse: bool = False,
+) -> Any:
+    """Decorate methods on a model indicating that they should be used to validate (and perhaps
+    modify) data either before or after standard model parsing/validation is performed.
+
+    Args:
+        pre (bool, optional): Whether this validator should be called before the standard
+            validators (else after). Defaults to False.
+        skip_on_failure (bool, optional): Whether to stop validation and return as soon as a
+            failure is encountered. Defaults to False.
+        allow_reuse (bool, optional): Whether to track and raise an error if another validator
+            refers to the decorated function. Defaults to False.
+
+    Returns:
+        Any: A decorator that can be used to decorate a function to be used as a root_validator.
+    """
+    warn(
+        'Pydantic V1 style `@root_validator` validators are deprecated.'
+        ' You should migrate to Pydantic V2 style `@model_validator` validators,'
+        ' see the migration guide for more details',
+        DeprecationWarning,
+        stacklevel=2,
+    )
+
+    if __args:
+        # Ensure a nice error is raised if someone attempts to use the bare decorator
+        return root_validator()(*__args)  # type: ignore
+
+    if allow_reuse is True:  # pragma: no cover
+        warn(_ALLOW_REUSE_WARNING_MESSAGE, DeprecationWarning, stacklevel=2)
+    mode: Literal['before', 'after'] = 'before' if pre is True else 'after'
+    if pre is False and skip_on_failure is not True:
+        raise PydanticUserError(
+            'If you use `@root_validator` with pre=False (the default) you MUST specify `skip_on_failure=True`.'
+            ' Note that `@root_validator` is deprecated and should be replaced with `@model_validator`.',
+            code='root-validator-pre-skip',
+        )
+
+    wrap = partial(_decorators_v1.make_v1_generic_root_validator, pre=pre)
+
+    def dec(f: Callable[..., Any] | classmethod[Any, Any, Any] | staticmethod[Any, Any]) -> Any:
+        if _decorators.is_instance_method_from_sig(f):
+            raise TypeError('`@root_validator` cannot be applied to instance methods')
+        # auto apply the @classmethod decorator
+        res = _decorators.ensure_classmethod_based_on_signature(f)
+        dec_info = _decorators.RootValidatorDecoratorInfo(mode=mode)
+        return _decorators.PydanticDescriptorProxy(res, dec_info, shim=wrap)
+
+    return dec

.mcp-venv/lib/python3.13/site-packages/pydantic/deprecated/config.py

@@ -0,0 +1,72 @@
+from __future__ import annotations as _annotations
+
+import warnings
+from typing import TYPE_CHECKING, Any, Literal
+
+from typing_extensions import deprecated
+
+from .._internal import _config
+from ..warnings import PydanticDeprecatedSince20
+
+if not TYPE_CHECKING:
+    # See PyCharm issues https://youtrack.jetbrains.com/issue/PY-21915
+    # and https://youtrack.jetbrains.com/issue/PY-51428
+    DeprecationWarning = PydanticDeprecatedSince20
+
+__all__ = 'BaseConfig', 'Extra'
+
+
+class _ConfigMetaclass(type):
+    def __getattr__(self, item: str) -> Any:
+        try:
+            obj = _config.config_defaults[item]
+            warnings.warn(_config.DEPRECATION_MESSAGE, DeprecationWarning)
+            return obj
+        except KeyError as exc:
+            raise AttributeError(f"type object '{self.__name__}' has no attribute {exc}") from exc
+
+
+@deprecated('BaseConfig is deprecated. Use the `pydantic.ConfigDict` instead.', category=PydanticDeprecatedSince20)
+class BaseConfig(metaclass=_ConfigMetaclass):
+    """This class is only retained for backwards compatibility.
+
+    !!! Warning "Deprecated"
+        BaseConfig is deprecated. Use the [`pydantic.ConfigDict`][pydantic.ConfigDict] instead.
+    """
+
+    def __getattr__(self, item: str) -> Any:
+        try:
+            obj = super().__getattribute__(item)
+            warnings.warn(_config.DEPRECATION_MESSAGE, DeprecationWarning)
+            return obj
+        except AttributeError as exc:
+            try:
+                return getattr(type(self), item)
+            except AttributeError:
+                # re-raising changes the displayed text to reflect that `self` is not a type
+                raise AttributeError(str(exc)) from exc
+
+    def __init_subclass__(cls, **kwargs: Any) -> None:
+        warnings.warn(_config.DEPRECATION_MESSAGE, DeprecationWarning)
+        return super().__init_subclass__(**kwargs)
+
+
+class _ExtraMeta(type):
+    def __getattribute__(self, __name: str) -> Any:
+        # The @deprecated decorator accesses other attributes, so we only emit a warning for the expected ones
+        if __name in {'allow', 'ignore', 'forbid'}:
+            warnings.warn(
+                "`pydantic.config.Extra` is deprecated, use literal values instead (e.g. `extra='allow'`)",
+                DeprecationWarning,
+                stacklevel=2,
+            )
+        return super().__getattribute__(__name)
+
+
+@deprecated(
+    "Extra is deprecated. Use literal values instead (e.g. `extra='allow'`)", category=PydanticDeprecatedSince20
+)
+class Extra(metaclass=_ExtraMeta):
+    allow: Literal['allow'] = 'allow'
+    ignore: Literal['ignore'] = 'ignore'
+    forbid: Literal['forbid'] = 'forbid'

.mcp-venv/lib/python3.13/site-packages/pydantic/deprecated/copy_internals.py

@@ -0,0 +1,224 @@
+from __future__ import annotations as _annotations
+
+import typing
+from copy import deepcopy
+from enum import Enum
+from typing import Any
+
+import typing_extensions
+
+from .._internal import (
+    _model_construction,
+    _typing_extra,
+    _utils,
+)
+
+if typing.TYPE_CHECKING:
+    from .. import BaseModel
+    from .._internal._utils import AbstractSetIntStr, MappingIntStrAny
+
+    AnyClassMethod = classmethod[Any, Any, Any]
+    TupleGenerator = typing.Generator[tuple[str, Any], None, None]
+    Model = typing.TypeVar('Model', bound='BaseModel')
+    # should be `set[int] | set[str] | dict[int, IncEx] | dict[str, IncEx] | None`, but mypy can't cope
+    IncEx: typing_extensions.TypeAlias = 'set[int] | set[str] | dict[int, Any] | dict[str, Any] | None'
+
+_object_setattr = _model_construction.object_setattr
+
+
+def _iter(
+    self: BaseModel,
+    to_dict: bool = False,
+    by_alias: bool = False,
+    include: AbstractSetIntStr | MappingIntStrAny | None = None,
+    exclude: AbstractSetIntStr | MappingIntStrAny | None = None,
+    exclude_unset: bool = False,
+    exclude_defaults: bool = False,
+    exclude_none: bool = False,
+) -> TupleGenerator:
+    # Merge field set excludes with explicit exclude parameter with explicit overriding field set options.
+    # The extra "is not None" guards are not logically necessary but optimizes performance for the simple case.
+    if exclude is not None:
+        exclude = _utils.ValueItems.merge(
+            {k: v.exclude for k, v in self.__pydantic_fields__.items() if v.exclude is not None}, exclude
+        )
+
+    if include is not None:
+        include = _utils.ValueItems.merge(dict.fromkeys(self.__pydantic_fields__, True), include, intersect=True)
+
+    allowed_keys = _calculate_keys(self, include=include, exclude=exclude, exclude_unset=exclude_unset)  # type: ignore
+    if allowed_keys is None and not (to_dict or by_alias or exclude_unset or exclude_defaults or exclude_none):
+        # huge boost for plain _iter()
+        yield from self.__dict__.items()
+        if self.__pydantic_extra__:
+            yield from self.__pydantic_extra__.items()
+        return
+
+    value_exclude = _utils.ValueItems(self, exclude) if exclude is not None else None
+    value_include = _utils.ValueItems(self, include) if include is not None else None
+
+    if self.__pydantic_extra__ is None:
+        items = self.__dict__.items()
+    else:
+        items = list(self.__dict__.items()) + list(self.__pydantic_extra__.items())
+
+    for field_key, v in items:
+        if (allowed_keys is not None and field_key not in allowed_keys) or (exclude_none and v is None):
+            continue
+
+        if exclude_defaults:
+            try:
+                field = self.__pydantic_fields__[field_key]
+            except KeyError:
+                pass
+            else:
+                if not field.is_required() and field.default == v:
+                    continue
+
+        if by_alias and field_key in self.__pydantic_fields__:
+            dict_key = self.__pydantic_fields__[field_key].alias or field_key
+        else:
+            dict_key = field_key
+
+        if to_dict or value_include or value_exclude:
+            v = _get_value(
+                type(self),
+                v,
+                to_dict=to_dict,
+                by_alias=by_alias,
+                include=value_include and value_include.for_element(field_key),
+                exclude=value_exclude and value_exclude.for_element(field_key),
+                exclude_unset=exclude_unset,
+                exclude_defaults=exclude_defaults,
+                exclude_none=exclude_none,
+            )
+        yield dict_key, v
+
+
+def _copy_and_set_values(
+    self: Model,
+    values: dict[str, Any],
+    fields_set: set[str],
+    extra: dict[str, Any] | None = None,
+    private: dict[str, Any] | None = None,
+    *,
+    deep: bool,  # UP006
+) -> Model:
+    if deep:
+        # chances of having empty dict here are quite low for using smart_deepcopy
+        values = deepcopy(values)
+        extra = deepcopy(extra)
+        private = deepcopy(private)
+
+    cls = self.__class__
+    m = cls.__new__(cls)
+    _object_setattr(m, '__dict__', values)
+    _object_setattr(m, '__pydantic_extra__', extra)
+    _object_setattr(m, '__pydantic_fields_set__', fields_set)
+    _object_setattr(m, '__pydantic_private__', private)
+
+    return m
+
+
+@typing.no_type_check
+def _get_value(
+    cls: type[BaseModel],
+    v: Any,
+    to_dict: bool,
+    by_alias: bool,
+    include: AbstractSetIntStr | MappingIntStrAny | None,
+    exclude: AbstractSetIntStr | MappingIntStrAny | None,
+    exclude_unset: bool,
+    exclude_defaults: bool,
+    exclude_none: bool,
+) -> Any:
+    from .. import BaseModel
+
+    if isinstance(v, BaseModel):
+        if to_dict:
+            return v.model_dump(
+                by_alias=by_alias,
+                exclude_unset=exclude_unset,
+                exclude_defaults=exclude_defaults,
+                include=include,  # type: ignore
+                exclude=exclude,  # type: ignore
+                exclude_none=exclude_none,
+            )
+        else:
+            return v.copy(include=include, exclude=exclude)
+
+    value_exclude = _utils.ValueItems(v, exclude) if exclude else None
+    value_include = _utils.ValueItems(v, include) if include else None
+
+    if isinstance(v, dict):
+        return {
+            k_: _get_value(
+                cls,
+                v_,
+                to_dict=to_dict,
+                by_alias=by_alias,
+                exclude_unset=exclude_unset,
+                exclude_defaults=exclude_defaults,
+                include=value_include and value_include.for_element(k_),
+                exclude=value_exclude and value_exclude.for_element(k_),
+                exclude_none=exclude_none,
+            )
+            for k_, v_ in v.items()
+            if (not value_exclude or not value_exclude.is_excluded(k_))
+            and (not value_include or value_include.is_included(k_))
+        }
+
+    elif _utils.sequence_like(v):
+        seq_args = (
+            _get_value(
+                cls,
+                v_,
+                to_dict=to_dict,
+                by_alias=by_alias,
+                exclude_unset=exclude_unset,
+                exclude_defaults=exclude_defaults,
+                include=value_include and value_include.for_element(i),
+                exclude=value_exclude and value_exclude.for_element(i),
+                exclude_none=exclude_none,
+            )
+            for i, v_ in enumerate(v)
+            if (not value_exclude or not value_exclude.is_excluded(i))
+            and (not value_include or value_include.is_included(i))
+        )
+
+        return v.__class__(*seq_args) if _typing_extra.is_namedtuple(v.__class__) else v.__class__(seq_args)
+
+    elif isinstance(v, Enum) and getattr(cls.model_config, 'use_enum_values', False):
+        return v.value
+
+    else:
+        return v
+
+
+def _calculate_keys(
+    self: BaseModel,
+    include: MappingIntStrAny | None,
+    exclude: MappingIntStrAny | None,
+    exclude_unset: bool,
+    update: dict[str, Any] | None = None,  # noqa UP006
+) -> typing.AbstractSet[str] | None:
+    if include is None and exclude is None and exclude_unset is False:
+        return None
+
+    keys: typing.AbstractSet[str]
+    if exclude_unset:
+        keys = self.__pydantic_fields_set__.copy()
+    else:
+        keys = set(self.__dict__.keys())
+        keys = keys | (self.__pydantic_extra__ or {}).keys()
+
+    if include is not None:
+        keys &= include.keys()
+
+    if update:
+        keys -= update.keys()
+
+    if exclude:
+        keys -= {k for k, v in exclude.items() if _utils.ValueItems.is_true(v)}
+
+    return keys

.mcp-venv/lib/python3.13/site-packages/pydantic/deprecated/decorator.py

@@ -0,0 +1,284 @@
+import warnings
+from collections.abc import Mapping
+from functools import wraps
+from typing import TYPE_CHECKING, Any, Callable, Optional, TypeVar, Union, overload
+
+from typing_extensions import deprecated
+
+from .._internal import _config, _typing_extra
+from ..alias_generators import to_pascal
+from ..errors import PydanticUserError
+from ..functional_validators import field_validator
+from ..main import BaseModel, create_model
+from ..warnings import PydanticDeprecatedSince20
+
+if not TYPE_CHECKING:
+    # See PyCharm issues https://youtrack.jetbrains.com/issue/PY-21915
+    # and https://youtrack.jetbrains.com/issue/PY-51428
+    DeprecationWarning = PydanticDeprecatedSince20
+
+__all__ = ('validate_arguments',)
+
+if TYPE_CHECKING:
+    AnyCallable = Callable[..., Any]
+
+    AnyCallableT = TypeVar('AnyCallableT', bound=AnyCallable)
+    ConfigType = Union[None, type[Any], dict[str, Any]]
+
+
+@overload
+def validate_arguments(
+    func: None = None, *, config: 'ConfigType' = None
+) -> Callable[['AnyCallableT'], 'AnyCallableT']: ...
+
+
+@overload
+def validate_arguments(func: 'AnyCallableT') -> 'AnyCallableT': ...
+
+
+@deprecated(
+    'The `validate_arguments` method is deprecated; use `validate_call` instead.',
+    category=None,
+)
+def validate_arguments(func: Optional['AnyCallableT'] = None, *, config: 'ConfigType' = None) -> Any:
+    """Decorator to validate the arguments passed to a function."""
+    warnings.warn(
+        'The `validate_arguments` method is deprecated; use `validate_call` instead.',
+        PydanticDeprecatedSince20,
+        stacklevel=2,
+    )
+
+    def validate(_func: 'AnyCallable') -> 'AnyCallable':
+        vd = ValidatedFunction(_func, config)
+
+        @wraps(_func)
+        def wrapper_function(*args: Any, **kwargs: Any) -> Any:
+            return vd.call(*args, **kwargs)
+
+        wrapper_function.vd = vd  # type: ignore
+        wrapper_function.validate = vd.init_model_instance  # type: ignore
+        wrapper_function.raw_function = vd.raw_function  # type: ignore
+        wrapper_function.model = vd.model  # type: ignore
+        return wrapper_function
+
+    if func:
+        return validate(func)
+    else:
+        return validate
+
+
+ALT_V_ARGS = 'v__args'
+ALT_V_KWARGS = 'v__kwargs'
+V_POSITIONAL_ONLY_NAME = 'v__positional_only'
+V_DUPLICATE_KWARGS = 'v__duplicate_kwargs'
+
+
+class ValidatedFunction:
+    def __init__(self, function: 'AnyCallable', config: 'ConfigType'):
+        from inspect import Parameter, signature
+
+        parameters: Mapping[str, Parameter] = signature(function).parameters
+
+        if parameters.keys() & {ALT_V_ARGS, ALT_V_KWARGS, V_POSITIONAL_ONLY_NAME, V_DUPLICATE_KWARGS}:
+            raise PydanticUserError(
+                f'"{ALT_V_ARGS}", "{ALT_V_KWARGS}", "{V_POSITIONAL_ONLY_NAME}" and "{V_DUPLICATE_KWARGS}" '
+                f'are not permitted as argument names when using the "{validate_arguments.__name__}" decorator',
+                code=None,
+            )
+
+        self.raw_function = function
+        self.arg_mapping: dict[int, str] = {}
+        self.positional_only_args: set[str] = set()
+        self.v_args_name = 'args'
+        self.v_kwargs_name = 'kwargs'
+
+        type_hints = _typing_extra.get_type_hints(function, include_extras=True)
+        takes_args = False
+        takes_kwargs = False
+        fields: dict[str, tuple[Any, Any]] = {}
+        for i, (name, p) in enumerate(parameters.items()):
+            if p.annotation is p.empty:
+                annotation = Any
+            else:
+                annotation = type_hints[name]
+
+            default = ... if p.default is p.empty else p.default
+            if p.kind == Parameter.POSITIONAL_ONLY:
+                self.arg_mapping[i] = name
+                fields[name] = annotation, default
+                fields[V_POSITIONAL_ONLY_NAME] = list[str], None
+                self.positional_only_args.add(name)
+            elif p.kind == Parameter.POSITIONAL_OR_KEYWORD:
+                self.arg_mapping[i] = name
+                fields[name] = annotation, default
+                fields[V_DUPLICATE_KWARGS] = list[str], None
+            elif p.kind == Parameter.KEYWORD_ONLY:
+                fields[name] = annotation, default
+            elif p.kind == Parameter.VAR_POSITIONAL:
+                self.v_args_name = name
+                fields[name] = tuple[annotation, ...], None
+                takes_args = True
+            else:
+                assert p.kind == Parameter.VAR_KEYWORD, p.kind
+                self.v_kwargs_name = name
+                fields[name] = dict[str, annotation], None
+                takes_kwargs = True
+
+        # these checks avoid a clash between "args" and a field with that name
+        if not takes_args and self.v_args_name in fields:
+            self.v_args_name = ALT_V_ARGS
+
+        # same with "kwargs"
+        if not takes_kwargs and self.v_kwargs_name in fields:
+            self.v_kwargs_name = ALT_V_KWARGS
+
+        if not takes_args:
+            # we add the field so validation below can raise the correct exception
+            fields[self.v_args_name] = list[Any], None
+
+        if not takes_kwargs:
+            # same with kwargs
+            fields[self.v_kwargs_name] = dict[Any, Any], None
+
+        self.create_model(fields, takes_args, takes_kwargs, config)
+
+    def init_model_instance(self, *args: Any, **kwargs: Any) -> BaseModel:
+        values = self.build_values(args, kwargs)
+        return self.model(**values)
+
+    def call(self, *args: Any, **kwargs: Any) -> Any:
+        m = self.init_model_instance(*args, **kwargs)
+        return self.execute(m)
+
+    def build_values(self, args: tuple[Any, ...], kwargs: dict[str, Any]) -> dict[str, Any]:
+        values: dict[str, Any] = {}
+        if args:
+            arg_iter = enumerate(args)
+            while True:
+                try:
+                    i, a = next(arg_iter)
+                except StopIteration:
+                    break
+                arg_name = self.arg_mapping.get(i)
+                if arg_name is not None:
+                    values[arg_name] = a
+                else:
+                    values[self.v_args_name] = [a] + [a for _, a in arg_iter]
+                    break
+
+        var_kwargs: dict[str, Any] = {}
+        wrong_positional_args = []
+        duplicate_kwargs = []
+        fields_alias = [
+            field.alias
+            for name, field in self.model.__pydantic_fields__.items()
+            if name not in (self.v_args_name, self.v_kwargs_name)
+        ]
+        non_var_fields = set(self.model.__pydantic_fields__) - {self.v_args_name, self.v_kwargs_name}
+        for k, v in kwargs.items():
+            if k in non_var_fields or k in fields_alias:
+                if k in self.positional_only_args:
+                    wrong_positional_args.append(k)
+                if k in values:
+                    duplicate_kwargs.append(k)
+                values[k] = v
+            else:
+                var_kwargs[k] = v
+
+        if var_kwargs:
+            values[self.v_kwargs_name] = var_kwargs
+        if wrong_positional_args:
+            values[V_POSITIONAL_ONLY_NAME] = wrong_positional_args
+        if duplicate_kwargs:
+            values[V_DUPLICATE_KWARGS] = duplicate_kwargs
+        return values
+
+    def execute(self, m: BaseModel) -> Any:
+        d = {
+            k: v
+            for k, v in m.__dict__.items()
+            if k in m.__pydantic_fields_set__ or m.__pydantic_fields__[k].default_factory
+        }
+        var_kwargs = d.pop(self.v_kwargs_name, {})
+
+        if self.v_args_name in d:
+            args_: list[Any] = []
+            in_kwargs = False
+            kwargs = {}
+            for name, value in d.items():
+                if in_kwargs:
+                    kwargs[name] = value
+                elif name == self.v_args_name:
+                    args_ += value
+                    in_kwargs = True
+                else:
+                    args_.append(value)
+            return self.raw_function(*args_, **kwargs, **var_kwargs)
+        elif self.positional_only_args:
+            args_ = []
+            kwargs = {}
+            for name, value in d.items():
+                if name in self.positional_only_args:
+                    args_.append(value)
+                else:
+                    kwargs[name] = value
+            return self.raw_function(*args_, **kwargs, **var_kwargs)
+        else:
+            return self.raw_function(**d, **var_kwargs)
+
+    def create_model(self, fields: dict[str, Any], takes_args: bool, takes_kwargs: bool, config: 'ConfigType') -> None:
+        pos_args = len(self.arg_mapping)
+
+        config_wrapper = _config.ConfigWrapper(config)
+
+        if config_wrapper.alias_generator:
+            raise PydanticUserError(
+                'Setting the "alias_generator" property on custom Config for '
+                '@validate_arguments is not yet supported, please remove.',
+                code=None,
+            )
+        if config_wrapper.extra is None:
+            config_wrapper.config_dict['extra'] = 'forbid'
+
+        class DecoratorBaseModel(BaseModel):
+            @field_validator(self.v_args_name, check_fields=False)
+            @classmethod
+            def check_args(cls, v: Optional[list[Any]]) -> Optional[list[Any]]:
+                if takes_args or v is None:
+                    return v
+
+                raise TypeError(f'{pos_args} positional arguments expected but {pos_args + len(v)} given')
+
+            @field_validator(self.v_kwargs_name, check_fields=False)
+            @classmethod
+            def check_kwargs(cls, v: Optional[dict[str, Any]]) -> Optional[dict[str, Any]]:
+                if takes_kwargs or v is None:
+                    return v
+
+                plural = '' if len(v) == 1 else 's'
+                keys = ', '.join(map(repr, v.keys()))
+                raise TypeError(f'unexpected keyword argument{plural}: {keys}')
+
+            @field_validator(V_POSITIONAL_ONLY_NAME, check_fields=False)
+            @classmethod
+            def check_positional_only(cls, v: Optional[list[str]]) -> None:
+                if v is None:
+                    return
+
+                plural = '' if len(v) == 1 else 's'
+                keys = ', '.join(map(repr, v))
+                raise TypeError(f'positional-only argument{plural} passed as keyword argument{plural}: {keys}')
+
+            @field_validator(V_DUPLICATE_KWARGS, check_fields=False)
+            @classmethod
+            def check_duplicate_kwargs(cls, v: Optional[list[str]]) -> None:
+                if v is None:
+                    return
+
+                plural = '' if len(v) == 1 else 's'
+                keys = ', '.join(map(repr, v))
+                raise TypeError(f'multiple values for argument{plural}: {keys}')
+
+            model_config = config_wrapper.config_dict
+
+        self.model = create_model(to_pascal(self.raw_function.__name__), __base__=DecoratorBaseModel, **fields)

.mcp-venv/lib/python3.13/site-packages/pydantic/deprecated/json.py

@@ -0,0 +1,141 @@
+import datetime
+import warnings
+from collections import deque
+from decimal import Decimal
+from enum import Enum
+from ipaddress import IPv4Address, IPv4Interface, IPv4Network, IPv6Address, IPv6Interface, IPv6Network
+from pathlib import Path
+from re import Pattern
+from types import GeneratorType
+from typing import TYPE_CHECKING, Any, Callable, Union
+from uuid import UUID
+
+from typing_extensions import deprecated
+
+from .._internal._import_utils import import_cached_base_model
+from ..color import Color
+from ..networks import NameEmail
+from ..types import SecretBytes, SecretStr
+from ..warnings import PydanticDeprecatedSince20
+
+if not TYPE_CHECKING:
+    # See PyCharm issues https://youtrack.jetbrains.com/issue/PY-21915
+    # and https://youtrack.jetbrains.com/issue/PY-51428
+    DeprecationWarning = PydanticDeprecatedSince20
+
+__all__ = 'pydantic_encoder', 'custom_pydantic_encoder', 'timedelta_isoformat'
+
+
+def isoformat(o: Union[datetime.date, datetime.time]) -> str:
+    return o.isoformat()
+
+
+def decimal_encoder(dec_value: Decimal) -> Union[int, float]:
+    """Encodes a Decimal as int of there's no exponent, otherwise float.
+
+    This is useful when we use ConstrainedDecimal to represent Numeric(x,0)
+    where a integer (but not int typed) is used. Encoding this as a float
+    results in failed round-tripping between encode and parse.
+    Our Id type is a prime example of this.
+
+    >>> decimal_encoder(Decimal("1.0"))
+    1.0
+
+    >>> decimal_encoder(Decimal("1"))
+    1
+    """
+    exponent = dec_value.as_tuple().exponent
+    if isinstance(exponent, int) and exponent >= 0:
+        return int(dec_value)
+    else:
+        return float(dec_value)
+
+
+ENCODERS_BY_TYPE: dict[type[Any], Callable[[Any], Any]] = {
+    bytes: lambda o: o.decode(),
+    Color: str,
+    datetime.date: isoformat,
+    datetime.datetime: isoformat,
+    datetime.time: isoformat,
+    datetime.timedelta: lambda td: td.total_seconds(),
+    Decimal: decimal_encoder,
+    Enum: lambda o: o.value,
+    frozenset: list,
+    deque: list,
+    GeneratorType: list,
+    IPv4Address: str,
+    IPv4Interface: str,
+    IPv4Network: str,
+    IPv6Address: str,
+    IPv6Interface: str,
+    IPv6Network: str,
+    NameEmail: str,
+    Path: str,
+    Pattern: lambda o: o.pattern,
+    SecretBytes: str,
+    SecretStr: str,
+    set: list,
+    UUID: str,
+}
+
+
+@deprecated(
+    '`pydantic_encoder` is deprecated, use `pydantic_core.to_jsonable_python` instead.',
+    category=None,
+)
+def pydantic_encoder(obj: Any) -> Any:
+    warnings.warn(
+        '`pydantic_encoder` is deprecated, use `pydantic_core.to_jsonable_python` instead.',
+        category=PydanticDeprecatedSince20,
+        stacklevel=2,
+    )
+    from dataclasses import asdict, is_dataclass
+
+    BaseModel = import_cached_base_model()
+
+    if isinstance(obj, BaseModel):
+        return obj.model_dump()
+    elif is_dataclass(obj):
+        return asdict(obj)  # type: ignore
+
+    # Check the class type and its superclasses for a matching encoder
+    for base in obj.__class__.__mro__[:-1]:
+        try:
+            encoder = ENCODERS_BY_TYPE[base]
+        except KeyError:
+            continue
+        return encoder(obj)
+    else:  # We have exited the for loop without finding a suitable encoder
+        raise TypeError(f"Object of type '{obj.__class__.__name__}' is not JSON serializable")
+
+
+# TODO: Add a suggested migration path once there is a way to use custom encoders
+@deprecated(
+    '`custom_pydantic_encoder` is deprecated, use `BaseModel.model_dump` instead.',
+    category=None,
+)
+def custom_pydantic_encoder(type_encoders: dict[Any, Callable[[type[Any]], Any]], obj: Any) -> Any:
+    warnings.warn(
+        '`custom_pydantic_encoder` is deprecated, use `BaseModel.model_dump` instead.',
+        category=PydanticDeprecatedSince20,
+        stacklevel=2,
+    )
+    # Check the class type and its superclasses for a matching encoder
+    for base in obj.__class__.__mro__[:-1]:
+        try:
+            encoder = type_encoders[base]
+        except KeyError:
+            continue
+
+        return encoder(obj)
+    else:  # We have exited the for loop without finding a suitable encoder
+        return pydantic_encoder(obj)
+
+
+@deprecated('`timedelta_isoformat` is deprecated.', category=None)
+def timedelta_isoformat(td: datetime.timedelta) -> str:
+    """ISO 8601 encoding for Python timedelta object."""
+    warnings.warn('`timedelta_isoformat` is deprecated.', category=PydanticDeprecatedSince20, stacklevel=2)
+    minutes, seconds = divmod(td.seconds, 60)
+    hours, minutes = divmod(minutes, 60)
+    return f'{"-" if td.days < 0 else ""}P{abs(td.days)}DT{hours:d}H{minutes:d}M{seconds:d}.{td.microseconds:06d}S'

.mcp-venv/lib/python3.13/site-packages/pydantic/deprecated/parse.py

@@ -0,0 +1,80 @@
+from __future__ import annotations
+
+import json
+import pickle
+import warnings
+from enum import Enum
+from pathlib import Path
+from typing import TYPE_CHECKING, Any, Callable
+
+from typing_extensions import deprecated
+
+from ..warnings import PydanticDeprecatedSince20
+
+if not TYPE_CHECKING:
+    # See PyCharm issues https://youtrack.jetbrains.com/issue/PY-21915
+    # and https://youtrack.jetbrains.com/issue/PY-51428
+    DeprecationWarning = PydanticDeprecatedSince20
+
+
+class Protocol(str, Enum):
+    json = 'json'
+    pickle = 'pickle'
+
+
+@deprecated('`load_str_bytes` is deprecated.', category=None)
+def load_str_bytes(
+    b: str | bytes,
+    *,
+    content_type: str | None = None,
+    encoding: str = 'utf8',
+    proto: Protocol | None = None,
+    allow_pickle: bool = False,
+    json_loads: Callable[[str], Any] = json.loads,
+) -> Any:
+    warnings.warn('`load_str_bytes` is deprecated.', category=PydanticDeprecatedSince20, stacklevel=2)
+    if proto is None and content_type:
+        if content_type.endswith(('json', 'javascript')):
+            pass
+        elif allow_pickle and content_type.endswith('pickle'):
+            proto = Protocol.pickle
+        else:
+            raise TypeError(f'Unknown content-type: {content_type}')
+
+    proto = proto or Protocol.json
+
+    if proto == Protocol.json:
+        if isinstance(b, bytes):
+            b = b.decode(encoding)
+        return json_loads(b)  # type: ignore
+    elif proto == Protocol.pickle:
+        if not allow_pickle:
+            raise RuntimeError('Trying to decode with pickle with allow_pickle=False')
+        bb = b if isinstance(b, bytes) else b.encode()  # type: ignore
+        return pickle.loads(bb)
+    else:
+        raise TypeError(f'Unknown protocol: {proto}')
+
+
+@deprecated('`load_file` is deprecated.', category=None)
+def load_file(
+    path: str | Path,
+    *,
+    content_type: str | None = None,
+    encoding: str = 'utf8',
+    proto: Protocol | None = None,
+    allow_pickle: bool = False,
+    json_loads: Callable[[str], Any] = json.loads,
+) -> Any:
+    warnings.warn('`load_file` is deprecated.', category=PydanticDeprecatedSince20, stacklevel=2)
+    path = Path(path)
+    b = path.read_bytes()
+    if content_type is None:
+        if path.suffix in ('.js', '.json'):
+            proto = Protocol.json
+        elif path.suffix == '.pkl':
+            proto = Protocol.pickle
+
+    return load_str_bytes(
+        b, proto=proto, content_type=content_type, encoding=encoding, allow_pickle=allow_pickle, json_loads=json_loads
+    )

.mcp-venv/lib/python3.13/site-packages/pydantic/deprecated/tools.py

@@ -0,0 +1,103 @@
+from __future__ import annotations
+
+import json
+import warnings
+from typing import TYPE_CHECKING, Any, Callable, TypeVar, Union
+
+from typing_extensions import deprecated
+
+from ..json_schema import DEFAULT_REF_TEMPLATE, GenerateJsonSchema
+from ..type_adapter import TypeAdapter
+from ..warnings import PydanticDeprecatedSince20
+
+if not TYPE_CHECKING:
+    # See PyCharm issues https://youtrack.jetbrains.com/issue/PY-21915
+    # and https://youtrack.jetbrains.com/issue/PY-51428
+    DeprecationWarning = PydanticDeprecatedSince20
+
+__all__ = 'parse_obj_as', 'schema_of', 'schema_json_of'
+
+NameFactory = Union[str, Callable[[type[Any]], str]]
+
+
+T = TypeVar('T')
+
+
+@deprecated(
+    '`parse_obj_as` is deprecated. Use `pydantic.TypeAdapter.validate_python` instead.',
+    category=None,
+)
+def parse_obj_as(type_: type[T], obj: Any, type_name: NameFactory | None = None) -> T:
+    warnings.warn(
+        '`parse_obj_as` is deprecated. Use `pydantic.TypeAdapter.validate_python` instead.',
+        category=PydanticDeprecatedSince20,
+        stacklevel=2,
+    )
+    if type_name is not None:  # pragma: no cover
+        warnings.warn(
+            'The type_name parameter is deprecated. parse_obj_as no longer creates temporary models',
+            DeprecationWarning,
+            stacklevel=2,
+        )
+    return TypeAdapter(type_).validate_python(obj)
+
+
+@deprecated(
+    '`schema_of` is deprecated. Use `pydantic.TypeAdapter.json_schema` instead.',
+    category=None,
+)
+def schema_of(
+    type_: Any,
+    *,
+    title: NameFactory | None = None,
+    by_alias: bool = True,
+    ref_template: str = DEFAULT_REF_TEMPLATE,
+    schema_generator: type[GenerateJsonSchema] = GenerateJsonSchema,
+) -> dict[str, Any]:
+    """Generate a JSON schema (as dict) for the passed model or dynamically generated one."""
+    warnings.warn(
+        '`schema_of` is deprecated. Use `pydantic.TypeAdapter.json_schema` instead.',
+        category=PydanticDeprecatedSince20,
+        stacklevel=2,
+    )
+    res = TypeAdapter(type_).json_schema(
+        by_alias=by_alias,
+        schema_generator=schema_generator,
+        ref_template=ref_template,
+    )
+    if title is not None:
+        if isinstance(title, str):
+            res['title'] = title
+        else:
+            warnings.warn(
+                'Passing a callable for the `title` parameter is deprecated and no longer supported',
+                DeprecationWarning,
+                stacklevel=2,
+            )
+            res['title'] = title(type_)
+    return res
+
+
+@deprecated(
+    '`schema_json_of` is deprecated. Use `pydantic.TypeAdapter.json_schema` instead.',
+    category=None,
+)
+def schema_json_of(
+    type_: Any,
+    *,
+    title: NameFactory | None = None,
+    by_alias: bool = True,
+    ref_template: str = DEFAULT_REF_TEMPLATE,
+    schema_generator: type[GenerateJsonSchema] = GenerateJsonSchema,
+    **dumps_kwargs: Any,
+) -> str:
+    """Generate a JSON schema (as JSON) for the passed model or dynamically generated one."""
+    warnings.warn(
+        '`schema_json_of` is deprecated. Use `pydantic.TypeAdapter.json_schema` instead.',
+        category=PydanticDeprecatedSince20,
+        stacklevel=2,
+    )
+    return json.dumps(
+        schema_of(type_, title=title, by_alias=by_alias, ref_template=ref_template, schema_generator=schema_generator),
+        **dumps_kwargs,
+    )

.mcp-venv/lib/python3.13/site-packages/pydantic/env_settings.py

@@ -0,0 +1,5 @@
+"""The `env_settings` module is a backport module from V1."""
+
+from ._migration import getattr_migration
+
+__getattr__ = getattr_migration(__name__)

.mcp-venv/lib/python3.13/site-packages/pydantic/error_wrappers.py

@@ -0,0 +1,5 @@
+"""The `error_wrappers` module is a backport module from V1."""
+
+from ._migration import getattr_migration
+
+__getattr__ = getattr_migration(__name__)

.mcp-venv/lib/python3.13/site-packages/pydantic/errors.py

@@ -0,0 +1,189 @@
+"""Pydantic-specific errors."""
+
+from __future__ import annotations as _annotations
+
+import re
+from typing import Any, ClassVar, Literal
+
+from typing_extensions import Self
+from typing_inspection.introspection import Qualifier
+
+from pydantic._internal import _repr
+
+from ._migration import getattr_migration
+from .version import version_short
+
+__all__ = (
+    'PydanticUserError',
+    'PydanticUndefinedAnnotation',
+    'PydanticImportError',
+    'PydanticSchemaGenerationError',
+    'PydanticInvalidForJsonSchema',
+    'PydanticForbiddenQualifier',
+    'PydanticErrorCodes',
+)
+
+# We use this URL to allow for future flexibility about how we host the docs, while allowing for Pydantic
+# code in the while with "old" URLs to still work.
+# 'u' refers to "user errors" - e.g. errors caused by developers using pydantic, as opposed to validation errors.
+DEV_ERROR_DOCS_URL = f'https://errors.pydantic.dev/{version_short()}/u/'
+PydanticErrorCodes = Literal[
+    'class-not-fully-defined',
+    'custom-json-schema',
+    'decorator-invalid-fields',
+    'decorator-missing-arguments',
+    'decorator-missing-field',
+    'discriminator-no-field',
+    'discriminator-alias-type',
+    'discriminator-needs-literal',
+    'discriminator-alias',
+    'discriminator-validator',
+    'callable-discriminator-no-tag',
+    'typed-dict-version',
+    'model-field-overridden',
+    'model-field-missing-annotation',
+    'config-both',
+    'removed-kwargs',
+    'circular-reference-schema',
+    'invalid-for-json-schema',
+    'json-schema-already-used',
+    'base-model-instantiated',
+    'undefined-annotation',
+    'schema-for-unknown-type',
+    'import-error',
+    'create-model-field-definitions',
+    'validator-instance-method',
+    'validator-input-type',
+    'root-validator-pre-skip',
+    'model-serializer-instance-method',
+    'validator-field-config-info',
+    'validator-v1-signature',
+    'validator-signature',
+    'field-serializer-signature',
+    'model-serializer-signature',
+    'multiple-field-serializers',
+    'invalid-annotated-type',
+    'type-adapter-config-unused',
+    'root-model-extra',
+    'unevaluable-type-annotation',
+    'dataclass-init-false-extra-allow',
+    'clashing-init-and-init-var',
+    'model-config-invalid-field-name',
+    'with-config-on-model',
+    'dataclass-on-model',
+    'validate-call-type',
+    'unpack-typed-dict',
+    'overlapping-unpack-typed-dict',
+    'invalid-self-type',
+    'validate-by-alias-and-name-false',
+]
+
+
+class PydanticErrorMixin:
+    """A mixin class for common functionality shared by all Pydantic-specific errors.
+
+    Attributes:
+        message: A message describing the error.
+        code: An optional error code from PydanticErrorCodes enum.
+    """
+
+    def __init__(self, message: str, *, code: PydanticErrorCodes | None) -> None:
+        self.message = message
+        self.code = code
+
+    def __str__(self) -> str:
+        if self.code is None:
+            return self.message
+        else:
+            return f'{self.message}\n\nFor further information visit {DEV_ERROR_DOCS_URL}{self.code}'
+
+
+class PydanticUserError(PydanticErrorMixin, RuntimeError):
+    """An error raised due to incorrect use of Pydantic."""
+
+
+class PydanticUndefinedAnnotation(PydanticErrorMixin, NameError):
+    """A subclass of `NameError` raised when handling undefined annotations during `CoreSchema` generation.
+
+    Attributes:
+        name: Name of the error.
+        message: Description of the error.
+    """
+
+    def __init__(self, name: str, message: str) -> None:
+        self.name = name
+        super().__init__(message=message, code='undefined-annotation')
+
+    @classmethod
+    def from_name_error(cls, name_error: NameError) -> Self:
+        """Convert a `NameError` to a `PydanticUndefinedAnnotation` error.
+
+        Args:
+            name_error: `NameError` to be converted.
+
+        Returns:
+            Converted `PydanticUndefinedAnnotation` error.
+        """
+        try:
+            name = name_error.name  # type: ignore  # python > 3.10
+        except AttributeError:
+            name = re.search(r".*'(.+?)'", str(name_error)).group(1)  # type: ignore[union-attr]
+        return cls(name=name, message=str(name_error))
+
+
+class PydanticImportError(PydanticErrorMixin, ImportError):
+    """An error raised when an import fails due to module changes between V1 and V2.
+
+    Attributes:
+        message: Description of the error.
+    """
+
+    def __init__(self, message: str) -> None:
+        super().__init__(message, code='import-error')
+
+
+class PydanticSchemaGenerationError(PydanticUserError):
+    """An error raised during failures to generate a `CoreSchema` for some type.
+
+    Attributes:
+        message: Description of the error.
+    """
+
+    def __init__(self, message: str) -> None:
+        super().__init__(message, code='schema-for-unknown-type')
+
+
+class PydanticInvalidForJsonSchema(PydanticUserError):
+    """An error raised during failures to generate a JSON schema for some `CoreSchema`.
+
+    Attributes:
+        message: Description of the error.
+    """
+
+    def __init__(self, message: str) -> None:
+        super().__init__(message, code='invalid-for-json-schema')
+
+
+class PydanticForbiddenQualifier(PydanticUserError):
+    """An error raised if a forbidden type qualifier is found in a type annotation."""
+
+    _qualifier_repr_map: ClassVar[dict[Qualifier, str]] = {
+        'required': 'typing.Required',
+        'not_required': 'typing.NotRequired',
+        'read_only': 'typing.ReadOnly',
+        'class_var': 'typing.ClassVar',
+        'init_var': 'dataclasses.InitVar',
+        'final': 'typing.Final',
+    }
+
+    def __init__(self, qualifier: Qualifier, annotation: Any) -> None:
+        super().__init__(
+            message=(
+                f'The annotation {_repr.display_as_type(annotation)!r} contains the {self._qualifier_repr_map[qualifier]!r} '
+                f'type qualifier, which is invalid in the context it is defined.'
+            ),
+            code=None,
+        )
+
+
+__getattr__ = getattr_migration(__name__)

.mcp-venv/lib/python3.13/site-packages/pydantic/experimental/__init__.py

@@ -0,0 +1 @@
+"""The "experimental" module of pydantic contains potential new features that are subject to change."""

.mcp-venv/lib/python3.13/site-packages/pydantic/experimental/arguments_schema.py

@@ -0,0 +1,44 @@
+"""Experimental module exposing a function to generate a core schema that validates callable arguments."""
+
+from __future__ import annotations
+
+from collections.abc import Callable
+from typing import Any, Literal
+
+from pydantic_core import CoreSchema
+
+from pydantic import ConfigDict
+from pydantic._internal import _config, _generate_schema, _namespace_utils
+
+
+def generate_arguments_schema(
+    func: Callable[..., Any],
+    schema_type: Literal['arguments', 'arguments-v3'] = 'arguments-v3',
+    parameters_callback: Callable[[int, str, Any], Literal['skip'] | None] | None = None,
+    config: ConfigDict | None = None,
+) -> CoreSchema:
+    """Generate the schema for the arguments of a function.
+
+    Args:
+        func: The function to generate the schema for.
+        schema_type: The type of schema to generate.
+        parameters_callback: A callable that will be invoked for each parameter. The callback
+            should take three required arguments: the index, the name and the type annotation
+            (or [`Parameter.empty`][inspect.Parameter.empty] if not annotated) of the parameter.
+            The callback can optionally return `'skip'`, so that the parameter gets excluded
+            from the resulting schema.
+        config: The configuration to use.
+
+    Returns:
+        The generated schema.
+    """
+    generate_schema = _generate_schema.GenerateSchema(
+        _config.ConfigWrapper(config),
+        ns_resolver=_namespace_utils.NsResolver(namespaces_tuple=_namespace_utils.ns_for_function(func)),
+    )
+
+    if schema_type == 'arguments':
+        schema = generate_schema._arguments_schema(func, parameters_callback)  # pyright: ignore[reportArgumentType]
+    else:
+        schema = generate_schema._arguments_v3_schema(func, parameters_callback)  # pyright: ignore[reportArgumentType]
+    return generate_schema.clean_schema(schema)

.mcp-venv/lib/python3.13/site-packages/pydantic/experimental/missing_sentinel.py

@@ -0,0 +1,5 @@
+"""Experimental module exposing a function a `MISSING` sentinel."""
+
+from pydantic_core import MISSING
+
+__all__ = ('MISSING',)

.mcp-venv/lib/python3.13/site-packages/pydantic/experimental/pipeline.py

@@ -0,0 +1,663 @@
+"""Experimental pipeline API functionality. Be careful with this API, it's subject to change."""
+
+from __future__ import annotations
+
+import datetime
+import operator
+import re
+import sys
+from collections import deque
+from collections.abc import Container
+from dataclasses import dataclass
+from decimal import Decimal
+from functools import cached_property, partial
+from re import Pattern
+from typing import TYPE_CHECKING, Annotated, Any, Callable, Generic, Protocol, TypeVar, Union, overload
+
+import annotated_types
+
+if TYPE_CHECKING:
+    from pydantic import GetCoreSchemaHandler
+
+from pydantic_core import PydanticCustomError
+from pydantic_core import core_schema as cs
+
+from pydantic import Strict
+from pydantic._internal._internal_dataclass import slots_true as _slots_true
+
+if sys.version_info < (3, 10):
+    EllipsisType = type(Ellipsis)
+else:
+    from types import EllipsisType
+
+__all__ = ['validate_as', 'validate_as_deferred', 'transform']
+
+_slots_frozen = {**_slots_true, 'frozen': True}
+
+
+@dataclass(**_slots_frozen)
+class _ValidateAs:
+    tp: type[Any]
+    strict: bool = False
+
+
+@dataclass
+class _ValidateAsDefer:
+    func: Callable[[], type[Any]]
+
+    @cached_property
+    def tp(self) -> type[Any]:
+        return self.func()
+
+
+@dataclass(**_slots_frozen)
+class _Transform:
+    func: Callable[[Any], Any]
+
+
+@dataclass(**_slots_frozen)
+class _PipelineOr:
+    left: _Pipeline[Any, Any]
+    right: _Pipeline[Any, Any]
+
+
+@dataclass(**_slots_frozen)
+class _PipelineAnd:
+    left: _Pipeline[Any, Any]
+    right: _Pipeline[Any, Any]
+
+
+@dataclass(**_slots_frozen)
+class _Eq:
+    value: Any
+
+
+@dataclass(**_slots_frozen)
+class _NotEq:
+    value: Any
+
+
+@dataclass(**_slots_frozen)
+class _In:
+    values: Container[Any]
+
+
+@dataclass(**_slots_frozen)
+class _NotIn:
+    values: Container[Any]
+
+
+_ConstraintAnnotation = Union[
+    annotated_types.Le,
+    annotated_types.Ge,
+    annotated_types.Lt,
+    annotated_types.Gt,
+    annotated_types.Len,
+    annotated_types.MultipleOf,
+    annotated_types.Timezone,
+    annotated_types.Interval,
+    annotated_types.Predicate,
+    # common predicates not included in annotated_types
+    _Eq,
+    _NotEq,
+    _In,
+    _NotIn,
+    # regular expressions
+    Pattern[str],
+]
+
+
+@dataclass(**_slots_frozen)
+class _Constraint:
+    constraint: _ConstraintAnnotation
+
+
+_Step = Union[_ValidateAs, _ValidateAsDefer, _Transform, _PipelineOr, _PipelineAnd, _Constraint]
+
+_InT = TypeVar('_InT')
+_OutT = TypeVar('_OutT')
+_NewOutT = TypeVar('_NewOutT')
+
+
+class _FieldTypeMarker:
+    pass
+
+
+# TODO: ultimately, make this public, see https://github.com/pydantic/pydantic/pull/9459#discussion_r1628197626
+# Also, make this frozen eventually, but that doesn't work right now because of the generic base
+# Which attempts to modify __orig_base__ and such.
+# We could go with a manual freeze, but that seems overkill for now.
+@dataclass(**_slots_true)
+class _Pipeline(Generic[_InT, _OutT]):
+    """Abstract representation of a chain of validation, transformation, and parsing steps."""
+
+    _steps: tuple[_Step, ...]
+
+    def transform(
+        self,
+        func: Callable[[_OutT], _NewOutT],
+    ) -> _Pipeline[_InT, _NewOutT]:
+        """Transform the output of the previous step.
+
+        If used as the first step in a pipeline, the type of the field is used.
+        That is, the transformation is applied to after the value is parsed to the field's type.
+        """
+        return _Pipeline[_InT, _NewOutT](self._steps + (_Transform(func),))
+
+    @overload
+    def validate_as(self, tp: type[_NewOutT], *, strict: bool = False) -> _Pipeline[_InT, _NewOutT]: ...
+
+    @overload
+    def validate_as(
+        self,
+        tp: ellipsis,  # noqa: F821  # TODO: use `_typing_extra.EllipsisType` when we drop Py3.9
+        *,
+        strict: bool = False,
+    ) -> _Pipeline[_InT, Any]: ...
+
+    # TODO PEP 747: use TypeForm to properly type Annotated aliases (e.g. NewPath, FilePath).
+    # This fallback accepts any type expression but loses generic type inference.
+    @overload
+    def validate_as(self, tp: Any, *, strict: bool = ...) -> _Pipeline[_InT, Any]: ...
+
+    def validate_as(self, tp: type[_NewOutT] | EllipsisType | Any, *, strict: bool = False) -> _Pipeline[_InT, Any]:  # type: ignore
+        """Validate / parse the input into a new type.
+
+        If no type is provided, the type of the field is used.
+
+        Types are parsed in Pydantic's `lax` mode by default,
+        but you can enable `strict` mode by passing `strict=True`.
+        """
+        if isinstance(tp, EllipsisType):
+            return _Pipeline[_InT, Any](self._steps + (_ValidateAs(_FieldTypeMarker, strict=strict),))
+        return _Pipeline[_InT, _NewOutT](self._steps + (_ValidateAs(tp, strict=strict),))
+
+    def validate_as_deferred(self, func: Callable[[], type[_NewOutT]]) -> _Pipeline[_InT, _NewOutT]:
+        """Parse the input into a new type, deferring resolution of the type until the current class
+        is fully defined.
+
+        This is useful when you need to reference the class in it's own type annotations.
+        """
+        return _Pipeline[_InT, _NewOutT](self._steps + (_ValidateAsDefer(func),))
+
+    # constraints
+    @overload
+    def constrain(self: _Pipeline[_InT, _NewOutGe], constraint: annotated_types.Ge) -> _Pipeline[_InT, _NewOutGe]: ...
+
+    @overload
+    def constrain(self: _Pipeline[_InT, _NewOutGt], constraint: annotated_types.Gt) -> _Pipeline[_InT, _NewOutGt]: ...
+
+    @overload
+    def constrain(self: _Pipeline[_InT, _NewOutLe], constraint: annotated_types.Le) -> _Pipeline[_InT, _NewOutLe]: ...
+
+    @overload
+    def constrain(self: _Pipeline[_InT, _NewOutLt], constraint: annotated_types.Lt) -> _Pipeline[_InT, _NewOutLt]: ...
+
+    @overload
+    def constrain(
+        self: _Pipeline[_InT, _NewOutLen], constraint: annotated_types.Len
+    ) -> _Pipeline[_InT, _NewOutLen]: ...
+
+    @overload
+    def constrain(
+        self: _Pipeline[_InT, _NewOutT], constraint: annotated_types.MultipleOf
+    ) -> _Pipeline[_InT, _NewOutT]: ...
+
+    @overload
+    def constrain(
+        self: _Pipeline[_InT, _NewOutDatetime], constraint: annotated_types.Timezone
+    ) -> _Pipeline[_InT, _NewOutDatetime]: ...
+
+    @overload
+    def constrain(self: _Pipeline[_InT, _OutT], constraint: annotated_types.Predicate) -> _Pipeline[_InT, _OutT]: ...
+
+    @overload
+    def constrain(
+        self: _Pipeline[_InT, _NewOutInterval], constraint: annotated_types.Interval
+    ) -> _Pipeline[_InT, _NewOutInterval]: ...
+
+    @overload
+    def constrain(self: _Pipeline[_InT, _OutT], constraint: _Eq) -> _Pipeline[_InT, _OutT]: ...
+
+    @overload
+    def constrain(self: _Pipeline[_InT, _OutT], constraint: _NotEq) -> _Pipeline[_InT, _OutT]: ...
+
+    @overload
+    def constrain(self: _Pipeline[_InT, _OutT], constraint: _In) -> _Pipeline[_InT, _OutT]: ...
+
+    @overload
+    def constrain(self: _Pipeline[_InT, _OutT], constraint: _NotIn) -> _Pipeline[_InT, _OutT]: ...
+
+    @overload
+    def constrain(self: _Pipeline[_InT, _NewOutT], constraint: Pattern[str]) -> _Pipeline[_InT, _NewOutT]: ...
+
+    def constrain(self, constraint: _ConstraintAnnotation) -> Any:
+        """Constrain a value to meet a certain condition.
+
+        We support most conditions from `annotated_types`, as well as regular expressions.
+
+        Most of the time you'll be calling a shortcut method like `gt`, `lt`, `len`, etc
+        so you don't need to call this directly.
+        """
+        return _Pipeline[_InT, _OutT](self._steps + (_Constraint(constraint),))
+
+    def predicate(self: _Pipeline[_InT, _NewOutT], func: Callable[[_NewOutT], bool]) -> _Pipeline[_InT, _NewOutT]:
+        """Constrain a value to meet a certain predicate."""
+        return self.constrain(annotated_types.Predicate(func))
+
+    def gt(self: _Pipeline[_InT, _NewOutGt], gt: _NewOutGt) -> _Pipeline[_InT, _NewOutGt]:
+        """Constrain a value to be greater than a certain value."""
+        return self.constrain(annotated_types.Gt(gt))
+
+    def lt(self: _Pipeline[_InT, _NewOutLt], lt: _NewOutLt) -> _Pipeline[_InT, _NewOutLt]:
+        """Constrain a value to be less than a certain value."""
+        return self.constrain(annotated_types.Lt(lt))
+
+    def ge(self: _Pipeline[_InT, _NewOutGe], ge: _NewOutGe) -> _Pipeline[_InT, _NewOutGe]:
+        """Constrain a value to be greater than or equal to a certain value."""
+        return self.constrain(annotated_types.Ge(ge))
+
+    def le(self: _Pipeline[_InT, _NewOutLe], le: _NewOutLe) -> _Pipeline[_InT, _NewOutLe]:
+        """Constrain a value to be less than or equal to a certain value."""
+        return self.constrain(annotated_types.Le(le))
+
+    def len(self: _Pipeline[_InT, _NewOutLen], min_len: int, max_len: int | None = None) -> _Pipeline[_InT, _NewOutLen]:
+        """Constrain a value to have a certain length."""
+        return self.constrain(annotated_types.Len(min_len, max_len))
+
+    @overload
+    def multiple_of(self: _Pipeline[_InT, _NewOutDiv], multiple_of: _NewOutDiv) -> _Pipeline[_InT, _NewOutDiv]: ...
+
+    @overload
+    def multiple_of(self: _Pipeline[_InT, _NewOutMod], multiple_of: _NewOutMod) -> _Pipeline[_InT, _NewOutMod]: ...
+
+    def multiple_of(self: _Pipeline[_InT, Any], multiple_of: Any) -> _Pipeline[_InT, Any]:
+        """Constrain a value to be a multiple of a certain number."""
+        return self.constrain(annotated_types.MultipleOf(multiple_of))
+
+    def eq(self: _Pipeline[_InT, _OutT], value: _OutT) -> _Pipeline[_InT, _OutT]:
+        """Constrain a value to be equal to a certain value."""
+        return self.constrain(_Eq(value))
+
+    def not_eq(self: _Pipeline[_InT, _OutT], value: _OutT) -> _Pipeline[_InT, _OutT]:
+        """Constrain a value to not be equal to a certain value."""
+        return self.constrain(_NotEq(value))
+
+    def in_(self: _Pipeline[_InT, _OutT], values: Container[_OutT]) -> _Pipeline[_InT, _OutT]:
+        """Constrain a value to be in a certain set."""
+        return self.constrain(_In(values))
+
+    def not_in(self: _Pipeline[_InT, _OutT], values: Container[_OutT]) -> _Pipeline[_InT, _OutT]:
+        """Constrain a value to not be in a certain set."""
+        return self.constrain(_NotIn(values))
+
+    # timezone methods
+    def datetime_tz_naive(self: _Pipeline[_InT, datetime.datetime]) -> _Pipeline[_InT, datetime.datetime]:
+        return self.constrain(annotated_types.Timezone(None))
+
+    def datetime_tz_aware(self: _Pipeline[_InT, datetime.datetime]) -> _Pipeline[_InT, datetime.datetime]:
+        return self.constrain(annotated_types.Timezone(...))
+
+    def datetime_tz(
+        self: _Pipeline[_InT, datetime.datetime], tz: datetime.tzinfo
+    ) -> _Pipeline[_InT, datetime.datetime]:
+        return self.constrain(annotated_types.Timezone(tz))  # type: ignore
+
+    def datetime_with_tz(
+        self: _Pipeline[_InT, datetime.datetime], tz: datetime.tzinfo | None
+    ) -> _Pipeline[_InT, datetime.datetime]:
+        return self.transform(partial(datetime.datetime.replace, tzinfo=tz))
+
+    # string methods
+    def str_lower(self: _Pipeline[_InT, str]) -> _Pipeline[_InT, str]:
+        return self.transform(str.lower)
+
+    def str_upper(self: _Pipeline[_InT, str]) -> _Pipeline[_InT, str]:
+        return self.transform(str.upper)
+
+    def str_title(self: _Pipeline[_InT, str]) -> _Pipeline[_InT, str]:
+        return self.transform(str.title)
+
+    def str_strip(self: _Pipeline[_InT, str]) -> _Pipeline[_InT, str]:
+        return self.transform(str.strip)
+
+    def str_pattern(self: _Pipeline[_InT, str], pattern: str) -> _Pipeline[_InT, str]:
+        return self.constrain(re.compile(pattern))
+
+    def str_contains(self: _Pipeline[_InT, str], substring: str) -> _Pipeline[_InT, str]:
+        return self.predicate(lambda v: substring in v)
+
+    def str_starts_with(self: _Pipeline[_InT, str], prefix: str) -> _Pipeline[_InT, str]:
+        return self.predicate(lambda v: v.startswith(prefix))
+
+    def str_ends_with(self: _Pipeline[_InT, str], suffix: str) -> _Pipeline[_InT, str]:
+        return self.predicate(lambda v: v.endswith(suffix))
+
+    # operators
+    def otherwise(self, other: _Pipeline[_OtherIn, _OtherOut]) -> _Pipeline[_InT | _OtherIn, _OutT | _OtherOut]:
+        """Combine two validation chains, returning the result of the first chain if it succeeds, and the second chain if it fails."""
+        return _Pipeline((_PipelineOr(self, other),))
+
+    __or__ = otherwise
+
+    def then(self, other: _Pipeline[_OutT, _OtherOut]) -> _Pipeline[_InT, _OtherOut]:
+        """Pipe the result of one validation chain into another."""
+        return _Pipeline((_PipelineAnd(self, other),))
+
+    __and__ = then
+
+    def __get_pydantic_core_schema__(self, source_type: Any, handler: GetCoreSchemaHandler) -> cs.CoreSchema:
+        queue = deque(self._steps)
+
+        s = None
+
+        while queue:
+            step = queue.popleft()
+            s = _apply_step(step, s, handler, source_type)
+
+        s = s or cs.any_schema()
+        return s
+
+    def __supports_type__(self, _: _OutT) -> bool:
+        raise NotImplementedError
+
+
+validate_as = _Pipeline[Any, Any](()).validate_as
+validate_as_deferred = _Pipeline[Any, Any](()).validate_as_deferred
+transform = _Pipeline[Any, Any]((_ValidateAs(_FieldTypeMarker),)).transform
+
+
+def _check_func(
+    func: Callable[[Any], bool], predicate_err: str | Callable[[], str], s: cs.CoreSchema | None
+) -> cs.CoreSchema:
+    def handler(v: Any) -> Any:
+        if func(v):
+            return v
+        raise ValueError(f'Expected {predicate_err if isinstance(predicate_err, str) else predicate_err()}')
+
+    if s is None:
+        return cs.no_info_plain_validator_function(handler)
+    else:
+        return cs.no_info_after_validator_function(handler, s)
+
+
+def _apply_step(step: _Step, s: cs.CoreSchema | None, handler: GetCoreSchemaHandler, source_type: Any) -> cs.CoreSchema:
+    if isinstance(step, _ValidateAs):
+        s = _apply_parse(s, step.tp, step.strict, handler, source_type)
+    elif isinstance(step, _ValidateAsDefer):
+        s = _apply_parse(s, step.tp, False, handler, source_type)
+    elif isinstance(step, _Transform):
+        s = _apply_transform(s, step.func, handler)
+    elif isinstance(step, _Constraint):
+        s = _apply_constraint(s, step.constraint)
+    elif isinstance(step, _PipelineOr):
+        s = cs.union_schema([handler(step.left), handler(step.right)])
+    else:
+        assert isinstance(step, _PipelineAnd)
+        s = cs.chain_schema([handler(step.left), handler(step.right)])
+    return s
+
+
+def _apply_parse(
+    s: cs.CoreSchema | None,
+    tp: type[Any],
+    strict: bool,
+    handler: GetCoreSchemaHandler,
+    source_type: Any,
+) -> cs.CoreSchema:
+    if tp is _FieldTypeMarker:
+        return cs.chain_schema([s, handler(source_type)]) if s else handler(source_type)
+
+    if strict:
+        tp = Annotated[tp, Strict()]  # type: ignore
+
+    if s and s['type'] == 'any':
+        return handler(tp)
+    else:
+        return cs.chain_schema([s, handler(tp)]) if s else handler(tp)
+
+
+def _apply_transform(
+    s: cs.CoreSchema | None, func: Callable[[Any], Any], handler: GetCoreSchemaHandler
+) -> cs.CoreSchema:
+    if s is None:
+        return cs.no_info_plain_validator_function(func)
+
+    if s['type'] == 'str':
+        if func is str.strip:
+            s = s.copy()
+            s['strip_whitespace'] = True
+            return s
+        elif func is str.lower:
+            s = s.copy()
+            s['to_lower'] = True
+            return s
+        elif func is str.upper:
+            s = s.copy()
+            s['to_upper'] = True
+            return s
+
+    return cs.no_info_after_validator_function(func, s)
+
+
+def _apply_constraint(  # noqa: C901
+    s: cs.CoreSchema | None, constraint: _ConstraintAnnotation
+) -> cs.CoreSchema:
+    """Apply a single constraint to a schema."""
+    if isinstance(constraint, annotated_types.Gt):
+        gt = constraint.gt
+        if s and s['type'] in {'int', 'float', 'decimal'}:
+            s = s.copy()
+            if s['type'] == 'int' and isinstance(gt, int):
+                s['gt'] = gt
+            elif s['type'] == 'float' and isinstance(gt, float):
+                s['gt'] = gt
+            elif s['type'] == 'decimal' and isinstance(gt, Decimal):
+                s['gt'] = gt
+        else:
+
+            def check_gt(v: Any) -> bool:
+                return v > gt
+
+            s = _check_func(check_gt, f'> {gt}', s)
+    elif isinstance(constraint, annotated_types.Ge):
+        ge = constraint.ge
+        if s and s['type'] in {'int', 'float', 'decimal'}:
+            s = s.copy()
+            if s['type'] == 'int' and isinstance(ge, int):
+                s['ge'] = ge
+            elif s['type'] == 'float' and isinstance(ge, float):
+                s['ge'] = ge
+            elif s['type'] == 'decimal' and isinstance(ge, Decimal):
+                s['ge'] = ge
+
+        def check_ge(v: Any) -> bool:
+            return v >= ge
+
+        s = _check_func(check_ge, f'>= {ge}', s)
+    elif isinstance(constraint, annotated_types.Lt):
+        lt = constraint.lt
+        if s and s['type'] in {'int', 'float', 'decimal'}:
+            s = s.copy()
+            if s['type'] == 'int' and isinstance(lt, int):
+                s['lt'] = lt
+            elif s['type'] == 'float' and isinstance(lt, float):
+                s['lt'] = lt
+            elif s['type'] == 'decimal' and isinstance(lt, Decimal):
+                s['lt'] = lt
+
+        def check_lt(v: Any) -> bool:
+            return v < lt
+
+        s = _check_func(check_lt, f'< {lt}', s)
+    elif isinstance(constraint, annotated_types.Le):
+        le = constraint.le
+        if s and s['type'] in {'int', 'float', 'decimal'}:
+            s = s.copy()
+            if s['type'] == 'int' and isinstance(le, int):
+                s['le'] = le
+            elif s['type'] == 'float' and isinstance(le, float):
+                s['le'] = le
+            elif s['type'] == 'decimal' and isinstance(le, Decimal):
+                s['le'] = le
+
+        def check_le(v: Any) -> bool:
+            return v <= le
+
+        s = _check_func(check_le, f'<= {le}', s)
+    elif isinstance(constraint, annotated_types.Len):
+        min_len = constraint.min_length
+        max_len = constraint.max_length
+
+        if s and s['type'] in {'str', 'list', 'tuple', 'set', 'frozenset', 'dict'}:
+            assert (
+                s['type'] == 'str'
+                or s['type'] == 'list'
+                or s['type'] == 'tuple'
+                or s['type'] == 'set'
+                or s['type'] == 'dict'
+                or s['type'] == 'frozenset'
+            )
+            s = s.copy()
+            if min_len != 0:
+                s['min_length'] = min_len
+            if max_len is not None:
+                s['max_length'] = max_len
+
+        def check_len(v: Any) -> bool:
+            if max_len is not None:
+                return (min_len <= len(v)) and (len(v) <= max_len)
+            return min_len <= len(v)
+
+        s = _check_func(check_len, f'length >= {min_len} and length <= {max_len}', s)
+    elif isinstance(constraint, annotated_types.MultipleOf):
+        multiple_of = constraint.multiple_of
+        if s and s['type'] in {'int', 'float', 'decimal'}:
+            s = s.copy()
+            if s['type'] == 'int' and isinstance(multiple_of, int):
+                s['multiple_of'] = multiple_of
+            elif s['type'] == 'float' and isinstance(multiple_of, float):
+                s['multiple_of'] = multiple_of
+            elif s['type'] == 'decimal' and isinstance(multiple_of, Decimal):
+                s['multiple_of'] = multiple_of
+
+        def check_multiple_of(v: Any) -> bool:
+            return v % multiple_of == 0
+
+        s = _check_func(check_multiple_of, f'% {multiple_of} == 0', s)
+    elif isinstance(constraint, annotated_types.Timezone):
+        tz = constraint.tz
+
+        if tz is ...:
+            if s and s['type'] == 'datetime':
+                s = s.copy()
+                s['tz_constraint'] = 'aware'
+            else:
+
+                def check_tz_aware(v: object) -> bool:
+                    assert isinstance(v, datetime.datetime)
+                    return v.tzinfo is not None
+
+                s = _check_func(check_tz_aware, 'timezone aware', s)
+        elif tz is None:
+            if s and s['type'] == 'datetime':
+                s = s.copy()
+                s['tz_constraint'] = 'naive'
+            else:
+
+                def check_tz_naive(v: object) -> bool:
+                    assert isinstance(v, datetime.datetime)
+                    return v.tzinfo is None
+
+                s = _check_func(check_tz_naive, 'timezone naive', s)
+        else:
+            raise NotImplementedError('Constraining to a specific timezone is not yet supported')
+    elif isinstance(constraint, annotated_types.Interval):
+        if constraint.ge:
+            s = _apply_constraint(s, annotated_types.Ge(constraint.ge))
+        if constraint.gt:
+            s = _apply_constraint(s, annotated_types.Gt(constraint.gt))
+        if constraint.le:
+            s = _apply_constraint(s, annotated_types.Le(constraint.le))
+        if constraint.lt:
+            s = _apply_constraint(s, annotated_types.Lt(constraint.lt))
+        assert s is not None
+    elif isinstance(constraint, annotated_types.Predicate):
+        func = constraint.func
+        # Same logic as in `_known_annotated_metadata.apply_known_metadata()`:
+        predicate_name = f'{func.__qualname__!r} ' if hasattr(func, '__qualname__') else ''
+
+        def predicate_func(v: Any) -> Any:
+            if not func(v):
+                raise PydanticCustomError(
+                    'predicate_failed',
+                    f'Predicate {predicate_name}failed',  # pyright: ignore[reportArgumentType]
+                )
+            return v
+
+        if s is None:
+            s = cs.no_info_plain_validator_function(predicate_func)
+        else:
+            s = cs.no_info_after_validator_function(predicate_func, s)
+    elif isinstance(constraint, _NotEq):
+        value = constraint.value
+
+        def check_not_eq(v: Any) -> bool:
+            return operator.__ne__(v, value)
+
+        s = _check_func(check_not_eq, f'!= {value}', s)
+    elif isinstance(constraint, _Eq):
+        value = constraint.value
+
+        def check_eq(v: Any) -> bool:
+            return operator.__eq__(v, value)
+
+        s = _check_func(check_eq, f'== {value}', s)
+    elif isinstance(constraint, _In):
+        values = constraint.values
+
+        def check_in(v: Any) -> bool:
+            return operator.__contains__(values, v)
+
+        s = _check_func(check_in, f'in {values}', s)
+    elif isinstance(constraint, _NotIn):
+        values = constraint.values
+
+        def check_not_in(v: Any) -> bool:
+            return operator.__not__(operator.__contains__(values, v))
+
+        s = _check_func(check_not_in, f'not in {values}', s)
+    else:
+        assert isinstance(constraint, Pattern)
+        if s and s['type'] == 'str':
+            s = s.copy()
+            s['pattern'] = constraint.pattern
+        else:
+
+            def check_pattern(v: object) -> bool:
+                assert isinstance(v, str)
+                return constraint.match(v) is not None
+
+            s = _check_func(check_pattern, f'~ {constraint.pattern}', s)
+    return s
+
+
+class _SupportsRange(annotated_types.SupportsLe, annotated_types.SupportsGe, Protocol):
+    pass
+
+
+class _SupportsLen(Protocol):
+    def __len__(self) -> int: ...
+
+
+_NewOutGt = TypeVar('_NewOutGt', bound=annotated_types.SupportsGt)
+_NewOutGe = TypeVar('_NewOutGe', bound=annotated_types.SupportsGe)
+_NewOutLt = TypeVar('_NewOutLt', bound=annotated_types.SupportsLt)
+_NewOutLe = TypeVar('_NewOutLe', bound=annotated_types.SupportsLe)
+_NewOutLen = TypeVar('_NewOutLen', bound=_SupportsLen)
+_NewOutDiv = TypeVar('_NewOutDiv', bound=annotated_types.SupportsDiv)
+_NewOutMod = TypeVar('_NewOutMod', bound=annotated_types.SupportsMod)
+_NewOutDatetime = TypeVar('_NewOutDatetime', bound=datetime.datetime)
+_NewOutInterval = TypeVar('_NewOutInterval', bound=_SupportsRange)
+_OtherIn = TypeVar('_OtherIn')
+_OtherOut = TypeVar('_OtherOut')

.mcp-venv/lib/python3.13/site-packages/pydantic/fields.py

@@ -0,0 +1,1892 @@
+"""Defining fields on models."""
+
+from __future__ import annotations as _annotations
+
+import dataclasses
+import inspect
+import re
+import sys
+from collections.abc import Callable, Mapping
+from copy import copy
+from dataclasses import Field as DataclassField
+from functools import cached_property
+from typing import TYPE_CHECKING, Annotated, Any, ClassVar, Literal, TypeVar, final, overload
+from warnings import warn
+
+import annotated_types
+import typing_extensions
+from pydantic_core import MISSING, PydanticUndefined
+from typing_extensions import Self, TypeAlias, TypedDict, Unpack, deprecated
+from typing_inspection import typing_objects
+from typing_inspection.introspection import UNKNOWN, AnnotationSource, ForbiddenQualifier, Qualifier, inspect_annotation
+
+from . import types
+from ._internal import _decorators, _fields, _generics, _internal_dataclass, _repr, _typing_extra, _utils
+from ._internal._namespace_utils import GlobalsNamespace, MappingNamespace
+from .aliases import AliasChoices, AliasGenerator, AliasPath
+from .config import JsonDict
+from .errors import PydanticForbiddenQualifier, PydanticUserError
+from .json_schema import PydanticJsonSchemaWarning
+from .warnings import PydanticDeprecatedSince20
+
+if TYPE_CHECKING:
+    from ._internal._config import ConfigWrapper
+    from ._internal._repr import ReprArgs
+
+
+__all__ = 'Field', 'FieldInfo', 'PrivateAttr', 'computed_field'
+
+
+_Unset: Any = PydanticUndefined
+
+if sys.version_info >= (3, 13):
+    import warnings
+
+    Deprecated: TypeAlias = warnings.deprecated | deprecated
+else:
+    Deprecated: TypeAlias = deprecated
+
+
+class _FromFieldInfoInputs(TypedDict, total=False):
+    """This class exists solely to add type checking for the `**kwargs` in `FieldInfo.from_field`."""
+
+    # TODO PEP 747: use TypeForm:
+    annotation: type[Any] | None
+    default_factory: Callable[[], Any] | Callable[[dict[str, Any]], Any] | None
+    alias: str | None
+    alias_priority: int | None
+    validation_alias: str | AliasPath | AliasChoices | None
+    serialization_alias: str | None
+    title: str | None
+    field_title_generator: Callable[[str, FieldInfo], str] | None
+    description: str | None
+    examples: list[Any] | None
+    exclude: bool | None
+    exclude_if: Callable[[Any], bool] | None
+    gt: annotated_types.SupportsGt | None
+    ge: annotated_types.SupportsGe | None
+    lt: annotated_types.SupportsLt | None
+    le: annotated_types.SupportsLe | None
+    multiple_of: float | None
+    strict: bool | None
+    min_length: int | None
+    max_length: int | None
+    pattern: str | re.Pattern[str] | None
+    allow_inf_nan: bool | None
+    max_digits: int | None
+    decimal_places: int | None
+    union_mode: Literal['smart', 'left_to_right'] | None
+    discriminator: str | types.Discriminator | None
+    deprecated: Deprecated | str | bool | None
+    json_schema_extra: JsonDict | Callable[[JsonDict], None] | None
+    frozen: bool | None
+    validate_default: bool | None
+    repr: bool
+    init: bool | None
+    init_var: bool | None
+    kw_only: bool | None
+    coerce_numbers_to_str: bool | None
+    fail_fast: bool | None
+
+
+class _FieldInfoInputs(_FromFieldInfoInputs, total=False):
+    """This class exists solely to add type checking for the `**kwargs` in `FieldInfo.__init__`."""
+
+    default: Any
+
+
+class _FieldInfoAsDict(TypedDict, closed=True):
+    # TODO PEP 747: use TypeForm:
+    annotation: Any
+    metadata: list[Any]
+    attributes: dict[str, Any]
+
+
+@final
+class FieldInfo(_repr.Representation):
+    """This class holds information about a field.
+
+    `FieldInfo` is used for any field definition regardless of whether the [`Field()`][pydantic.fields.Field]
+    function is explicitly used.
+
+    !!! warning
+        The `FieldInfo` class is meant to expose information about a field in a Pydantic model or dataclass.
+        `FieldInfo` instances shouldn't be instantiated directly, nor mutated.
+
+        If you need to derive a new model from another one and are willing to alter `FieldInfo` instances,
+        refer to this [dynamic model example](../examples/dynamic_models.md).
+
+    Attributes:
+        annotation: The type annotation of the field.
+        default: The default value of the field.
+        default_factory: A callable to generate the default value. The callable can either take 0 arguments
+            (in which case it is called as is) or a single argument containing the already validated data.
+        alias: The alias name of the field.
+        alias_priority: The priority of the field's alias.
+        validation_alias: The validation alias of the field.
+        serialization_alias: The serialization alias of the field.
+        title: The title of the field.
+        field_title_generator: A callable that takes a field name and returns title for it.
+        description: The description of the field.
+        examples: List of examples of the field.
+        exclude: Whether to exclude the field from the model serialization.
+        exclude_if: A callable that determines whether to exclude a field during serialization based on its value.
+        discriminator: Field name or Discriminator for discriminating the type in a tagged union.
+        deprecated: A deprecation message, an instance of `warnings.deprecated` or the `typing_extensions.deprecated` backport,
+            or a boolean. If `True`, a default deprecation message will be emitted when accessing the field.
+        json_schema_extra: A dict or callable to provide extra JSON schema properties.
+        frozen: Whether the field is frozen.
+        validate_default: Whether to validate the default value of the field.
+        repr: Whether to include the field in representation of the model.
+        init: Whether the field should be included in the constructor of the dataclass.
+        init_var: Whether the field should _only_ be included in the constructor of the dataclass, and not stored.
+        kw_only: Whether the field should be a keyword-only argument in the constructor of the dataclass.
+        metadata: The metadata list. Contains all the data that isn't expressed as direct `FieldInfo` attributes, including:
+
+            * Type-specific constraints, such as `gt` or `min_length` (these are converted to metadata classes such as `annotated_types.Gt`).
+            * Any other arbitrary object used within [`Annotated`][typing.Annotated] metadata
+              (e.g. [custom types handlers](../concepts/types.md#as-an-annotation) or any object not recognized by Pydantic).
+    """
+
+    # TODO PEP 747: use TypeForm:
+    annotation: type[Any] | None
+    default: Any
+    default_factory: Callable[[], Any] | Callable[[dict[str, Any]], Any] | None
+    alias: str | None
+    alias_priority: int | None
+    validation_alias: str | AliasPath | AliasChoices | None
+    serialization_alias: str | None
+    title: str | None
+    field_title_generator: Callable[[str, FieldInfo], str] | None
+    description: str | None
+    examples: list[Any] | None
+    exclude: bool | None
+    exclude_if: Callable[[Any], bool] | None
+    discriminator: str | types.Discriminator | None
+    deprecated: Deprecated | str | bool | None
+    json_schema_extra: JsonDict | Callable[[JsonDict], None] | None
+    frozen: bool | None
+    validate_default: bool | None
+    repr: bool
+    init: bool | None
+    init_var: bool | None
+    kw_only: bool | None
+    metadata: list[Any]
+
+    __slots__ = (
+        'annotation',
+        'default',
+        'default_factory',
+        'alias',
+        'alias_priority',
+        'validation_alias',
+        'serialization_alias',
+        'title',
+        'field_title_generator',
+        'description',
+        'examples',
+        'exclude',
+        'exclude_if',
+        'discriminator',
+        'deprecated',
+        'json_schema_extra',
+        'frozen',
+        'validate_default',
+        'repr',
+        'init',
+        'init_var',
+        'kw_only',
+        'metadata',
+        '_attributes_set',
+        '_qualifiers',
+        '_complete',
+        '_original_assignment',
+        '_original_annotation',
+        '_final',
+    )
+
+    # used to convert kwargs to metadata/constraints,
+    # None has a special meaning - these items are collected into a `PydanticGeneralMetadata`
+    metadata_lookup: ClassVar[dict[str, Callable[[Any], Any] | None]] = {
+        'strict': types.Strict,
+        'gt': annotated_types.Gt,
+        'ge': annotated_types.Ge,
+        'lt': annotated_types.Lt,
+        'le': annotated_types.Le,
+        'multiple_of': annotated_types.MultipleOf,
+        'min_length': annotated_types.MinLen,
+        'max_length': annotated_types.MaxLen,
+        'pattern': None,
+        'allow_inf_nan': None,
+        'max_digits': None,
+        'decimal_places': None,
+        'union_mode': None,
+        'coerce_numbers_to_str': None,
+        'fail_fast': types.FailFast,
+    }
+
+    def __init__(self, **kwargs: Unpack[_FieldInfoInputs]) -> None:
+        """This class should generally not be initialized directly; instead, use the `pydantic.fields.Field` function
+        or one of the constructor classmethods.
+
+        See the signature of `pydantic.fields.Field` for more details about the expected arguments.
+        """
+        # Tracking the explicitly set attributes is necessary to correctly merge `Field()` functions
+        # (e.g. with `Annotated[int, Field(alias='a'), Field(alias=None)]`, even though `None` is the default value,
+        # we need to track that `alias=None` was explicitly set):
+        self._attributes_set = {k: v for k, v in kwargs.items() if v is not _Unset and k not in self.metadata_lookup}
+        kwargs = {k: _DefaultValues.get(k) if v is _Unset else v for k, v in kwargs.items()}  # type: ignore
+        self.annotation = kwargs.get('annotation')
+
+        # Note: in theory, the second `pop()` arguments are not required below, as defaults are already set from `_DefaultsValues`.
+        default = kwargs.pop('default', PydanticUndefined)
+        if default is Ellipsis:
+            self.default = PydanticUndefined
+            self._attributes_set.pop('default', None)
+        else:
+            self.default = default
+
+        self.default_factory = kwargs.pop('default_factory', None)
+
+        if self.default is not PydanticUndefined and self.default_factory is not None:
+            raise TypeError('cannot specify both default and default_factory')
+
+        self.alias = kwargs.pop('alias', None)
+        self.validation_alias = kwargs.pop('validation_alias', None)
+        self.serialization_alias = kwargs.pop('serialization_alias', None)
+        alias_is_set = any(alias is not None for alias in (self.alias, self.validation_alias, self.serialization_alias))
+        self.alias_priority = kwargs.pop('alias_priority', None) or 2 if alias_is_set else None
+        self.title = kwargs.pop('title', None)
+        self.field_title_generator = kwargs.pop('field_title_generator', None)
+        self.description = kwargs.pop('description', None)
+        self.examples = kwargs.pop('examples', None)
+        self.exclude = kwargs.pop('exclude', None)
+        self.exclude_if = kwargs.pop('exclude_if', None)
+        self.discriminator = kwargs.pop('discriminator', None)
+        # For compatibility with FastAPI<=0.110.0, we preserve the existing value if it is not overridden
+        self.deprecated = kwargs.pop('deprecated', getattr(self, 'deprecated', None))
+        self.repr = kwargs.pop('repr', True)
+        self.json_schema_extra = kwargs.pop('json_schema_extra', None)
+        self.validate_default = kwargs.pop('validate_default', None)
+        self.frozen = kwargs.pop('frozen', None)
+        # currently only used on dataclasses
+        self.init = kwargs.pop('init', None)
+        self.init_var = kwargs.pop('init_var', None)
+        self.kw_only = kwargs.pop('kw_only', None)
+
+        self.metadata = self._collect_metadata(kwargs)  # type: ignore
+
+        # Private attributes:
+        self._qualifiers: set[Qualifier] = set()
+        # Used to rebuild FieldInfo instances:
+        self._complete = True
+        self._original_annotation: Any = PydanticUndefined
+        self._original_assignment: Any = PydanticUndefined
+        # Used to track whether the `FieldInfo` instance represents the data about a field (and is exposed in `model_fields`/`__pydantic_fields__`),
+        # or if it is the result of the `Field()` function being used as metadata in an `Annotated` type/as an assignment
+        # (not an ideal pattern, see https://github.com/pydantic/pydantic/issues/11122):
+        self._final = False
+
+    @staticmethod
+    def from_field(default: Any = PydanticUndefined, **kwargs: Unpack[_FromFieldInfoInputs]) -> FieldInfo:
+        """Create a new `FieldInfo` object with the `Field` function.
+
+        Args:
+            default: The default value for the field. Defaults to Undefined.
+            **kwargs: Additional arguments dictionary.
+
+        Raises:
+            TypeError: If 'annotation' is passed as a keyword argument.
+
+        Returns:
+            A new FieldInfo object with the given parameters.
+
+        Example:
+            This is how you can create a field with default value like this:
+
+            ```python
+            import pydantic
+
+            class MyModel(pydantic.BaseModel):
+                foo: int = pydantic.Field(4)
+            ```
+        """
+        if 'annotation' in kwargs:
+            raise TypeError('"annotation" is not permitted as a Field keyword argument')
+        return FieldInfo(default=default, **kwargs)
+
+    @staticmethod
+    def from_annotation(annotation: type[Any], *, _source: AnnotationSource = AnnotationSource.ANY) -> FieldInfo:
+        """Creates a `FieldInfo` instance from a bare annotation.
+
+        This function is used internally to create a `FieldInfo` from a bare annotation like this:
+
+        ```python
+        import pydantic
+
+        class MyModel(pydantic.BaseModel):
+            foo: int  # <-- like this
+        ```
+
+        We also account for the case where the annotation can be an instance of `Annotated` and where
+        one of the (not first) arguments in `Annotated` is an instance of `FieldInfo`, e.g.:
+
+        ```python
+        from typing import Annotated
+
+        import annotated_types
+
+        import pydantic
+
+        class MyModel(pydantic.BaseModel):
+            foo: Annotated[int, annotated_types.Gt(42)]
+            bar: Annotated[int, pydantic.Field(gt=42)]
+        ```
+
+        Args:
+            annotation: An annotation object.
+
+        Returns:
+            An instance of the field metadata.
+        """
+        try:
+            inspected_ann = inspect_annotation(
+                annotation,
+                annotation_source=_source,
+                unpack_type_aliases='skip',
+            )
+        except ForbiddenQualifier as e:
+            raise PydanticForbiddenQualifier(e.qualifier, annotation)
+
+        # TODO check for classvar and error?
+
+        # No assigned value, this happens when using a bare `Final` qualifier (also for other
+        # qualifiers, but they shouldn't appear here). In this case we infer the type as `Any`
+        # because we don't have any assigned value.
+        type_expr: Any = Any if inspected_ann.type is UNKNOWN else inspected_ann.type
+        final = 'final' in inspected_ann.qualifiers
+        metadata = inspected_ann.metadata
+
+        attr_overrides = {'annotation': type_expr}
+        if final:
+            attr_overrides['frozen'] = True
+        field_info = FieldInfo._construct(metadata, **attr_overrides)
+        field_info._qualifiers = inspected_ann.qualifiers
+        field_info._final = True
+        return field_info
+
+    @staticmethod
+    def from_annotated_attribute(
+        annotation: type[Any], default: Any, *, _source: AnnotationSource = AnnotationSource.ANY
+    ) -> FieldInfo:
+        """Create `FieldInfo` from an annotation with a default value.
+
+        This is used in cases like the following:
+
+        ```python
+        from typing import Annotated
+
+        import annotated_types
+
+        import pydantic
+
+        class MyModel(pydantic.BaseModel):
+            foo: int = 4  # <-- like this
+            bar: Annotated[int, annotated_types.Gt(4)] = 4  # <-- or this
+            spam: Annotated[int, pydantic.Field(gt=4)] = 4  # <-- or this
+        ```
+
+        Args:
+            annotation: The type annotation of the field.
+            default: The default value of the field.
+
+        Returns:
+            A field object with the passed values.
+        """
+        if annotation is not MISSING and annotation is default:
+            raise PydanticUserError(
+                'Error when building FieldInfo from annotated attribute. '
+                "Make sure you don't have any field name clashing with a type annotation.",
+                code='unevaluable-type-annotation',
+            )
+
+        try:
+            inspected_ann = inspect_annotation(
+                annotation,
+                annotation_source=_source,
+                unpack_type_aliases='skip',
+            )
+        except ForbiddenQualifier as e:
+            raise PydanticForbiddenQualifier(e.qualifier, annotation)
+
+        # TODO check for classvar and error?
+
+        # TODO infer from the default, this can be done in v3 once we treat final fields with
+        # a default as proper fields and not class variables:
+        type_expr: Any = Any if inspected_ann.type is UNKNOWN else inspected_ann.type
+        final = 'final' in inspected_ann.qualifiers
+        metadata = inspected_ann.metadata
+
+        # HACK 1: the order in which the metadata is merged is inconsistent; we need to prepend
+        # metadata from the assignment at the beginning of the metadata. Changing this is only
+        # possible in v3 (at least). See https://github.com/pydantic/pydantic/issues/10507
+        prepend_metadata: list[Any] | None = None
+        attr_overrides = {'annotation': type_expr}
+        if final:
+            attr_overrides['frozen'] = True
+
+        # HACK 2: FastAPI is subclassing `FieldInfo` and historically expected the actual
+        # instance's type to be preserved when constructing new models with its subclasses as assignments.
+        # This code is never reached by Pydantic itself, and in an ideal world this shouldn't be necessary.
+        if not metadata and isinstance(default, FieldInfo) and type(default) is not FieldInfo:
+            field_info = default._copy()
+            field_info._attributes_set.update(attr_overrides)
+            for k, v in attr_overrides.items():
+                setattr(field_info, k, v)
+            return field_info
+
+        if isinstance(default, FieldInfo):
+            default_copy = default._copy()  # Copy unnecessary when we remove HACK 1.
+            prepend_metadata = default_copy.metadata
+            default_copy.metadata = []
+            metadata = metadata + [default_copy]
+            if 'init_var' in inspected_ann.qualifiers:
+                # Only relevant for dataclasses, when `f: InitVar[<type>] = Field(...)`
+                # is used:
+                attr_overrides['init_var'] = True
+        elif isinstance(default, dataclasses.Field):
+            from_field = FieldInfo._from_dataclass_field(default)
+            prepend_metadata = from_field.metadata  # Unnecessary when we remove HACK 1.
+            from_field.metadata = []
+            metadata = metadata + [from_field]
+            if 'init_var' in inspected_ann.qualifiers:
+                attr_overrides['init_var'] = True
+            if (init := getattr(default, 'init', None)) is not None:
+                attr_overrides['init'] = init
+            if (kw_only := getattr(default, 'kw_only', None)) is not None:
+                attr_overrides['kw_only'] = kw_only
+        else:
+            # `default` is the actual default value
+            attr_overrides['default'] = default
+
+        field_info = FieldInfo._construct(
+            prepend_metadata + metadata if prepend_metadata is not None else metadata, **attr_overrides
+        )
+        field_info._qualifiers = inspected_ann.qualifiers
+        field_info._final = True
+        return field_info
+
+    @classmethod
+    def _construct(cls, metadata: list[Any], **attr_overrides: Any) -> Self:
+        """Construct the final `FieldInfo` instance, by merging the possibly existing `FieldInfo` instances from the metadata.
+
+        With the following example:
+
+        ```python {test="skip" lint="skip"}
+        class Model(BaseModel):
+            f: Annotated[int, Gt(1), Field(description='desc', lt=2)]
+        ```
+
+        `metadata` refers to the metadata elements of the `Annotated` form. This metadata is iterated over from left to right:
+
+        - If the element is a `Field()` function (which is itself a `FieldInfo` instance), the field attributes (such as
+          `description`) are saved to be set on the final `FieldInfo` instance.
+          On the other hand, some kwargs (such as `lt`) are stored as `metadata` (see `FieldInfo.__init__()`, calling
+          `FieldInfo._collect_metadata()`). In this case, the final metadata list is extended with the one from this instance.
+        - Else, the element is considered as a single metadata object, and is appended to the final metadata list.
+
+        Args:
+            metadata: The list of metadata elements to merge together. If the `FieldInfo` instance to be constructed is for
+                a field with an assigned `Field()`, this `Field()` assignment should be added as the last element of the
+                provided metadata.
+            **attr_overrides: Extra attributes that should be set on the final merged `FieldInfo` instance.
+
+        Returns:
+            The final merged `FieldInfo` instance.
+        """
+        merged_metadata: list[Any] = []
+        merged_kwargs: dict[str, Any] = {}
+
+        for meta in metadata:
+            if isinstance(meta, FieldInfo):
+                merged_metadata.extend(meta.metadata)
+
+                new_js_extra: JsonDict | None = None
+                current_js_extra = meta.json_schema_extra
+                if current_js_extra is not None and 'json_schema_extra' in merged_kwargs:
+                    # We need to merge `json_schema_extra`'s:
+                    existing_js_extra = merged_kwargs['json_schema_extra']
+                    if isinstance(existing_js_extra, dict):
+                        if isinstance(current_js_extra, dict):
+                            new_js_extra = {
+                                **existing_js_extra,
+                                **current_js_extra,
+                            }
+                        elif callable(current_js_extra):
+                            warn(
+                                'Composing `dict` and `callable` type `json_schema_extra` is not supported. '
+                                'The `callable` type is being ignored. '
+                                "If you'd like support for this behavior, please open an issue on pydantic.",
+                                UserWarning,
+                            )
+                    elif callable(existing_js_extra) and isinstance(current_js_extra, dict):
+                        warn(
+                            'Composing `dict` and `callable` type `json_schema_extra` is not supported. '
+                            'The `callable` type is being ignored. '
+                            "If you'd like support for this behavior, please open an issue on pydantic.",
+                            UserWarning,
+                        )
+
+                # HACK: It is common for users to define "make model partial" (or similar) utilities, that
+                # convert all model fields to be optional (i.e. have a default value). To do so, they mutate
+                # each `FieldInfo` instance from `model_fields` to set a `default`, and use `create_model()`
+                # with `Annotated[<orig_type> | None, mutated_field_info]`` as an annotation. However, such
+                # mutations (by doing simple assignments) are only accidentally working, because we also
+                # need to track attributes explicitly set in `_attributes_set` (relying on default values for
+                # each attribute is *not* enough, for instance with `Annotated[int, Field(alias='a'), Field(alias=None)]`
+                # the resulting `FieldInfo` should have `alias=None`).
+                # To mitigate this, we add a special case when a "final" `FieldInfo` instance (that is an instance coming
+                # from `model_fields`) is used in annotated metadata (or assignment). In this case, we assume *all* attributes
+                # were explicitly set, and as such we use all of them (and this will correctly pick up the mutations).
+                # In theory, this shouldn't really be supported, you are only supposed to use the `Field()` function, not
+                # a `FieldInfo` instance directly (granted, `Field()` returns a `FieldInfo`, see
+                # https://github.com/pydantic/pydantic/issues/11122):
+                if meta._final:
+                    merged_kwargs.update({attr: getattr(meta, attr) for attr in _Attrs})
+                else:
+                    merged_kwargs.update(meta._attributes_set)
+
+                if new_js_extra is not None:
+                    merged_kwargs['json_schema_extra'] = new_js_extra
+            elif typing_objects.is_deprecated(meta):
+                merged_kwargs['deprecated'] = meta
+            else:
+                merged_metadata.append(meta)
+
+        merged_kwargs.update(attr_overrides)
+        merged_field_info = cls(**merged_kwargs)
+        merged_field_info.metadata = merged_metadata
+        return merged_field_info
+
+    @staticmethod
+    @typing_extensions.deprecated(
+        "The 'merge_field_infos()' method is deprecated and will be removed in a future version. "
+        'If you relied on this method, please open an issue in the Pydantic issue tracker.',
+        category=None,
+    )
+    def merge_field_infos(*field_infos: FieldInfo, **overrides: Any) -> FieldInfo:
+        """Merge `FieldInfo` instances keeping only explicitly set attributes.
+
+        Later `FieldInfo` instances override earlier ones.
+
+        Returns:
+            FieldInfo: A merged FieldInfo instance.
+        """
+        if len(field_infos) == 1:
+            # No merging necessary, but we still need to make a copy and apply the overrides
+            field_info = field_infos[0]._copy()
+            field_info._attributes_set.update(overrides)
+
+            default_override = overrides.pop('default', PydanticUndefined)
+            if default_override is Ellipsis:
+                default_override = PydanticUndefined
+            if default_override is not PydanticUndefined:
+                field_info.default = default_override
+
+            for k, v in overrides.items():
+                setattr(field_info, k, v)
+            return field_info  # type: ignore
+
+        merged_field_info_kwargs: dict[str, Any] = {}
+        metadata = {}
+        for field_info in field_infos:
+            attributes_set = field_info._attributes_set.copy()
+
+            try:
+                json_schema_extra = attributes_set.pop('json_schema_extra')
+                existing_json_schema_extra = merged_field_info_kwargs.get('json_schema_extra')
+
+                if existing_json_schema_extra is None:
+                    merged_field_info_kwargs['json_schema_extra'] = json_schema_extra
+                if isinstance(existing_json_schema_extra, dict):
+                    if isinstance(json_schema_extra, dict):
+                        merged_field_info_kwargs['json_schema_extra'] = {
+                            **existing_json_schema_extra,
+                            **json_schema_extra,
+                        }
+                    if callable(json_schema_extra):
+                        warn(
+                            'Composing `dict` and `callable` type `json_schema_extra` is not supported.'
+                            'The `callable` type is being ignored.'
+                            "If you'd like support for this behavior, please open an issue on pydantic.",
+                            PydanticJsonSchemaWarning,
+                        )
+                elif callable(json_schema_extra):
+                    # if ever there's a case of a callable, we'll just keep the last json schema extra spec
+                    merged_field_info_kwargs['json_schema_extra'] = json_schema_extra
+            except KeyError:
+                pass
+
+            # later FieldInfo instances override everything except json_schema_extra from earlier FieldInfo instances
+            merged_field_info_kwargs.update(attributes_set)
+
+            for x in field_info.metadata:
+                if not isinstance(x, FieldInfo):
+                    metadata[type(x)] = x
+
+        merged_field_info_kwargs.update(overrides)
+        field_info = FieldInfo(**merged_field_info_kwargs)
+        field_info.metadata = list(metadata.values())
+        return field_info
+
+    @staticmethod
+    def _from_dataclass_field(dc_field: DataclassField[Any]) -> FieldInfo:
+        """Return a new `FieldInfo` instance from a `dataclasses.Field` instance.
+
+        Args:
+            dc_field: The `dataclasses.Field` instance to convert.
+
+        Returns:
+            The corresponding `FieldInfo` instance.
+
+        Raises:
+            TypeError: If any of the `FieldInfo` kwargs does not match the `dataclass.Field` kwargs.
+        """
+        default = dc_field.default
+        if default is dataclasses.MISSING:
+            default = _Unset
+
+        if dc_field.default_factory is dataclasses.MISSING:
+            default_factory = _Unset
+        else:
+            default_factory = dc_field.default_factory
+
+        # use the `Field` function so in correct kwargs raise the correct `TypeError`
+        dc_field_metadata = {k: v for k, v in dc_field.metadata.items() if k in _FIELD_ARG_NAMES}
+        if sys.version_info >= (3, 14) and dc_field.doc is not None:
+            dc_field_metadata['description'] = dc_field.doc
+        return Field(default=default, default_factory=default_factory, repr=dc_field.repr, **dc_field_metadata)  # pyright: ignore[reportCallIssue]
+
+    @staticmethod
+    def _collect_metadata(kwargs: dict[str, Any]) -> list[Any]:
+        """Collect annotations from kwargs.
+
+        Args:
+            kwargs: Keyword arguments passed to the function.
+
+        Returns:
+            A list of metadata objects - a combination of `annotated_types.BaseMetadata` and
+                `PydanticMetadata`.
+        """
+        metadata: list[Any] = []
+        general_metadata = {}
+        for key, value in list(kwargs.items()):
+            try:
+                marker = FieldInfo.metadata_lookup[key]
+            except KeyError:
+                continue
+
+            del kwargs[key]
+            if value is not None:
+                if marker is None:
+                    general_metadata[key] = value
+                else:
+                    metadata.append(marker(value))
+        if general_metadata:
+            metadata.append(_fields.pydantic_general_metadata(**general_metadata))
+        return metadata
+
+    @property
+    def deprecation_message(self) -> str | None:
+        """The deprecation message to be emitted, or `None` if not set."""
+        if self.deprecated is None:
+            return None
+        if isinstance(self.deprecated, bool):
+            return 'deprecated' if self.deprecated else None
+        return self.deprecated if isinstance(self.deprecated, str) else self.deprecated.message
+
+    @property
+    def default_factory_takes_validated_data(self) -> bool | None:
+        """Whether the provided default factory callable has a validated data parameter.
+
+        Returns `None` if no default factory is set.
+        """
+        if self.default_factory is not None:
+            return _fields.takes_validated_data_argument(self.default_factory)
+
+    @overload
+    def get_default(
+        self, *, call_default_factory: Literal[True], validated_data: dict[str, Any] | None = None
+    ) -> Any: ...
+
+    @overload
+    def get_default(self, *, call_default_factory: Literal[False] = ...) -> Any: ...
+
+    def get_default(self, *, call_default_factory: bool = False, validated_data: dict[str, Any] | None = None) -> Any:
+        """Get the default value.
+
+        We expose an option for whether to call the default_factory (if present), as calling it may
+        result in side effects that we want to avoid. However, there are times when it really should
+        be called (namely, when instantiating a model via `model_construct`).
+
+        Args:
+            call_default_factory: Whether to call the default factory or not.
+            validated_data: The already validated data to be passed to the default factory.
+
+        Returns:
+            The default value, calling the default factory if requested or `PydanticUndefined` if not set.
+        """
+        return _fields.resolve_default_value(
+            default=self.default,
+            default_factory=self.default_factory,
+            validated_data=validated_data,
+            call_default_factory=call_default_factory,
+        )
+
+    def is_required(self) -> bool:
+        """Check if the field is required (i.e., does not have a default value or factory).
+
+        Returns:
+            `True` if the field is required, `False` otherwise.
+        """
+        return self.default is PydanticUndefined and self.default_factory is None
+
+    def rebuild_annotation(self) -> Any:
+        """Attempts to rebuild the original annotation for use in function signatures.
+
+        If metadata is present, it adds it to the original annotation using
+        `Annotated`. Otherwise, it returns the original annotation as-is.
+
+        Note that because the metadata has been flattened, the original annotation
+        may not be reconstructed exactly as originally provided, e.g. if the original
+        type had unrecognized annotations, or was annotated with a call to `pydantic.Field`.
+
+        Returns:
+            The rebuilt annotation.
+        """
+        if not self.metadata:
+            return self.annotation
+        else:
+            # Annotated arguments must be a tuple
+            return Annotated[(self.annotation, *self.metadata)]  # type: ignore
+
+    def apply_typevars_map(
+        self,
+        typevars_map: Mapping[TypeVar, Any] | None,
+        globalns: GlobalsNamespace | None = None,
+        localns: MappingNamespace | None = None,
+    ) -> None:
+        """Apply a `typevars_map` to the annotation.
+
+        This method is used when analyzing parametrized generic types to replace typevars with their concrete types.
+
+        This method applies the `typevars_map` to the annotation in place.
+
+        Args:
+            typevars_map: A dictionary mapping type variables to their concrete types.
+            globalns: The globals namespace to use during type annotation evaluation.
+            localns: The locals namespace to use during type annotation evaluation.
+
+        See Also:
+            pydantic._internal._generics.replace_types is used for replacing the typevars with
+                their concrete types.
+        """
+        annotation = _generics.replace_types(self.annotation, typevars_map)
+        annotation, evaluated = _typing_extra.try_eval_type(annotation, globalns, localns)
+        self.annotation = annotation
+        if not evaluated:
+            self._complete = False
+            self._original_annotation = self.annotation
+
+    def asdict(self) -> _FieldInfoAsDict:
+        """Return a dictionary representation of the `FieldInfo` instance.
+
+        The returned value is a dictionary with three items:
+
+        * `annotation`: The type annotation of the field.
+        * `metadata`: The metadata list.
+        * `attributes`: A mapping of the remaining `FieldInfo` attributes to their values (e.g. `alias`, `title`).
+        """
+        return {
+            'annotation': self.annotation,
+            'metadata': self.metadata,
+            'attributes': {attr: getattr(self, attr) for attr in _Attrs},
+        }
+
+    def _copy(self) -> Self:
+        """Return a copy of the `FieldInfo` instance."""
+        # Note: we can't define a custom `__copy__()`, as `FieldInfo` is being subclassed
+        # by some third-party libraries with extra attributes defined (and as `FieldInfo`
+        # is slotted, we can't make a copy of the `__dict__`).
+        if type(self) is FieldInfo:
+            # Fast-path if the instance isn't a subclass (`copy.copy()` relies on pickling which is slower):
+            copied = FieldInfo.__new__(FieldInfo)
+            for attr_name in FieldInfo.__slots__:
+                setattr(copied, attr_name, getattr(self, attr_name))
+        else:
+            copied = copy(self)
+
+        for attr_name in ('metadata', '_attributes_set', '_qualifiers'):
+            # Apply "deep-copy" behavior on collections attributes:
+            setattr(copied, attr_name, getattr(copied, attr_name).copy())
+
+        return copied  # pyright: ignore[reportReturnType]
+
+    def __repr_args__(self) -> ReprArgs:
+        yield 'annotation', _repr.PlainRepr(_repr.display_as_type(self.annotation))
+        yield 'required', self.is_required()
+
+        for s in self.__slots__:
+            # TODO: properly make use of the protocol (https://rich.readthedocs.io/en/stable/pretty.html#rich-repr-protocol)
+            # By yielding a three-tuple:
+            if s in (
+                'annotation',
+                '_attributes_set',
+                '_qualifiers',
+                '_complete',
+                '_original_assignment',
+                '_original_annotation',
+                '_final',
+            ):
+                continue
+            elif s == 'metadata' and not self.metadata:
+                continue
+            elif s == 'repr' and self.repr is True:
+                continue
+            if s == 'frozen' and self.frozen is False:
+                continue
+            if s == 'validation_alias' and self.validation_alias == self.alias:
+                continue
+            if s == 'serialization_alias' and self.serialization_alias == self.alias:
+                continue
+            if s == 'default' and self.default is not PydanticUndefined:
+                yield 'default', self.default
+            elif s == 'default_factory' and self.default_factory is not None:
+                yield 'default_factory', _repr.PlainRepr(_repr.display_as_type(self.default_factory))
+            else:
+                value = getattr(self, s)
+                if value is not None and value is not PydanticUndefined:
+                    yield s, value
+
+
+class _EmptyKwargs(TypedDict):
+    """This class exists solely to ensure that type checking warns about passing `**extra` in `Field`."""
+
+
+_Attrs = {
+    'default': ...,
+    'default_factory': None,
+    'alias': None,
+    'alias_priority': None,
+    'validation_alias': None,
+    'serialization_alias': None,
+    'title': None,
+    'field_title_generator': None,
+    'description': None,
+    'examples': None,
+    'exclude': None,
+    'exclude_if': None,
+    'discriminator': None,
+    'deprecated': None,
+    'json_schema_extra': None,
+    'frozen': None,
+    'validate_default': None,
+    'repr': True,
+    'init': None,
+    'init_var': None,
+    'kw_only': None,
+}
+
+_DefaultValues = {
+    **_Attrs,
+    'kw_only': None,
+    'pattern': None,
+    'strict': None,
+    'gt': None,
+    'ge': None,
+    'lt': None,
+    'le': None,
+    'multiple_of': None,
+    'allow_inf_nan': None,
+    'max_digits': None,
+    'decimal_places': None,
+    'min_length': None,
+    'max_length': None,
+    'coerce_numbers_to_str': None,
+}
+
+
+_T = TypeVar('_T')
+
+
+# NOTE: Actual return type is 'FieldInfo', but we want to help type checkers
+# to understand the magic that happens at runtime with the following overloads:
+@overload  # type hint the return value as `Any` to avoid type checking regressions when using `...`.
+def Field(
+    default: ellipsis,  # noqa: F821  # TODO: use `_typing_extra.EllipsisType` when we drop Py3.9
+    *,
+    alias: str | None = _Unset,
+    alias_priority: int | None = _Unset,
+    validation_alias: str | AliasPath | AliasChoices | None = _Unset,
+    serialization_alias: str | None = _Unset,
+    title: str | None = _Unset,
+    field_title_generator: Callable[[str, FieldInfo], str] | None = _Unset,
+    description: str | None = _Unset,
+    examples: list[Any] | None = _Unset,
+    exclude: bool | None = _Unset,
+    exclude_if: Callable[[Any], bool] | None = _Unset,
+    discriminator: str | types.Discriminator | None = _Unset,
+    deprecated: Deprecated | str | bool | None = _Unset,
+    json_schema_extra: JsonDict | Callable[[JsonDict], None] | None = _Unset,
+    frozen: bool | None = _Unset,
+    validate_default: bool | None = _Unset,
+    repr: bool = _Unset,
+    init: bool | None = _Unset,
+    init_var: bool | None = _Unset,
+    kw_only: bool | None = _Unset,
+    pattern: str | re.Pattern[str] | None = _Unset,
+    strict: bool | None = _Unset,
+    coerce_numbers_to_str: bool | None = _Unset,
+    gt: annotated_types.SupportsGt | None = _Unset,
+    ge: annotated_types.SupportsGe | None = _Unset,
+    lt: annotated_types.SupportsLt | None = _Unset,
+    le: annotated_types.SupportsLe | None = _Unset,
+    multiple_of: float | None = _Unset,
+    allow_inf_nan: bool | None = _Unset,
+    max_digits: int | None = _Unset,
+    decimal_places: int | None = _Unset,
+    min_length: int | None = _Unset,
+    max_length: int | None = _Unset,
+    union_mode: Literal['smart', 'left_to_right'] = _Unset,
+    fail_fast: bool | None = _Unset,
+    **extra: Unpack[_EmptyKwargs],
+) -> Any: ...
+@overload  # `default` argument set, validate_default=True (no type checking on the default value)
+def Field(
+    default: Any,
+    *,
+    alias: str | None = _Unset,
+    alias_priority: int | None = _Unset,
+    validation_alias: str | AliasPath | AliasChoices | None = _Unset,
+    serialization_alias: str | None = _Unset,
+    title: str | None = _Unset,
+    field_title_generator: Callable[[str, FieldInfo], str] | None = _Unset,
+    description: str | None = _Unset,
+    examples: list[Any] | None = _Unset,
+    exclude: bool | None = _Unset,
+    exclude_if: Callable[[Any], bool] | None = _Unset,
+    discriminator: str | types.Discriminator | None = _Unset,
+    deprecated: Deprecated | str | bool | None = _Unset,
+    json_schema_extra: JsonDict | Callable[[JsonDict], None] | None = _Unset,
+    frozen: bool | None = _Unset,
+    validate_default: Literal[True],
+    repr: bool = _Unset,
+    init: bool | None = _Unset,
+    init_var: bool | None = _Unset,
+    kw_only: bool | None = _Unset,
+    pattern: str | re.Pattern[str] | None = _Unset,
+    strict: bool | None = _Unset,
+    coerce_numbers_to_str: bool | None = _Unset,
+    gt: annotated_types.SupportsGt | None = _Unset,
+    ge: annotated_types.SupportsGe | None = _Unset,
+    lt: annotated_types.SupportsLt | None = _Unset,
+    le: annotated_types.SupportsLe | None = _Unset,
+    multiple_of: float | None = _Unset,
+    allow_inf_nan: bool | None = _Unset,
+    max_digits: int | None = _Unset,
+    decimal_places: int | None = _Unset,
+    min_length: int | None = _Unset,
+    max_length: int | None = _Unset,
+    union_mode: Literal['smart', 'left_to_right'] = _Unset,
+    fail_fast: bool | None = _Unset,
+    **extra: Unpack[_EmptyKwargs],
+) -> Any: ...
+@overload  # `default` argument set, validate_default=False or unset
+def Field(
+    default: _T,
+    *,
+    alias: str | None = _Unset,
+    alias_priority: int | None = _Unset,
+    validation_alias: str | AliasPath | AliasChoices | None = _Unset,
+    serialization_alias: str | None = _Unset,
+    title: str | None = _Unset,
+    field_title_generator: Callable[[str, FieldInfo], str] | None = _Unset,
+    description: str | None = _Unset,
+    examples: list[Any] | None = _Unset,
+    exclude: bool | None = _Unset,
+    # NOTE: to get proper type checking on `exclude_if`'s argument, we could use `_T` instead of `Any`. However,
+    # this requires (at least for pyright) adding an additional overload where `exclude_if` is required (otherwise
+    # `a: int = Field(default_factory=str)` results in a false negative).
+    exclude_if: Callable[[Any], bool] | None = _Unset,
+    discriminator: str | types.Discriminator | None = _Unset,
+    deprecated: Deprecated | str | bool | None = _Unset,
+    json_schema_extra: JsonDict | Callable[[JsonDict], None] | None = _Unset,
+    frozen: bool | None = _Unset,
+    validate_default: Literal[False] = ...,
+    repr: bool = _Unset,
+    init: bool | None = _Unset,
+    init_var: bool | None = _Unset,
+    kw_only: bool | None = _Unset,
+    pattern: str | re.Pattern[str] | None = _Unset,
+    strict: bool | None = _Unset,
+    coerce_numbers_to_str: bool | None = _Unset,
+    gt: annotated_types.SupportsGt | None = _Unset,
+    ge: annotated_types.SupportsGe | None = _Unset,
+    lt: annotated_types.SupportsLt | None = _Unset,
+    le: annotated_types.SupportsLe | None = _Unset,
+    multiple_of: float | None = _Unset,
+    allow_inf_nan: bool | None = _Unset,
+    max_digits: int | None = _Unset,
+    decimal_places: int | None = _Unset,
+    min_length: int | None = _Unset,
+    max_length: int | None = _Unset,
+    union_mode: Literal['smart', 'left_to_right'] = _Unset,
+    fail_fast: bool | None = _Unset,
+    **extra: Unpack[_EmptyKwargs],
+) -> _T: ...
+@overload  # `default_factory` argument set, validate_default=True  (no type checking on the default value)
+def Field(  # pyright: ignore[reportOverlappingOverload]
+    *,
+    default_factory: Callable[[], Any] | Callable[[dict[str, Any]], Any],
+    alias: str | None = _Unset,
+    alias_priority: int | None = _Unset,
+    validation_alias: str | AliasPath | AliasChoices | None = _Unset,
+    serialization_alias: str | None = _Unset,
+    title: str | None = _Unset,
+    field_title_generator: Callable[[str, FieldInfo], str] | None = _Unset,
+    description: str | None = _Unset,
+    examples: list[Any] | None = _Unset,
+    exclude: bool | None = _Unset,
+    exclude_if: Callable[[Any], bool] | None = _Unset,
+    discriminator: str | types.Discriminator | None = _Unset,
+    deprecated: Deprecated | str | bool | None = _Unset,
+    json_schema_extra: JsonDict | Callable[[JsonDict], None] | None = _Unset,
+    frozen: bool | None = _Unset,
+    validate_default: Literal[True],
+    repr: bool = _Unset,
+    init: bool | None = _Unset,
+    init_var: bool | None = _Unset,
+    kw_only: bool | None = _Unset,
+    pattern: str | re.Pattern[str] | None = _Unset,
+    strict: bool | None = _Unset,
+    coerce_numbers_to_str: bool | None = _Unset,
+    gt: annotated_types.SupportsGt | None = _Unset,
+    ge: annotated_types.SupportsGe | None = _Unset,
+    lt: annotated_types.SupportsLt | None = _Unset,
+    le: annotated_types.SupportsLe | None = _Unset,
+    multiple_of: float | None = _Unset,
+    allow_inf_nan: bool | None = _Unset,
+    max_digits: int | None = _Unset,
+    decimal_places: int | None = _Unset,
+    min_length: int | None = _Unset,
+    max_length: int | None = _Unset,
+    union_mode: Literal['smart', 'left_to_right'] = _Unset,
+    fail_fast: bool | None = _Unset,
+    **extra: Unpack[_EmptyKwargs],
+) -> Any: ...
+@overload  # `default_factory` argument set, validate_default=False or unset
+def Field(
+    *,
+    default_factory: Callable[[], _T] | Callable[[dict[str, Any]], _T],
+    alias: str | None = _Unset,
+    alias_priority: int | None = _Unset,
+    validation_alias: str | AliasPath | AliasChoices | None = _Unset,
+    serialization_alias: str | None = _Unset,
+    title: str | None = _Unset,
+    field_title_generator: Callable[[str, FieldInfo], str] | None = _Unset,
+    description: str | None = _Unset,
+    examples: list[Any] | None = _Unset,
+    exclude: bool | None = _Unset,
+    # NOTE: to get proper type checking on `exclude_if`'s argument, we could use `_T` instead of `Any`. However,
+    # this requires (at least for pyright) adding an additional overload where `exclude_if` is required (otherwise
+    # `a: int = Field(default_factory=str)` results in a false negative).
+    exclude_if: Callable[[Any], bool] | None = _Unset,
+    discriminator: str | types.Discriminator | None = _Unset,
+    deprecated: Deprecated | str | bool | None = _Unset,
+    json_schema_extra: JsonDict | Callable[[JsonDict], None] | None = _Unset,
+    frozen: bool | None = _Unset,
+    validate_default: Literal[False] | None = _Unset,
+    repr: bool = _Unset,
+    init: bool | None = _Unset,
+    init_var: bool | None = _Unset,
+    kw_only: bool | None = _Unset,
+    pattern: str | re.Pattern[str] | None = _Unset,
+    strict: bool | None = _Unset,
+    coerce_numbers_to_str: bool | None = _Unset,
+    gt: annotated_types.SupportsGt | None = _Unset,
+    ge: annotated_types.SupportsGe | None = _Unset,
+    lt: annotated_types.SupportsLt | None = _Unset,
+    le: annotated_types.SupportsLe | None = _Unset,
+    multiple_of: float | None = _Unset,
+    allow_inf_nan: bool | None = _Unset,
+    max_digits: int | None = _Unset,
+    decimal_places: int | None = _Unset,
+    min_length: int | None = _Unset,
+    max_length: int | None = _Unset,
+    union_mode: Literal['smart', 'left_to_right'] = _Unset,
+    fail_fast: bool | None = _Unset,
+    **extra: Unpack[_EmptyKwargs],
+) -> _T: ...
+@overload
+def Field(  # No default set
+    *,
+    alias: str | None = _Unset,
+    alias_priority: int | None = _Unset,
+    validation_alias: str | AliasPath | AliasChoices | None = _Unset,
+    serialization_alias: str | None = _Unset,
+    title: str | None = _Unset,
+    field_title_generator: Callable[[str, FieldInfo], str] | None = _Unset,
+    description: str | None = _Unset,
+    examples: list[Any] | None = _Unset,
+    exclude: bool | None = _Unset,
+    exclude_if: Callable[[Any], bool] | None = _Unset,
+    discriminator: str | types.Discriminator | None = _Unset,
+    deprecated: Deprecated | str | bool | None = _Unset,
+    json_schema_extra: JsonDict | Callable[[JsonDict], None] | None = _Unset,
+    frozen: bool | None = _Unset,
+    validate_default: bool | None = _Unset,
+    repr: bool = _Unset,
+    init: bool | None = _Unset,
+    init_var: bool | None = _Unset,
+    kw_only: bool | None = _Unset,
+    pattern: str | re.Pattern[str] | None = _Unset,
+    strict: bool | None = _Unset,
+    coerce_numbers_to_str: bool | None = _Unset,
+    gt: annotated_types.SupportsGt | None = _Unset,
+    ge: annotated_types.SupportsGe | None = _Unset,
+    lt: annotated_types.SupportsLt | None = _Unset,
+    le: annotated_types.SupportsLe | None = _Unset,
+    multiple_of: float | None = _Unset,
+    allow_inf_nan: bool | None = _Unset,
+    max_digits: int | None = _Unset,
+    decimal_places: int | None = _Unset,
+    min_length: int | None = _Unset,
+    max_length: int | None = _Unset,
+    union_mode: Literal['smart', 'left_to_right'] = _Unset,
+    fail_fast: bool | None = _Unset,
+    **extra: Unpack[_EmptyKwargs],
+) -> Any: ...
+def Field(  # noqa: C901
+    default: Any = PydanticUndefined,
+    *,
+    default_factory: Callable[[], Any] | Callable[[dict[str, Any]], Any] | None = _Unset,
+    alias: str | None = _Unset,
+    alias_priority: int | None = _Unset,
+    validation_alias: str | AliasPath | AliasChoices | None = _Unset,
+    serialization_alias: str | None = _Unset,
+    title: str | None = _Unset,
+    field_title_generator: Callable[[str, FieldInfo], str] | None = _Unset,
+    description: str | None = _Unset,
+    examples: list[Any] | None = _Unset,
+    exclude: bool | None = _Unset,
+    exclude_if: Callable[[Any], bool] | None = _Unset,
+    discriminator: str | types.Discriminator | None = _Unset,
+    deprecated: Deprecated | str | bool | None = _Unset,
+    json_schema_extra: JsonDict | Callable[[JsonDict], None] | None = _Unset,
+    frozen: bool | None = _Unset,
+    validate_default: bool | None = _Unset,
+    repr: bool = _Unset,
+    init: bool | None = _Unset,
+    init_var: bool | None = _Unset,
+    kw_only: bool | None = _Unset,
+    pattern: str | re.Pattern[str] | None = _Unset,
+    strict: bool | None = _Unset,
+    coerce_numbers_to_str: bool | None = _Unset,
+    gt: annotated_types.SupportsGt | None = _Unset,
+    ge: annotated_types.SupportsGe | None = _Unset,
+    lt: annotated_types.SupportsLt | None = _Unset,
+    le: annotated_types.SupportsLe | None = _Unset,
+    multiple_of: float | None = _Unset,
+    allow_inf_nan: bool | None = _Unset,
+    max_digits: int | None = _Unset,
+    decimal_places: int | None = _Unset,
+    min_length: int | None = _Unset,
+    max_length: int | None = _Unset,
+    union_mode: Literal['smart', 'left_to_right'] = _Unset,
+    fail_fast: bool | None = _Unset,
+    **extra: Unpack[_EmptyKwargs],
+) -> Any:
+    """!!! abstract "Usage Documentation"
+        [Fields](../concepts/fields.md)
+
+    Create a field for objects that can be configured.
+
+    Used to provide extra information about a field, either for the model schema or complex validation. Some arguments
+    apply only to number fields (`int`, `float`, `Decimal`) and some apply only to `str`.
+
+    Note:
+        - Any `_Unset` objects will be replaced by the corresponding value defined in the `_DefaultValues` dictionary. If a key for the `_Unset` object is not found in the `_DefaultValues` dictionary, it will default to `None`
+
+    Args:
+        default: Default value if the field is not set.
+        default_factory: A callable to generate the default value. The callable can either take 0 arguments
+            (in which case it is called as is) or a single argument containing the already validated data.
+        alias: The name to use for the attribute when validating or serializing by alias.
+            This is often used for things like converting between snake and camel case.
+        alias_priority: Priority of the alias. This affects whether an alias generator is used.
+        validation_alias: Like `alias`, but only affects validation, not serialization.
+        serialization_alias: Like `alias`, but only affects serialization, not validation.
+        title: Human-readable title.
+        field_title_generator: A callable that takes a field name and returns title for it.
+        description: Human-readable description.
+        examples: Example values for this field.
+        exclude: Whether to exclude the field from the model serialization.
+        exclude_if: A callable that determines whether to exclude a field during serialization based on its value.
+        discriminator: Field name or Discriminator for discriminating the type in a tagged union.
+        deprecated: A deprecation message, an instance of `warnings.deprecated` or the `typing_extensions.deprecated` backport,
+            or a boolean. If `True`, a default deprecation message will be emitted when accessing the field.
+        json_schema_extra: A dict or callable to provide extra JSON schema properties.
+        frozen: Whether the field is frozen. If true, attempts to change the value on an instance will raise an error.
+        validate_default: If `True`, apply validation to the default value every time you create an instance.
+            Otherwise, for performance reasons, the default value of the field is trusted and not validated.
+        repr: A boolean indicating whether to include the field in the `__repr__` output.
+        init: Whether the field should be included in the constructor of the dataclass.
+            (Only applies to dataclasses.)
+        init_var: Whether the field should _only_ be included in the constructor of the dataclass.
+            (Only applies to dataclasses.)
+        kw_only: Whether the field should be a keyword-only argument in the constructor of the dataclass.
+            (Only applies to dataclasses.)
+        coerce_numbers_to_str: Whether to enable coercion of any `Number` type to `str` (not applicable in `strict` mode).
+        strict: If `True`, strict validation is applied to the field.
+            See [Strict Mode](../concepts/strict_mode.md) for details.
+        gt: Greater than. If set, value must be greater than this. Only applicable to numbers.
+        ge: Greater than or equal. If set, value must be greater than or equal to this. Only applicable to numbers.
+        lt: Less than. If set, value must be less than this. Only applicable to numbers.
+        le: Less than or equal. If set, value must be less than or equal to this. Only applicable to numbers.
+        multiple_of: Value must be a multiple of this. Only applicable to numbers.
+        min_length: Minimum length for iterables.
+        max_length: Maximum length for iterables.
+        pattern: Pattern for strings (a regular expression).
+        allow_inf_nan: Allow `inf`, `-inf`, `nan`. Only applicable to float and [`Decimal`][decimal.Decimal] numbers.
+        max_digits: Maximum number of allow digits for strings.
+        decimal_places: Maximum number of decimal places allowed for numbers.
+        union_mode: The strategy to apply when validating a union. Can be `smart` (the default), or `left_to_right`.
+            See [Union Mode](../concepts/unions.md#union-modes) for details.
+        fail_fast: If `True`, validation will stop on the first error. If `False`, all validation errors will be collected.
+            This option can be applied only to iterable types (list, tuple, set, and frozenset).
+        extra: (Deprecated) Extra fields that will be included in the JSON schema.
+
+            !!! warning Deprecated
+                The `extra` kwargs is deprecated. Use `json_schema_extra` instead.
+
+    Returns:
+        A new [`FieldInfo`][pydantic.fields.FieldInfo]. The return annotation is `Any` so `Field` can be used on
+            type-annotated fields without causing a type error.
+    """
+    # Check deprecated and removed params from V1. This logic should eventually be removed.
+    const = extra.pop('const', None)  # type: ignore
+    if const is not None:
+        raise PydanticUserError('`const` is removed, use `Literal` instead', code='removed-kwargs')
+
+    min_items = extra.pop('min_items', None)  # type: ignore
+    if min_items is not None:
+        warn(
+            '`min_items` is deprecated and will be removed, use `min_length` instead',
+            PydanticDeprecatedSince20,
+            stacklevel=2,
+        )
+        if min_length in (None, _Unset):
+            min_length = min_items  # type: ignore
+
+    max_items = extra.pop('max_items', None)  # type: ignore
+    if max_items is not None:
+        warn(
+            '`max_items` is deprecated and will be removed, use `max_length` instead',
+            PydanticDeprecatedSince20,
+            stacklevel=2,
+        )
+        if max_length in (None, _Unset):
+            max_length = max_items  # type: ignore
+
+    unique_items = extra.pop('unique_items', None)  # type: ignore
+    if unique_items is not None:
+        raise PydanticUserError(
+            (
+                '`unique_items` is removed, use `Set` instead'
+                '(this feature is discussed in https://github.com/pydantic/pydantic-core/issues/296)'
+            ),
+            code='removed-kwargs',
+        )
+
+    allow_mutation = extra.pop('allow_mutation', None)  # type: ignore
+    if allow_mutation is not None:
+        warn(
+            '`allow_mutation` is deprecated and will be removed. use `frozen` instead',
+            PydanticDeprecatedSince20,
+            stacklevel=2,
+        )
+        if allow_mutation is False:
+            frozen = True
+
+    regex = extra.pop('regex', None)  # type: ignore
+    if regex is not None:
+        raise PydanticUserError('`regex` is removed. use `pattern` instead', code='removed-kwargs')
+
+    if extra:
+        warn(
+            'Using extra keyword arguments on `Field` is deprecated and will be removed.'
+            ' Use `json_schema_extra` instead.'
+            f' (Extra keys: {", ".join(k.__repr__() for k in extra.keys())})',
+            PydanticDeprecatedSince20,
+            stacklevel=2,
+        )
+        if not json_schema_extra or json_schema_extra is _Unset:
+            json_schema_extra = extra  # type: ignore
+
+    if (
+        validation_alias
+        and validation_alias is not _Unset
+        and not isinstance(validation_alias, (str, AliasChoices, AliasPath))
+    ):
+        raise TypeError('Invalid `validation_alias` type. it should be `str`, `AliasChoices`, or `AliasPath`')
+
+    if serialization_alias in (_Unset, None) and isinstance(alias, str):
+        serialization_alias = alias
+
+    if validation_alias in (_Unset, None):
+        validation_alias = alias
+
+    include = extra.pop('include', None)  # type: ignore
+    if include is not None:
+        warn(
+            '`include` is deprecated and does nothing. It will be removed, use `exclude` instead',
+            PydanticDeprecatedSince20,
+            stacklevel=2,
+        )
+
+    return FieldInfo.from_field(
+        default,
+        default_factory=default_factory,
+        alias=alias,
+        alias_priority=alias_priority,
+        validation_alias=validation_alias,
+        serialization_alias=serialization_alias,
+        title=title,
+        field_title_generator=field_title_generator,
+        description=description,
+        examples=examples,
+        exclude=exclude,
+        exclude_if=exclude_if,
+        discriminator=discriminator,
+        deprecated=deprecated,
+        json_schema_extra=json_schema_extra,
+        frozen=frozen,
+        pattern=pattern,
+        validate_default=validate_default,
+        repr=repr,
+        init=init,
+        init_var=init_var,
+        kw_only=kw_only,
+        coerce_numbers_to_str=coerce_numbers_to_str,
+        strict=strict,
+        gt=gt,
+        ge=ge,
+        lt=lt,
+        le=le,
+        multiple_of=multiple_of,
+        min_length=min_length,
+        max_length=max_length,
+        allow_inf_nan=allow_inf_nan,
+        max_digits=max_digits,
+        decimal_places=decimal_places,
+        union_mode=union_mode,
+        fail_fast=fail_fast,
+    )
+
+
+_FIELD_ARG_NAMES = set(inspect.signature(Field).parameters)
+_FIELD_ARG_NAMES.remove('extra')  # do not include the varkwargs parameter
+
+
+class ModelPrivateAttr(_repr.Representation):
+    """A descriptor for private attributes in class models.
+
+    !!! warning
+        You generally shouldn't be creating `ModelPrivateAttr` instances directly, instead use
+        `pydantic.fields.PrivateAttr`. (This is similar to `FieldInfo` vs. `Field`.)
+
+    Attributes:
+        default: The default value of the attribute if not provided.
+        default_factory: A callable to generate the default value. The callable can either take 0 arguments
+            (in which case it is called as is) or a single argument containing the validated data (the model's
+            [`__dict__`][object.__dict__]) and the already initialized private attributes.
+    """
+
+    __slots__ = ('default', 'default_factory')
+
+    def __init__(
+        self,
+        default: Any = PydanticUndefined,
+        *,
+        default_factory: Callable[[], Any] | Callable[[dict[str, Any]], Any] | None = None,
+    ) -> None:
+        if default is Ellipsis:
+            self.default = PydanticUndefined
+        else:
+            self.default = default
+        self.default_factory = default_factory
+
+    if not TYPE_CHECKING:
+        # We put `__getattr__` in a non-TYPE_CHECKING block because otherwise, mypy allows arbitrary attribute access
+
+        def __getattr__(self, item: str) -> Any:
+            """This function improves compatibility with custom descriptors by ensuring delegation happens
+            as expected when the default value of a private attribute is a descriptor.
+            """
+            if item in {'__get__', '__set__', '__delete__'}:
+                if hasattr(self.default, item):
+                    return getattr(self.default, item)
+            raise AttributeError(f'{type(self).__name__!r} object has no attribute {item!r}')
+
+    def __set_name__(self, cls: type[Any], name: str) -> None:
+        """Preserve `__set_name__` protocol defined in https://peps.python.org/pep-0487."""
+        default = self.default
+        if default is PydanticUndefined:
+            return
+        set_name = getattr(default, '__set_name__', None)
+        if callable(set_name):
+            set_name(cls, name)
+
+    @property
+    def default_factory_takes_validated_data(self) -> bool | None:
+        """Whether the provided default factory callable has a validated data parameter.
+
+        Returns `None` if no default factory is set.
+        """
+        if self.default_factory is not None:
+            return _fields.takes_validated_data_argument(self.default_factory)
+
+    @overload
+    def get_default(
+        self, *, call_default_factory: Literal[True], validated_data: dict[str, Any] | None = None
+    ) -> Any: ...
+
+    @overload
+    def get_default(self, *, call_default_factory: Literal[False] = ...) -> Any: ...
+
+    def get_default(self, *, call_default_factory: bool = False, validated_data: dict[str, Any] | None = None) -> Any:
+        """Get the default value.
+
+        We expose an option for whether to call the default_factory (if present), as calling it may
+        result in side effects that we want to avoid. However, there are times when it really should
+        be called (namely, when instantiating a model via `model_construct`).
+
+        Args:
+            call_default_factory: Whether to call the default factory or not.
+            validated_data: The already validated data to be passed to the default factory.
+
+        Returns:
+            The default value, calling the default factory if requested or `None` if not set.
+        """
+        return _fields.resolve_default_value(
+            default=self.default,
+            default_factory=self.default_factory,
+            validated_data=validated_data,
+            call_default_factory=call_default_factory,
+        )
+
+    def __eq__(self, other: Any) -> bool:
+        return isinstance(other, self.__class__) and (self.default, self.default_factory) == (
+            other.default,
+            other.default_factory,
+        )
+
+
+# NOTE: Actual return type is 'ModelPrivateAttr', but we want to help type checkers
+# to understand the magic that happens at runtime.
+@overload  # `default` argument set
+def PrivateAttr(
+    default: _T,
+    *,
+    init: Literal[False] = False,
+) -> _T: ...
+@overload  # `default_factory` argument set
+def PrivateAttr(
+    *,
+    default_factory: Callable[[], _T] | Callable[[dict[str, Any]], _T],
+    init: Literal[False] = False,
+) -> _T: ...
+@overload  # No default set
+def PrivateAttr(
+    *,
+    init: Literal[False] = False,
+) -> Any: ...
+def PrivateAttr(
+    default: Any = PydanticUndefined,
+    *,
+    default_factory: Callable[[], Any] | Callable[[dict[str, Any]], Any] | None = None,
+    init: Literal[False] = False,
+) -> Any:
+    """!!! abstract "Usage Documentation"
+        [Private Model Attributes](../concepts/models.md#private-model-attributes)
+
+    Indicates that an attribute is intended for private use and not handled during normal validation/serialization.
+
+    Private attributes are not validated by Pydantic, so it's up to you to ensure they are used in a type-safe manner.
+
+    Private attributes are stored in `__private_attributes__` on the model.
+
+    Args:
+        default: The attribute's default value. Defaults to Undefined.
+        default_factory: A callable to generate the default value. The callable can either take 0 arguments
+            (in which case it is called as is) or a single argument containing the validated data (the model's
+            [`__dict__`][object.__dict__]) and the already initialized private attributes.
+            If both `default` and `default_factory` are set, an error will be raised.
+        init: Whether the attribute should be included in the constructor of the dataclass. Always `False`.
+
+    Returns:
+        An instance of [`ModelPrivateAttr`][pydantic.fields.ModelPrivateAttr] class.
+
+    Raises:
+        ValueError: If both `default` and `default_factory` are set.
+    """
+    if default is not PydanticUndefined and default_factory is not None:
+        raise TypeError('cannot specify both default and default_factory')
+
+    return ModelPrivateAttr(
+        default,
+        default_factory=default_factory,
+    )
+
+
+@dataclasses.dataclass(**_internal_dataclass.slots_true)
+class ComputedFieldInfo:
+    """A container for data from `@computed_field` so that we can access it while building the pydantic-core schema.
+
+    Attributes:
+        decorator_repr: A class variable representing the decorator string, '@computed_field'.
+        wrapped_property: The wrapped computed field property.
+        return_type: The type of the computed field property's return value.
+        alias: The alias of the property to be used during serialization.
+        alias_priority: The priority of the alias. This affects whether an alias generator is used.
+        title: Title of the computed field to include in the serialization JSON schema.
+        field_title_generator: A callable that takes a field name and returns title for it.
+        description: Description of the computed field to include in the serialization JSON schema.
+        deprecated: A deprecation message, an instance of `warnings.deprecated` or the `typing_extensions.deprecated` backport,
+            or a boolean. If `True`, a default deprecation message will be emitted when accessing the field.
+        examples: Example values of the computed field to include in the serialization JSON schema.
+        json_schema_extra: A dict or callable to provide extra JSON schema properties.
+        repr: A boolean indicating whether to include the field in the __repr__ output.
+    """
+
+    decorator_repr: ClassVar[str] = '@computed_field'
+    wrapped_property: property
+    return_type: Any
+    alias: str | None
+    alias_priority: int | None
+    exclude_if: Callable[[Any], bool] | None
+    title: str | None
+    field_title_generator: Callable[[str, ComputedFieldInfo], str] | None
+    description: str | None
+    deprecated: Deprecated | str | bool | None
+    examples: list[Any] | None
+    json_schema_extra: JsonDict | Callable[[JsonDict], None] | None
+    repr: bool
+    # NOTE: if you add a new field, add it to the `__copy__()` implementation.
+
+    def __copy__(self) -> Self:
+        return type(self)(
+            wrapped_property=self.wrapped_property,
+            return_type=self.return_type,
+            alias=self.alias,
+            alias_priority=self.alias_priority,
+            exclude_if=self.exclude_if,
+            title=self.title,
+            field_title_generator=self.field_title_generator,
+            description=self.description,
+            deprecated=self.deprecated,
+            examples=self.examples.copy() if isinstance(self.examples, list) else self.examples,
+            json_schema_extra=self.json_schema_extra.copy()
+            if isinstance(self.json_schema_extra, dict)
+            else self.json_schema_extra,
+            repr=self.repr,
+        )
+
+    @property
+    def deprecation_message(self) -> str | None:
+        """The deprecation message to be emitted, or `None` if not set."""
+        if self.deprecated is None:
+            return None
+        if isinstance(self.deprecated, bool):
+            return 'deprecated' if self.deprecated else None
+        return self.deprecated if isinstance(self.deprecated, str) else self.deprecated.message
+
+    def _update_from_config(self, config_wrapper: ConfigWrapper, name: str) -> None:
+        """Update the instance from the configuration set on the class this computed field belongs to."""
+        title_generator = self.field_title_generator or config_wrapper.field_title_generator
+        if title_generator is not None and self.title is None:
+            self.title = title_generator(name, self)
+        if config_wrapper.alias_generator is not None:
+            self._apply_alias_generator(config_wrapper.alias_generator, name)
+
+    def _apply_alias_generator(self, alias_generator: Callable[[str], str] | AliasGenerator, name: str) -> None:
+        """Apply an alias generator to aliases if appropriate.
+
+        Args:
+            alias_generator: A callable that takes a string and returns a string, or an `AliasGenerator` instance.
+            name: The name of the computed field from which to generate the alias.
+        """
+        # Apply an alias_generator if
+        # 1. An alias is not specified
+        # 2. An alias is specified, but the priority is <= 1
+
+        if self.alias_priority is None or self.alias_priority <= 1 or self.alias is None:
+            alias, _, serialization_alias = None, None, None
+
+            if isinstance(alias_generator, AliasGenerator):
+                alias, _, serialization_alias = alias_generator.generate_aliases(name)
+            elif callable(alias_generator):
+                alias = alias_generator(name)
+
+            # if priority is not set, we set to 1
+            # which supports the case where the alias_generator from a child class is used
+            # to generate an alias for a field in a parent class
+            if self.alias_priority is None or self.alias_priority <= 1:
+                self.alias_priority = 1
+
+            # if the priority is 1, then we set the aliases to the generated alias
+            # note that we use the serialization_alias with priority over alias, as computed_field
+            # aliases are used for serialization only (not validation)
+            if self.alias_priority == 1:
+                self.alias = _utils.get_first_not_none(serialization_alias, alias)
+
+
+def _wrapped_property_is_private(property_: cached_property | property) -> bool:  # type: ignore
+    """Returns true if provided property is private, False otherwise."""
+    wrapped_name: str = ''
+
+    if isinstance(property_, property):
+        wrapped_name = getattr(property_.fget, '__name__', '')
+    elif isinstance(property_, cached_property):  # type: ignore
+        wrapped_name = getattr(property_.func, '__name__', '')  # type: ignore
+
+    return wrapped_name.startswith('_') and not wrapped_name.startswith('__')
+
+
+# this should really be `property[T], cached_property[T]` but property is not generic unlike cached_property
+# See https://github.com/python/typing/issues/985 and linked issues
+PropertyT = TypeVar('PropertyT')
+
+
+@overload
+def computed_field(func: PropertyT, /) -> PropertyT: ...
+
+
+@overload
+def computed_field(
+    *,
+    alias: str | None = None,
+    alias_priority: int | None = None,
+    exclude_if: Callable[[Any], bool] | None = None,
+    title: str | None = None,
+    field_title_generator: Callable[[str, ComputedFieldInfo], str] | None = None,
+    description: str | None = None,
+    deprecated: Deprecated | str | bool | None = None,
+    examples: list[Any] | None = None,
+    json_schema_extra: JsonDict | Callable[[JsonDict], None] | None = None,
+    repr: bool = True,
+    return_type: Any = PydanticUndefined,
+) -> Callable[[PropertyT], PropertyT]: ...
+
+
+def computed_field(
+    func: PropertyT | None = None,
+    /,
+    *,
+    alias: str | None = None,
+    alias_priority: int | None = None,
+    exclude_if: Callable[[Any], bool] | None = None,
+    title: str | None = None,
+    field_title_generator: Callable[[str, ComputedFieldInfo], str] | None = None,
+    description: str | None = None,
+    deprecated: Deprecated | str | bool | None = None,
+    examples: list[Any] | None = None,
+    json_schema_extra: JsonDict | Callable[[JsonDict], None] | None = None,
+    repr: bool | None = None,
+    return_type: Any = PydanticUndefined,
+) -> PropertyT | Callable[[PropertyT], PropertyT]:
+    """!!! abstract "Usage Documentation"
+        [The `computed_field` decorator](../concepts/fields.md#the-computed_field-decorator)
+
+    Decorator to include `property` and `cached_property` when serializing models or dataclasses.
+
+    This is useful for fields that are computed from other fields, or for fields that are expensive to compute and should be cached.
+
+    ```python
+    from pydantic import BaseModel, computed_field
+
+    class Rectangle(BaseModel):
+        width: int
+        length: int
+
+        @computed_field
+        @property
+        def area(self) -> int:
+            return self.width * self.length
+
+    print(Rectangle(width=3, length=2).model_dump())
+    #> {'width': 3, 'length': 2, 'area': 6}
+    ```
+
+    If applied to functions not yet decorated with `@property` or `@cached_property`, the function is
+    automatically wrapped with `property`. Although this is more concise, you will lose IntelliSense in your IDE,
+    and confuse static type checkers, thus explicit use of `@property` is recommended.
+
+    !!! warning "Mypy Warning"
+        Even with the `@property` or `@cached_property` applied to your function before `@computed_field`,
+        mypy may throw a `Decorated property not supported` error.
+        See [mypy issue #1362](https://github.com/python/mypy/issues/1362), for more information.
+        To avoid this error message, add `# type: ignore[prop-decorator]` to the `@computed_field` line.
+
+        [pyright](https://github.com/microsoft/pyright) supports `@computed_field` without error.
+
+    ```python
+    import random
+
+    from pydantic import BaseModel, computed_field
+
+    class Square(BaseModel):
+        width: float
+
+        @computed_field
+        def area(self) -> float:  # converted to a `property` by `computed_field`
+            return round(self.width**2, 2)
+
+        @area.setter
+        def area(self, new_area: float) -> None:
+            self.width = new_area**0.5
+
+        @computed_field(alias='the magic number', repr=False)
+        def random_number(self) -> int:
+            return random.randint(0, 1_000)
+
+    square = Square(width=1.3)
+
+    # `random_number` does not appear in representation
+    print(repr(square))
+    #> Square(width=1.3, area=1.69)
+
+    print(square.random_number)
+    #> 3
+
+    square.area = 4
+
+    print(square.model_dump_json(by_alias=True))
+    #> {"width":2.0,"area":4.0,"the magic number":3}
+    ```
+
+    !!! warning "Overriding with `computed_field`"
+        You can't override a field from a parent class with a `computed_field` in the child class.
+        `mypy` complains about this behavior if allowed, and `dataclasses` doesn't allow this pattern either.
+        See the example below:
+
+    ```python
+    from pydantic import BaseModel, computed_field
+
+    class Parent(BaseModel):
+        a: str
+
+    try:
+
+        class Child(Parent):
+            @computed_field
+            @property
+            def a(self) -> str:
+                return 'new a'
+
+    except TypeError as e:
+        print(e)
+        '''
+        Field 'a' of class 'Child' overrides symbol of same name in a parent class. This override with a computed_field is incompatible.
+        '''
+    ```
+
+    Private properties decorated with `@computed_field` have `repr=False` by default.
+
+    ```python
+    from functools import cached_property
+
+    from pydantic import BaseModel, computed_field
+
+    class Model(BaseModel):
+        foo: int
+
+        @computed_field
+        @cached_property
+        def _private_cached_property(self) -> int:
+            return -self.foo
+
+        @computed_field
+        @property
+        def _private_property(self) -> int:
+            return -self.foo
+
+    m = Model(foo=1)
+    print(repr(m))
+    #> Model(foo=1)
+    ```
+
+    Args:
+        func: the function to wrap.
+        alias: alias to use when serializing this computed field, only used when `by_alias=True`
+        alias_priority: priority of the alias. This affects whether an alias generator is used
+        exclude_if: A callable that determines whether to exclude this computed field during serialization based on its value.
+        title: Title to use when including this computed field in JSON Schema
+        field_title_generator: A callable that takes a field name and returns title for it.
+        description: Description to use when including this computed field in JSON Schema, defaults to the function's
+            docstring
+        deprecated: A deprecation message (or an instance of `warnings.deprecated` or the `typing_extensions.deprecated` backport).
+            to be emitted when accessing the field. Or a boolean. This will automatically be set if the property is decorated with the
+            `deprecated` decorator.
+        examples: Example values to use when including this computed field in JSON Schema
+        json_schema_extra: A dict or callable to provide extra JSON schema properties.
+        repr: whether to include this computed field in model repr.
+            Default is `False` for private properties and `True` for public properties.
+        return_type: optional return for serialization logic to expect when serializing to JSON, if included
+            this must be correct, otherwise a `TypeError` is raised.
+            If you don't include a return type Any is used, which does runtime introspection to handle arbitrary
+            objects.
+
+    Returns:
+        A proxy wrapper for the property.
+    """
+
+    def dec(f: Any) -> Any:
+        nonlocal description, deprecated, return_type, alias_priority
+        unwrapped = _decorators.unwrap_wrapped_function(f)
+
+        if description is None and unwrapped.__doc__:
+            description = inspect.cleandoc(unwrapped.__doc__)
+
+        if deprecated is None and hasattr(unwrapped, '__deprecated__'):
+            deprecated = unwrapped.__deprecated__
+
+        # if the function isn't already decorated with `@property` (or another descriptor), then we wrap it now
+        f = _decorators.ensure_property(f)
+        alias_priority = (alias_priority or 2) if alias is not None else None
+
+        if repr is None:
+            repr_: bool = not _wrapped_property_is_private(property_=f)
+        else:
+            repr_ = repr
+
+        dec_info = ComputedFieldInfo(
+            f,
+            return_type,
+            alias,
+            alias_priority,
+            exclude_if,
+            title,
+            field_title_generator,
+            description,
+            deprecated,
+            examples,
+            json_schema_extra,
+            repr_,
+        )
+        return _decorators.PydanticDescriptorProxy(f, dec_info)
+
+    if func is None:
+        return dec
+    else:
+        return dec(func)

.mcp-venv/lib/python3.13/site-packages/pydantic/functional_serializers.py

@@ -0,0 +1,470 @@
+"""This module contains related classes and functions for serialization."""
+
+from __future__ import annotations
+
+import dataclasses
+from functools import partial, partialmethod
+from typing import TYPE_CHECKING, Annotated, Any, Callable, Literal, TypeVar, overload
+
+from pydantic_core import PydanticUndefined, core_schema
+from pydantic_core.core_schema import SerializationInfo, SerializerFunctionWrapHandler, WhenUsed
+from typing_extensions import TypeAlias
+
+from . import PydanticUndefinedAnnotation
+from ._internal import _decorators, _internal_dataclass
+from .annotated_handlers import GetCoreSchemaHandler
+from .errors import PydanticUserError
+
+
+@dataclasses.dataclass(**_internal_dataclass.slots_true, frozen=True)
+class PlainSerializer:
+    """Plain serializers use a function to modify the output of serialization.
+
+    This is particularly helpful when you want to customize the serialization for annotated types.
+    Consider an input of `list`, which will be serialized into a space-delimited string.
+
+    ```python
+    from typing import Annotated
+
+    from pydantic import BaseModel, PlainSerializer
+
+    CustomStr = Annotated[
+        list, PlainSerializer(lambda x: ' '.join(x), return_type=str)
+    ]
+
+    class StudentModel(BaseModel):
+        courses: CustomStr
+
+    student = StudentModel(courses=['Math', 'Chemistry', 'English'])
+    print(student.model_dump())
+    #> {'courses': 'Math Chemistry English'}
+    ```
+
+    Attributes:
+        func: The serializer function.
+        return_type: The return type for the function. If omitted it will be inferred from the type annotation.
+        when_used: Determines when this serializer should be used. Accepts a string with values `'always'`,
+            `'unless-none'`, `'json'`, and `'json-unless-none'`. Defaults to 'always'.
+    """
+
+    func: core_schema.SerializerFunction
+    return_type: Any = PydanticUndefined
+    when_used: WhenUsed = 'always'
+
+    def __get_pydantic_core_schema__(self, source_type: Any, handler: GetCoreSchemaHandler) -> core_schema.CoreSchema:
+        """Gets the Pydantic core schema.
+
+        Args:
+            source_type: The source type.
+            handler: The `GetCoreSchemaHandler` instance.
+
+        Returns:
+            The Pydantic core schema.
+        """
+        schema = handler(source_type)
+        if self.return_type is not PydanticUndefined:
+            return_type = self.return_type
+        else:
+            try:
+                # Do not pass in globals as the function could be defined in a different module.
+                # Instead, let `get_callable_return_type` infer the globals to use, but still pass
+                # in locals that may contain a parent/rebuild namespace:
+                return_type = _decorators.get_callable_return_type(
+                    self.func,
+                    localns=handler._get_types_namespace().locals,
+                )
+            except NameError as e:
+                raise PydanticUndefinedAnnotation.from_name_error(e) from e
+
+        return_schema = None if return_type is PydanticUndefined else handler.generate_schema(return_type)
+        schema['serialization'] = core_schema.plain_serializer_function_ser_schema(
+            function=self.func,
+            info_arg=_decorators.inspect_annotated_serializer(self.func, 'plain'),
+            return_schema=return_schema,
+            when_used=self.when_used,
+        )
+        return schema
+
+
+@dataclasses.dataclass(**_internal_dataclass.slots_true, frozen=True)
+class WrapSerializer:
+    """Wrap serializers receive the raw inputs along with a handler function that applies the standard serialization
+    logic, and can modify the resulting value before returning it as the final output of serialization.
+
+    For example, here's a scenario in which a wrap serializer transforms timezones to UTC **and** utilizes the existing `datetime` serialization logic.
+
+    ```python
+    from datetime import datetime, timezone
+    from typing import Annotated, Any
+
+    from pydantic import BaseModel, WrapSerializer
+
+    class EventDatetime(BaseModel):
+        start: datetime
+        end: datetime
+
+    def convert_to_utc(value: Any, handler, info) -> dict[str, datetime]:
+        # Note that `handler` can actually help serialize the `value` for
+        # further custom serialization in case it's a subclass.
+        partial_result = handler(value, info)
+        if info.mode == 'json':
+            return {
+                k: datetime.fromisoformat(v).astimezone(timezone.utc)
+                for k, v in partial_result.items()
+            }
+        return {k: v.astimezone(timezone.utc) for k, v in partial_result.items()}
+
+    UTCEventDatetime = Annotated[EventDatetime, WrapSerializer(convert_to_utc)]
+
+    class EventModel(BaseModel):
+        event_datetime: UTCEventDatetime
+
+    dt = EventDatetime(
+        start='2024-01-01T07:00:00-08:00', end='2024-01-03T20:00:00+06:00'
+    )
+    event = EventModel(event_datetime=dt)
+    print(event.model_dump())
+    '''
+    {
+        'event_datetime': {
+            'start': datetime.datetime(
+                2024, 1, 1, 15, 0, tzinfo=datetime.timezone.utc
+            ),
+            'end': datetime.datetime(
+                2024, 1, 3, 14, 0, tzinfo=datetime.timezone.utc
+            ),
+        }
+    }
+    '''
+
+    print(event.model_dump_json())
+    '''
+    {"event_datetime":{"start":"2024-01-01T15:00:00Z","end":"2024-01-03T14:00:00Z"}}
+    '''
+    ```
+
+    Attributes:
+        func: The serializer function to be wrapped.
+        return_type: The return type for the function. If omitted it will be inferred from the type annotation.
+        when_used: Determines when this serializer should be used. Accepts a string with values `'always'`,
+            `'unless-none'`, `'json'`, and `'json-unless-none'`. Defaults to 'always'.
+    """
+
+    func: core_schema.WrapSerializerFunction
+    return_type: Any = PydanticUndefined
+    when_used: WhenUsed = 'always'
+
+    def __get_pydantic_core_schema__(self, source_type: Any, handler: GetCoreSchemaHandler) -> core_schema.CoreSchema:
+        """This method is used to get the Pydantic core schema of the class.
+
+        Args:
+            source_type: Source type.
+            handler: Core schema handler.
+
+        Returns:
+            The generated core schema of the class.
+        """
+        schema = handler(source_type)
+        if self.return_type is not PydanticUndefined:
+            return_type = self.return_type
+        else:
+            try:
+                # Do not pass in globals as the function could be defined in a different module.
+                # Instead, let `get_callable_return_type` infer the globals to use, but still pass
+                # in locals that may contain a parent/rebuild namespace:
+                return_type = _decorators.get_callable_return_type(
+                    self.func,
+                    localns=handler._get_types_namespace().locals,
+                )
+            except NameError as e:
+                raise PydanticUndefinedAnnotation.from_name_error(e) from e
+
+        return_schema = None if return_type is PydanticUndefined else handler.generate_schema(return_type)
+        schema['serialization'] = core_schema.wrap_serializer_function_ser_schema(
+            function=self.func,
+            info_arg=_decorators.inspect_annotated_serializer(self.func, 'wrap'),
+            return_schema=return_schema,
+            when_used=self.when_used,
+        )
+        return schema
+
+
+if TYPE_CHECKING:
+    _Partial: TypeAlias = 'partial[Any] | partialmethod[Any]'
+
+    FieldPlainSerializer: TypeAlias = 'core_schema.SerializerFunction | _Partial'
+    """A field serializer method or function in `plain` mode."""
+
+    FieldWrapSerializer: TypeAlias = 'core_schema.WrapSerializerFunction | _Partial'
+    """A field serializer method or function in `wrap` mode."""
+
+    FieldSerializer: TypeAlias = 'FieldPlainSerializer | FieldWrapSerializer'
+    """A field serializer method or function."""
+
+    _FieldPlainSerializerT = TypeVar('_FieldPlainSerializerT', bound=FieldPlainSerializer)
+    _FieldWrapSerializerT = TypeVar('_FieldWrapSerializerT', bound=FieldWrapSerializer)
+
+
+@overload
+def field_serializer(
+    field: str,
+    /,
+    *fields: str,
+    mode: Literal['wrap'],
+    return_type: Any = ...,
+    when_used: WhenUsed = ...,
+    check_fields: bool | None = ...,
+) -> Callable[[_FieldWrapSerializerT], _FieldWrapSerializerT]: ...
+
+
+@overload
+def field_serializer(
+    field: str,
+    /,
+    *fields: str,
+    mode: Literal['plain'] = ...,
+    return_type: Any = ...,
+    when_used: WhenUsed = ...,
+    check_fields: bool | None = ...,
+) -> Callable[[_FieldPlainSerializerT], _FieldPlainSerializerT]: ...
+
+
+def field_serializer(  # noqa: D417
+    field: str,
+    /,
+    *fields: str,
+    mode: Literal['plain', 'wrap'] = 'plain',
+    # TODO PEP 747 (grep for 'return_type' on the whole code base):
+    return_type: Any = PydanticUndefined,
+    when_used: WhenUsed = 'always',
+    check_fields: bool | None = None,
+) -> (
+    Callable[[_FieldWrapSerializerT], _FieldWrapSerializerT]
+    | Callable[[_FieldPlainSerializerT], _FieldPlainSerializerT]
+):
+    """Decorator that enables custom field serialization.
+
+    In the below example, a field of type `set` is used to mitigate duplication. A `field_serializer` is used to serialize the data as a sorted list.
+
+    ```python
+    from pydantic import BaseModel, field_serializer
+
+    class StudentModel(BaseModel):
+        name: str = 'Jane'
+        courses: set[str]
+
+        @field_serializer('courses', when_used='json')
+        def serialize_courses_in_order(self, courses: set[str]):
+            return sorted(courses)
+
+    student = StudentModel(courses={'Math', 'Chemistry', 'English'})
+    print(student.model_dump_json())
+    #> {"name":"Jane","courses":["Chemistry","English","Math"]}
+    ```
+
+    See [the usage documentation](../concepts/serialization.md#serializers) for more information.
+
+    Four signatures are supported for the decorated serializer:
+
+    - `(self, value: Any, info: FieldSerializationInfo)`
+    - `(self, value: Any, nxt: SerializerFunctionWrapHandler, info: FieldSerializationInfo)`
+    - `(value: Any, info: SerializationInfo)`
+    - `(value: Any, nxt: SerializerFunctionWrapHandler, info: SerializationInfo)`
+
+    Args:
+        *fields: The field names the serializer should apply to.
+        mode: The serialization mode.
+
+            - `plain` means the function will be called instead of the default serialization logic,
+            - `wrap` means the function will be called with an argument to optionally call the
+               default serialization logic.
+        return_type: Optional return type for the function, if omitted it will be inferred from the type annotation.
+        when_used: Determines the serializer will be used for serialization.
+        check_fields: Whether to check that the fields actually exist on the model.
+
+    Raises:
+        PydanticUserError:
+            - If the decorator is used without any arguments (at least one field name must be provided).
+            - If the provided field names are not strings.
+    """
+    if callable(field) or isinstance(field, classmethod):
+        raise PydanticUserError(
+            'The `@field_serializer` decorator cannot be used without arguments, at least one field must be provided. '
+            "For example: `@field_serializer('<field_name>', ...)`.",
+            code='decorator-missing-arguments',
+        )
+
+    fields = field, *fields
+    if not all(isinstance(field, str) for field in fields):
+        raise PydanticUserError(
+            'The provided field names to the `@field_serializer` decorator should be strings. '
+            "For example: `@field_serializer('<field_name_1>', '<field_name_2>', ...).`",
+            code='decorator-invalid-fields',
+        )
+
+    def dec(f: FieldSerializer) -> _decorators.PydanticDescriptorProxy[Any]:
+        dec_info = _decorators.FieldSerializerDecoratorInfo(
+            fields=fields,
+            mode=mode,
+            return_type=return_type,
+            when_used=when_used,
+            check_fields=check_fields,
+        )
+        return _decorators.PydanticDescriptorProxy(f, dec_info)  # pyright: ignore[reportArgumentType]
+
+    return dec  # pyright: ignore[reportReturnType]
+
+
+if TYPE_CHECKING:
+    # The first argument in the following callables represent the `self` type:
+
+    ModelPlainSerializerWithInfo: TypeAlias = Callable[[Any, SerializationInfo[Any]], Any]
+    """A model serializer method with the `info` argument, in `plain` mode."""
+
+    ModelPlainSerializerWithoutInfo: TypeAlias = Callable[[Any], Any]
+    """A model serializer method without the `info` argument, in `plain` mode."""
+
+    ModelPlainSerializer: TypeAlias = 'ModelPlainSerializerWithInfo | ModelPlainSerializerWithoutInfo'
+    """A model serializer method in `plain` mode."""
+
+    ModelWrapSerializerWithInfo: TypeAlias = Callable[[Any, SerializerFunctionWrapHandler, SerializationInfo[Any]], Any]
+    """A model serializer method with the `info` argument, in `wrap` mode."""
+
+    ModelWrapSerializerWithoutInfo: TypeAlias = Callable[[Any, SerializerFunctionWrapHandler], Any]
+    """A model serializer method without the `info` argument, in `wrap` mode."""
+
+    ModelWrapSerializer: TypeAlias = 'ModelWrapSerializerWithInfo | ModelWrapSerializerWithoutInfo'
+    """A model serializer method in `wrap` mode."""
+
+    ModelSerializer: TypeAlias = 'ModelPlainSerializer | ModelWrapSerializer'
+
+    _ModelPlainSerializerT = TypeVar('_ModelPlainSerializerT', bound=ModelPlainSerializer)
+    _ModelWrapSerializerT = TypeVar('_ModelWrapSerializerT', bound=ModelWrapSerializer)
+
+
+@overload
+def model_serializer(f: _ModelPlainSerializerT, /) -> _ModelPlainSerializerT: ...
+
+
+@overload
+def model_serializer(
+    *, mode: Literal['wrap'], when_used: WhenUsed = 'always', return_type: Any = ...
+) -> Callable[[_ModelWrapSerializerT], _ModelWrapSerializerT]: ...
+
+
+@overload
+def model_serializer(
+    *,
+    mode: Literal['plain'] = ...,
+    when_used: WhenUsed = 'always',
+    return_type: Any = ...,
+) -> Callable[[_ModelPlainSerializerT], _ModelPlainSerializerT]: ...
+
+
+def model_serializer(
+    f: _ModelPlainSerializerT | _ModelWrapSerializerT | None = None,
+    /,
+    *,
+    mode: Literal['plain', 'wrap'] = 'plain',
+    when_used: WhenUsed = 'always',
+    return_type: Any = PydanticUndefined,
+) -> (
+    _ModelPlainSerializerT
+    | Callable[[_ModelWrapSerializerT], _ModelWrapSerializerT]
+    | Callable[[_ModelPlainSerializerT], _ModelPlainSerializerT]
+):
+    """Decorator that enables custom model serialization.
+
+    This is useful when a model need to be serialized in a customized manner, allowing for flexibility beyond just specific fields.
+
+    An example would be to serialize temperature to the same temperature scale, such as degrees Celsius.
+
+    ```python
+    from typing import Literal
+
+    from pydantic import BaseModel, model_serializer
+
+    class TemperatureModel(BaseModel):
+        unit: Literal['C', 'F']
+        value: int
+
+        @model_serializer()
+        def serialize_model(self):
+            if self.unit == 'F':
+                return {'unit': 'C', 'value': int((self.value - 32) / 1.8)}
+            return {'unit': self.unit, 'value': self.value}
+
+    temperature = TemperatureModel(unit='F', value=212)
+    print(temperature.model_dump())
+    #> {'unit': 'C', 'value': 100}
+    ```
+
+    Two signatures are supported for `mode='plain'`, which is the default:
+
+    - `(self)`
+    - `(self, info: SerializationInfo)`
+
+    And two other signatures for `mode='wrap'`:
+
+    - `(self, nxt: SerializerFunctionWrapHandler)`
+    - `(self, nxt: SerializerFunctionWrapHandler, info: SerializationInfo)`
+
+        See [the usage documentation](../concepts/serialization.md#serializers) for more information.
+
+    Args:
+        f: The function to be decorated.
+        mode: The serialization mode.
+
+            - `'plain'` means the function will be called instead of the default serialization logic
+            - `'wrap'` means the function will be called with an argument to optionally call the default
+                serialization logic.
+        when_used: Determines when this serializer should be used.
+        return_type: The return type for the function. If omitted it will be inferred from the type annotation.
+
+    Returns:
+        The decorator function.
+    """
+
+    def dec(f: ModelSerializer) -> _decorators.PydanticDescriptorProxy[Any]:
+        dec_info = _decorators.ModelSerializerDecoratorInfo(mode=mode, return_type=return_type, when_used=when_used)
+        return _decorators.PydanticDescriptorProxy(f, dec_info)
+
+    if f is None:
+        return dec  # pyright: ignore[reportReturnType]
+    else:
+        return dec(f)  # pyright: ignore[reportReturnType]
+
+
+AnyType = TypeVar('AnyType')
+
+
+if TYPE_CHECKING:
+    SerializeAsAny = Annotated[AnyType, ...]  # SerializeAsAny[list[str]] will be treated by type checkers as list[str]
+    """Annotation used to mark a type as having duck-typing serialization behavior.
+
+    See [usage documentation](../concepts/serialization.md#serializing-with-duck-typing) for more details.
+    """
+else:
+
+    @dataclasses.dataclass(**_internal_dataclass.slots_true)
+    class SerializeAsAny:
+        """Annotation used to mark a type as having duck-typing serialization behavior.
+
+        See [usage documentation](../concepts/serialization.md#serializing-with-duck-typing) for more details.
+        """
+
+        def __class_getitem__(cls, item: Any) -> Any:
+            return Annotated[item, SerializeAsAny()]
+
+        def __get_pydantic_core_schema__(
+            self, source_type: Any, handler: GetCoreSchemaHandler
+        ) -> core_schema.CoreSchema:
+            schema = handler(source_type)
+            schema_to_update = schema
+            while schema_to_update['type'] == 'definitions':
+                schema_to_update = schema_to_update.copy()
+                schema_to_update = schema_to_update['schema']
+            schema_to_update['serialization'] = core_schema.simple_ser_schema('any')
+            return schema
+
+        __hash__ = object.__hash__

.mcp-venv/lib/python3.13/site-packages/pydantic/functional_validators.py

@@ -0,0 +1,889 @@
+"""This module contains related classes and functions for validation."""
+
+from __future__ import annotations as _annotations
+
+import dataclasses
+import sys
+import warnings
+from functools import partialmethod
+from typing import TYPE_CHECKING, Annotated, Any, Callable, Literal, TypeVar, Union, cast, overload
+
+from pydantic_core import PydanticUndefined, core_schema
+from typing_extensions import Self, TypeAlias
+
+from ._internal import _decorators, _generics, _internal_dataclass
+from .annotated_handlers import GetCoreSchemaHandler
+from .errors import PydanticUserError
+from .version import version_short
+from .warnings import ArbitraryTypeWarning, PydanticDeprecatedSince212
+
+if sys.version_info < (3, 11):
+    from typing_extensions import Protocol
+else:
+    from typing import Protocol
+
+_inspect_validator = _decorators.inspect_validator
+
+
+@dataclasses.dataclass(frozen=True, **_internal_dataclass.slots_true)
+class AfterValidator:
+    """!!! abstract "Usage Documentation"
+        [field *after* validators](../concepts/validators.md#field-after-validator)
+
+    A metadata class that indicates that a validation should be applied **after** the inner validation logic.
+
+    Attributes:
+        func: The validator function.
+
+    Example:
+        ```python
+        from typing import Annotated
+
+        from pydantic import AfterValidator, BaseModel, ValidationError
+
+        MyInt = Annotated[int, AfterValidator(lambda v: v + 1)]
+
+        class Model(BaseModel):
+            a: MyInt
+
+        print(Model(a=1).a)
+        #> 2
+
+        try:
+            Model(a='a')
+        except ValidationError as e:
+            print(e.json(indent=2))
+            '''
+            [
+              {
+                "type": "int_parsing",
+                "loc": [
+                  "a"
+                ],
+                "msg": "Input should be a valid integer, unable to parse string as an integer",
+                "input": "a",
+                "url": "https://errors.pydantic.dev/2/v/int_parsing"
+              }
+            ]
+            '''
+        ```
+    """
+
+    func: core_schema.NoInfoValidatorFunction | core_schema.WithInfoValidatorFunction
+
+    def __get_pydantic_core_schema__(self, source_type: Any, handler: GetCoreSchemaHandler) -> core_schema.CoreSchema:
+        schema = handler(source_type)
+        info_arg = _inspect_validator(self.func, mode='after', type='field')
+        if info_arg:
+            func = cast(core_schema.WithInfoValidatorFunction, self.func)
+            return core_schema.with_info_after_validator_function(func, schema=schema)
+        else:
+            func = cast(core_schema.NoInfoValidatorFunction, self.func)
+            return core_schema.no_info_after_validator_function(func, schema=schema)
+
+    @classmethod
+    def _from_decorator(cls, decorator: _decorators.Decorator[_decorators.FieldValidatorDecoratorInfo]) -> Self:
+        return cls(func=decorator.func)
+
+
+@dataclasses.dataclass(frozen=True, **_internal_dataclass.slots_true)
+class BeforeValidator:
+    """!!! abstract "Usage Documentation"
+        [field *before* validators](../concepts/validators.md#field-before-validator)
+
+    A metadata class that indicates that a validation should be applied **before** the inner validation logic.
+
+    Attributes:
+        func: The validator function.
+        json_schema_input_type: The input type used to generate the appropriate
+            JSON Schema (in validation mode). The actual input type is `Any`.
+
+    Example:
+        ```python
+        from typing import Annotated
+
+        from pydantic import BaseModel, BeforeValidator
+
+        MyInt = Annotated[int, BeforeValidator(lambda v: v + 1)]
+
+        class Model(BaseModel):
+            a: MyInt
+
+        print(Model(a=1).a)
+        #> 2
+
+        try:
+            Model(a='a')
+        except TypeError as e:
+            print(e)
+            #> can only concatenate str (not "int") to str
+        ```
+    """
+
+    func: core_schema.NoInfoValidatorFunction | core_schema.WithInfoValidatorFunction
+    json_schema_input_type: Any = PydanticUndefined
+
+    def __get_pydantic_core_schema__(self, source_type: Any, handler: GetCoreSchemaHandler) -> core_schema.CoreSchema:
+        schema = handler(source_type)
+        input_schema = (
+            None
+            if self.json_schema_input_type is PydanticUndefined
+            else handler.generate_schema(self.json_schema_input_type)
+        )
+
+        info_arg = _inspect_validator(self.func, mode='before', type='field')
+        if info_arg:
+            func = cast(core_schema.WithInfoValidatorFunction, self.func)
+            return core_schema.with_info_before_validator_function(
+                func,
+                schema=schema,
+                json_schema_input_schema=input_schema,
+            )
+        else:
+            func = cast(core_schema.NoInfoValidatorFunction, self.func)
+            return core_schema.no_info_before_validator_function(
+                func, schema=schema, json_schema_input_schema=input_schema
+            )
+
+    @classmethod
+    def _from_decorator(cls, decorator: _decorators.Decorator[_decorators.FieldValidatorDecoratorInfo]) -> Self:
+        return cls(
+            func=decorator.func,
+            json_schema_input_type=decorator.info.json_schema_input_type,
+        )
+
+
+@dataclasses.dataclass(frozen=True, **_internal_dataclass.slots_true)
+class PlainValidator:
+    """!!! abstract "Usage Documentation"
+        [field *plain* validators](../concepts/validators.md#field-plain-validator)
+
+    A metadata class that indicates that a validation should be applied **instead** of the inner validation logic.
+
+    !!! note
+        Before v2.9, `PlainValidator` wasn't always compatible with JSON Schema generation for `mode='validation'`.
+        You can now use the `json_schema_input_type` argument to specify the input type of the function
+        to be used in the JSON schema when `mode='validation'` (the default). See the example below for more details.
+
+    Attributes:
+        func: The validator function.
+        json_schema_input_type: The input type used to generate the appropriate
+            JSON Schema (in validation mode). The actual input type is `Any`.
+
+    Example:
+        ```python
+        from typing import Annotated, Union
+
+        from pydantic import BaseModel, PlainValidator
+
+        def validate(v: object) -> int:
+            if not isinstance(v, (int, str)):
+                raise ValueError(f'Expected int or str, got {type(v)}')
+
+            return int(v) + 1
+
+        MyInt = Annotated[
+            int,
+            PlainValidator(validate, json_schema_input_type=Union[str, int]),  # (1)!
+        ]
+
+        class Model(BaseModel):
+            a: MyInt
+
+        print(Model(a='1').a)
+        #> 2
+
+        print(Model(a=1).a)
+        #> 2
+        ```
+
+        1. In this example, we've specified the `json_schema_input_type` as `Union[str, int]` which indicates to the JSON schema
+        generator that in validation mode, the input type for the `a` field can be either a [`str`][] or an [`int`][].
+    """
+
+    func: core_schema.NoInfoValidatorFunction | core_schema.WithInfoValidatorFunction
+    json_schema_input_type: Any = Any
+
+    def __get_pydantic_core_schema__(self, source_type: Any, handler: GetCoreSchemaHandler) -> core_schema.CoreSchema:
+        # Note that for some valid uses of PlainValidator, it is not possible to generate a core schema for the
+        # source_type, so calling `handler(source_type)` will error, which prevents us from generating a proper
+        # serialization schema. To work around this for use cases that will not involve serialization, we simply
+        # catch any PydanticSchemaGenerationError that may be raised while attempting to build the serialization schema
+        # and abort any attempts to handle special serialization.
+        from pydantic import PydanticSchemaGenerationError
+
+        try:
+            schema = handler(source_type)
+            # TODO if `schema['serialization']` is one of `'include-exclude-dict/sequence',
+            # schema validation will fail. That's why we use 'type ignore' comments below.
+            serialization = schema.get(
+                'serialization',
+                core_schema.wrap_serializer_function_ser_schema(
+                    function=lambda v, h: h(v),
+                    schema=schema,
+                    return_schema=handler.generate_schema(source_type),
+                ),
+            )
+        except PydanticSchemaGenerationError:
+            serialization = None
+
+        input_schema = handler.generate_schema(self.json_schema_input_type)
+
+        info_arg = _inspect_validator(self.func, mode='plain', type='field')
+        if info_arg:
+            func = cast(core_schema.WithInfoValidatorFunction, self.func)
+            return core_schema.with_info_plain_validator_function(
+                func,
+                serialization=serialization,  # pyright: ignore[reportArgumentType]
+                json_schema_input_schema=input_schema,
+            )
+        else:
+            func = cast(core_schema.NoInfoValidatorFunction, self.func)
+            return core_schema.no_info_plain_validator_function(
+                func,
+                serialization=serialization,  # pyright: ignore[reportArgumentType]
+                json_schema_input_schema=input_schema,
+            )
+
+    @classmethod
+    def _from_decorator(cls, decorator: _decorators.Decorator[_decorators.FieldValidatorDecoratorInfo]) -> Self:
+        return cls(
+            func=decorator.func,
+            json_schema_input_type=decorator.info.json_schema_input_type,
+        )
+
+
+@dataclasses.dataclass(frozen=True, **_internal_dataclass.slots_true)
+class WrapValidator:
+    """!!! abstract "Usage Documentation"
+        [field *wrap* validators](../concepts/validators.md#field-wrap-validator)
+
+    A metadata class that indicates that a validation should be applied **around** the inner validation logic.
+
+    Attributes:
+        func: The validator function.
+        json_schema_input_type: The input type used to generate the appropriate
+            JSON Schema (in validation mode). The actual input type is `Any`.
+
+    ```python
+    from datetime import datetime
+    from typing import Annotated
+
+    from pydantic import BaseModel, ValidationError, WrapValidator
+
+    def validate_timestamp(v, handler):
+        if v == 'now':
+            # we don't want to bother with further validation, just return the new value
+            return datetime.now()
+        try:
+            return handler(v)
+        except ValidationError:
+            # validation failed, in this case we want to return a default value
+            return datetime(2000, 1, 1)
+
+    MyTimestamp = Annotated[datetime, WrapValidator(validate_timestamp)]
+
+    class Model(BaseModel):
+        a: MyTimestamp
+
+    print(Model(a='now').a)
+    #> 2032-01-02 03:04:05.000006
+    print(Model(a='invalid').a)
+    #> 2000-01-01 00:00:00
+    ```
+    """
+
+    func: core_schema.NoInfoWrapValidatorFunction | core_schema.WithInfoWrapValidatorFunction
+    json_schema_input_type: Any = PydanticUndefined
+
+    def __get_pydantic_core_schema__(self, source_type: Any, handler: GetCoreSchemaHandler) -> core_schema.CoreSchema:
+        schema = handler(source_type)
+        input_schema = (
+            None
+            if self.json_schema_input_type is PydanticUndefined
+            else handler.generate_schema(self.json_schema_input_type)
+        )
+
+        info_arg = _inspect_validator(self.func, mode='wrap', type='field')
+        if info_arg:
+            func = cast(core_schema.WithInfoWrapValidatorFunction, self.func)
+            return core_schema.with_info_wrap_validator_function(
+                func,
+                schema=schema,
+                json_schema_input_schema=input_schema,
+            )
+        else:
+            func = cast(core_schema.NoInfoWrapValidatorFunction, self.func)
+            return core_schema.no_info_wrap_validator_function(
+                func,
+                schema=schema,
+                json_schema_input_schema=input_schema,
+            )
+
+    @classmethod
+    def _from_decorator(cls, decorator: _decorators.Decorator[_decorators.FieldValidatorDecoratorInfo]) -> Self:
+        return cls(
+            func=decorator.func,
+            json_schema_input_type=decorator.info.json_schema_input_type,
+        )
+
+
+if TYPE_CHECKING:
+
+    class _OnlyValueValidatorClsMethod(Protocol):
+        def __call__(self, cls: Any, value: Any, /) -> Any: ...
+
+    class _V2ValidatorClsMethod(Protocol):
+        def __call__(self, cls: Any, value: Any, info: core_schema.ValidationInfo[Any], /) -> Any: ...
+
+    class _OnlyValueWrapValidatorClsMethod(Protocol):
+        def __call__(self, cls: Any, value: Any, handler: core_schema.ValidatorFunctionWrapHandler, /) -> Any: ...
+
+    class _V2WrapValidatorClsMethod(Protocol):
+        def __call__(
+            self,
+            cls: Any,
+            value: Any,
+            handler: core_schema.ValidatorFunctionWrapHandler,
+            info: core_schema.ValidationInfo[Any],
+            /,
+        ) -> Any: ...
+
+    _V2Validator = Union[
+        _V2ValidatorClsMethod,
+        core_schema.WithInfoValidatorFunction,
+        _OnlyValueValidatorClsMethod,
+        core_schema.NoInfoValidatorFunction,
+    ]
+
+    _V2WrapValidator = Union[
+        _V2WrapValidatorClsMethod,
+        core_schema.WithInfoWrapValidatorFunction,
+        _OnlyValueWrapValidatorClsMethod,
+        core_schema.NoInfoWrapValidatorFunction,
+    ]
+
+    _PartialClsOrStaticMethod: TypeAlias = Union[classmethod[Any, Any, Any], staticmethod[Any, Any], partialmethod[Any]]
+
+    _V2BeforeAfterOrPlainValidatorType = TypeVar(
+        '_V2BeforeAfterOrPlainValidatorType',
+        bound=Union[_V2Validator, _PartialClsOrStaticMethod],
+    )
+    _V2WrapValidatorType = TypeVar('_V2WrapValidatorType', bound=Union[_V2WrapValidator, _PartialClsOrStaticMethod])
+
+FieldValidatorModes: TypeAlias = Literal['before', 'after', 'wrap', 'plain']
+
+
+@overload
+def field_validator(
+    field: str,
+    /,
+    *fields: str,
+    mode: Literal['wrap'],
+    check_fields: bool | None = ...,
+    json_schema_input_type: Any = ...,
+) -> Callable[[_V2WrapValidatorType], _V2WrapValidatorType]: ...
+
+
+@overload
+def field_validator(
+    field: str,
+    /,
+    *fields: str,
+    mode: Literal['before', 'plain'],
+    check_fields: bool | None = ...,
+    json_schema_input_type: Any = ...,
+) -> Callable[[_V2BeforeAfterOrPlainValidatorType], _V2BeforeAfterOrPlainValidatorType]: ...
+
+
+@overload
+def field_validator(
+    field: str,
+    /,
+    *fields: str,
+    mode: Literal['after'] = ...,
+    check_fields: bool | None = ...,
+) -> Callable[[_V2BeforeAfterOrPlainValidatorType], _V2BeforeAfterOrPlainValidatorType]: ...
+
+
+def field_validator(  # noqa: D417
+    field: str,
+    /,
+    *fields: str,
+    mode: FieldValidatorModes = 'after',
+    check_fields: bool | None = None,
+    json_schema_input_type: Any = PydanticUndefined,
+) -> Callable[[Any], Any]:
+    """!!! abstract "Usage Documentation"
+        [field validators](../concepts/validators.md#field-validators)
+
+    Decorate methods on the class indicating that they should be used to validate fields.
+
+    Example usage:
+    ```python
+    from typing import Any
+
+    from pydantic import (
+        BaseModel,
+        ValidationError,
+        field_validator,
+    )
+
+    class Model(BaseModel):
+        a: str
+
+        @field_validator('a')
+        @classmethod
+        def ensure_foobar(cls, v: Any):
+            if 'foobar' not in v:
+                raise ValueError('"foobar" not found in a')
+            return v
+
+    print(repr(Model(a='this is foobar good')))
+    #> Model(a='this is foobar good')
+
+    try:
+        Model(a='snap')
+    except ValidationError as exc_info:
+        print(exc_info)
+        '''
+        1 validation error for Model
+        a
+          Value error, "foobar" not found in a [type=value_error, input_value='snap', input_type=str]
+        '''
+    ```
+
+    For more in depth examples, see [Field Validators](../concepts/validators.md#field-validators).
+
+    Args:
+        *fields: The field names the validator should apply to.
+        mode: Specifies whether to validate the fields before or after validation.
+        check_fields: Whether to check that the fields actually exist on the model.
+        json_schema_input_type: The input type of the function. This is only used to generate
+            the appropriate JSON Schema (in validation mode) and can only specified
+            when `mode` is either `'before'`, `'plain'` or `'wrap'`.
+
+    Raises:
+        PydanticUserError:
+            - If the decorator is used without any arguments (at least one field name must be provided).
+            - If the provided field names are not strings.
+            - If `json_schema_input_type` is provided with an unsupported `mode`.
+            - If the decorator is applied to an instance method.
+    """
+    if callable(field) or isinstance(field, classmethod):
+        raise PydanticUserError(
+            'The `@field_validator` decorator cannot be used without arguments, at least one field must be provided. '
+            "For example: `@field_validator('<field_name>', ...)`.",
+            code='decorator-missing-arguments',
+        )
+
+    if mode not in ('before', 'plain', 'wrap') and json_schema_input_type is not PydanticUndefined:
+        raise PydanticUserError(
+            f"`json_schema_input_type` can't be used when mode is set to {mode!r}",
+            code='validator-input-type',
+        )
+
+    if json_schema_input_type is PydanticUndefined and mode == 'plain':
+        json_schema_input_type = Any
+
+    fields = field, *fields
+    if not all(isinstance(field, str) for field in fields):
+        raise PydanticUserError(
+            'The provided field names to the `@field_validator` decorator should be strings. '
+            "For example: `@field_validator('<field_name_1>', '<field_name_2>', ...).`",
+            code='decorator-invalid-fields',
+        )
+
+    def dec(
+        f: Callable[..., Any] | staticmethod[Any, Any] | classmethod[Any, Any, Any],
+    ) -> _decorators.PydanticDescriptorProxy[Any]:
+        if _decorators.is_instance_method_from_sig(f):
+            raise PydanticUserError(
+                'The `@field_validator` decorator cannot be applied to instance methods',
+                code='validator-instance-method',
+            )
+
+        # auto apply the @classmethod decorator
+        f = _decorators.ensure_classmethod_based_on_signature(f)
+
+        dec_info = _decorators.FieldValidatorDecoratorInfo(
+            fields=fields, mode=mode, check_fields=check_fields, json_schema_input_type=json_schema_input_type
+        )
+        return _decorators.PydanticDescriptorProxy(f, dec_info)
+
+    return dec
+
+
+_ModelType = TypeVar('_ModelType')
+_ModelTypeCo = TypeVar('_ModelTypeCo', covariant=True)
+
+
+class ModelWrapValidatorHandler(core_schema.ValidatorFunctionWrapHandler, Protocol[_ModelTypeCo]):
+    """`@model_validator` decorated function handler argument type. This is used when `mode='wrap'`."""
+
+    def __call__(  # noqa: D102
+        self,
+        value: Any,
+        outer_location: str | int | None = None,
+        /,
+    ) -> _ModelTypeCo:  # pragma: no cover
+        ...
+
+
+class ModelWrapValidatorWithoutInfo(Protocol[_ModelType]):
+    """A `@model_validator` decorated function signature.
+    This is used when `mode='wrap'` and the function does not have info argument.
+    """
+
+    def __call__(  # noqa: D102
+        self,
+        cls: type[_ModelType],
+        # this can be a dict, a model instance
+        # or anything else that gets passed to validate_python
+        # thus validators _must_ handle all cases
+        value: Any,
+        handler: ModelWrapValidatorHandler[_ModelType],
+        /,
+    ) -> _ModelType: ...
+
+
+class ModelWrapValidator(Protocol[_ModelType]):
+    """A `@model_validator` decorated function signature. This is used when `mode='wrap'`."""
+
+    def __call__(  # noqa: D102
+        self,
+        cls: type[_ModelType],
+        # this can be a dict, a model instance
+        # or anything else that gets passed to validate_python
+        # thus validators _must_ handle all cases
+        value: Any,
+        handler: ModelWrapValidatorHandler[_ModelType],
+        info: core_schema.ValidationInfo,
+        /,
+    ) -> _ModelType: ...
+
+
+class FreeModelBeforeValidatorWithoutInfo(Protocol):
+    """A `@model_validator` decorated function signature.
+    This is used when `mode='before'` and the function does not have info argument.
+    """
+
+    def __call__(  # noqa: D102
+        self,
+        # this can be a dict, a model instance
+        # or anything else that gets passed to validate_python
+        # thus validators _must_ handle all cases
+        value: Any,
+        /,
+    ) -> Any: ...
+
+
+class ModelBeforeValidatorWithoutInfo(Protocol):
+    """A `@model_validator` decorated function signature.
+    This is used when `mode='before'` and the function does not have info argument.
+    """
+
+    def __call__(  # noqa: D102
+        self,
+        cls: Any,
+        # this can be a dict, a model instance
+        # or anything else that gets passed to validate_python
+        # thus validators _must_ handle all cases
+        value: Any,
+        /,
+    ) -> Any: ...
+
+
+class FreeModelBeforeValidator(Protocol):
+    """A `@model_validator` decorated function signature. This is used when `mode='before'`."""
+
+    def __call__(  # noqa: D102
+        self,
+        # this can be a dict, a model instance
+        # or anything else that gets passed to validate_python
+        # thus validators _must_ handle all cases
+        value: Any,
+        info: core_schema.ValidationInfo[Any],
+        /,
+    ) -> Any: ...
+
+
+class ModelBeforeValidator(Protocol):
+    """A `@model_validator` decorated function signature. This is used when `mode='before'`."""
+
+    def __call__(  # noqa: D102
+        self,
+        cls: Any,
+        # this can be a dict, a model instance
+        # or anything else that gets passed to validate_python
+        # thus validators _must_ handle all cases
+        value: Any,
+        info: core_schema.ValidationInfo[Any],
+        /,
+    ) -> Any: ...
+
+
+ModelAfterValidatorWithoutInfo = Callable[[_ModelType], _ModelType]
+"""A `@model_validator` decorated function signature. This is used when `mode='after'` and the function does not
+have info argument.
+"""
+
+ModelAfterValidator = Callable[[_ModelType, core_schema.ValidationInfo[Any]], _ModelType]
+"""A `@model_validator` decorated function signature. This is used when `mode='after'`."""
+
+_AnyModelWrapValidator = Union[ModelWrapValidator[_ModelType], ModelWrapValidatorWithoutInfo[_ModelType]]
+_AnyModelBeforeValidator = Union[
+    FreeModelBeforeValidator, ModelBeforeValidator, FreeModelBeforeValidatorWithoutInfo, ModelBeforeValidatorWithoutInfo
+]
+_AnyModelAfterValidator = Union[ModelAfterValidator[_ModelType], ModelAfterValidatorWithoutInfo[_ModelType]]
+
+
+@overload
+def model_validator(
+    *,
+    mode: Literal['wrap'],
+) -> Callable[
+    [_AnyModelWrapValidator[_ModelType]], _decorators.PydanticDescriptorProxy[_decorators.ModelValidatorDecoratorInfo]
+]: ...
+
+
+@overload
+def model_validator(
+    *,
+    mode: Literal['before'],
+) -> Callable[
+    [_AnyModelBeforeValidator], _decorators.PydanticDescriptorProxy[_decorators.ModelValidatorDecoratorInfo]
+]: ...
+
+
+@overload
+def model_validator(
+    *,
+    mode: Literal['after'],
+) -> Callable[
+    [_AnyModelAfterValidator[_ModelType]], _decorators.PydanticDescriptorProxy[_decorators.ModelValidatorDecoratorInfo]
+]: ...
+
+
+def model_validator(
+    *,
+    mode: Literal['wrap', 'before', 'after'],
+) -> Any:
+    """!!! abstract "Usage Documentation"
+        [Model Validators](../concepts/validators.md#model-validators)
+
+    Decorate model methods for validation purposes.
+
+    Example usage:
+    ```python
+    from typing_extensions import Self
+
+    from pydantic import BaseModel, ValidationError, model_validator
+
+    class Square(BaseModel):
+        width: float
+        height: float
+
+        @model_validator(mode='after')
+        def verify_square(self) -> Self:
+            if self.width != self.height:
+                raise ValueError('width and height do not match')
+            return self
+
+    s = Square(width=1, height=1)
+    print(repr(s))
+    #> Square(width=1.0, height=1.0)
+
+    try:
+        Square(width=1, height=2)
+    except ValidationError as e:
+        print(e)
+        '''
+        1 validation error for Square
+          Value error, width and height do not match [type=value_error, input_value={'width': 1, 'height': 2}, input_type=dict]
+        '''
+    ```
+
+    For more in depth examples, see [Model Validators](../concepts/validators.md#model-validators).
+
+    Args:
+        mode: A required string literal that specifies the validation mode.
+            It can be one of the following: 'wrap', 'before', or 'after'.
+
+    Returns:
+        A decorator that can be used to decorate a function to be used as a model validator.
+    """
+
+    def dec(f: Any) -> _decorators.PydanticDescriptorProxy[Any]:
+        # auto apply the @classmethod decorator. NOTE: in V3, do not apply the conversion for 'after' validators:
+        f = _decorators.ensure_classmethod_based_on_signature(f)
+        if mode == 'after' and isinstance(f, classmethod):
+            warnings.warn(
+                category=PydanticDeprecatedSince212,
+                message=(
+                    "Using `@model_validator` with mode='after' on a classmethod is deprecated. Instead, use an instance method. "
+                    f'See the documentation at https://docs.pydantic.dev/{version_short()}/concepts/validators/#model-after-validator.'
+                ),
+                stacklevel=2,
+            )
+
+        dec_info = _decorators.ModelValidatorDecoratorInfo(mode=mode)
+        return _decorators.PydanticDescriptorProxy(f, dec_info)
+
+    return dec
+
+
+AnyType = TypeVar('AnyType')
+
+
+if TYPE_CHECKING:
+    # If we add configurable attributes to IsInstance, we'd probably need to stop hiding it from type checkers like this
+    InstanceOf = Annotated[AnyType, ...]  # `IsInstance[Sequence]` will be recognized by type checkers as `Sequence`
+
+else:
+
+    @dataclasses.dataclass(**_internal_dataclass.slots_true)
+    class InstanceOf:
+        '''Generic type for annotating a type that is an instance of a given class.
+
+        Example:
+            ```python
+            from pydantic import BaseModel, InstanceOf
+
+            class Foo:
+                ...
+
+            class Bar(BaseModel):
+                foo: InstanceOf[Foo]
+
+            Bar(foo=Foo())
+            try:
+                Bar(foo=42)
+            except ValidationError as e:
+                print(e)
+                """
+                [
+                │   {
+                │   │   'type': 'is_instance_of',
+                │   │   'loc': ('foo',),
+                │   │   'msg': 'Input should be an instance of Foo',
+                │   │   'input': 42,
+                │   │   'ctx': {'class': 'Foo'},
+                │   │   'url': 'https://errors.pydantic.dev/0.38.0/v/is_instance_of'
+                │   }
+                ]
+                """
+            ```
+        '''
+
+        @classmethod
+        def __class_getitem__(cls, item: AnyType) -> AnyType:
+            return Annotated[item, cls()]
+
+        @classmethod
+        def __get_pydantic_core_schema__(cls, source: Any, handler: GetCoreSchemaHandler) -> core_schema.CoreSchema:
+            from pydantic._internal._generate_schema import GENERATE_SCHEMA_ERRORS
+
+            # use the generic _origin_ as the second argument to isinstance when appropriate
+            instance_of_schema = core_schema.is_instance_schema(_generics.get_origin(source) or source)
+
+            try:
+                # Try to generate the "standard" schema, which will be used when loading from JSON
+                original_schema = handler(source)
+            except GENERATE_SCHEMA_ERRORS:
+                # If that fails, just produce a schema that can validate from python
+                return instance_of_schema
+            else:
+                # Use the "original" approach to serialization
+                instance_of_schema['serialization'] = core_schema.wrap_serializer_function_ser_schema(
+                    function=lambda v, h: h(v), schema=original_schema
+                )
+                return core_schema.json_or_python_schema(python_schema=instance_of_schema, json_schema=original_schema)
+
+        __hash__ = object.__hash__
+
+
+if TYPE_CHECKING:
+    SkipValidation = Annotated[AnyType, ...]  # SkipValidation[list[str]] will be treated by type checkers as list[str]
+else:
+
+    @dataclasses.dataclass(**_internal_dataclass.slots_true)
+    class SkipValidation:
+        """If this is applied as an annotation (e.g., via `x: Annotated[int, SkipValidation]`), validation will be
+            skipped. You can also use `SkipValidation[int]` as a shorthand for `Annotated[int, SkipValidation]`.
+
+        This can be useful if you want to use a type annotation for documentation/IDE/type-checking purposes,
+        and know that it is safe to skip validation for one or more of the fields.
+
+        Because this converts the validation schema to `any_schema`, subsequent annotation-applied transformations
+        may not have the expected effects. Therefore, when used, this annotation should generally be the final
+        annotation applied to a type.
+        """
+
+        def __class_getitem__(cls, item: Any) -> Any:
+            return Annotated[item, SkipValidation()]
+
+        @classmethod
+        def __get_pydantic_core_schema__(cls, source: Any, handler: GetCoreSchemaHandler) -> core_schema.CoreSchema:
+            with warnings.catch_warnings():
+                warnings.simplefilter('ignore', ArbitraryTypeWarning)
+                original_schema = handler(source)
+            metadata = {'pydantic_js_annotation_functions': [lambda _c, h: h(original_schema)]}
+            return core_schema.any_schema(
+                metadata=metadata,
+                serialization=core_schema.wrap_serializer_function_ser_schema(
+                    function=lambda v, h: h(v), schema=original_schema
+                ),
+            )
+
+        __hash__ = object.__hash__
+
+
+_FromTypeT = TypeVar('_FromTypeT')
+
+
+class ValidateAs:
+    """A helper class to validate a custom type from a type that is natively supported by Pydantic.
+
+    Args:
+        from_type: The type natively supported by Pydantic to use to perform validation.
+        instantiation_hook: A callable taking the validated type as an argument, and returning
+            the populated custom type.
+
+    Example:
+        ```python {lint="skip"}
+        from typing import Annotated
+
+        from pydantic import BaseModel, TypeAdapter, ValidateAs
+
+        class MyCls:
+            def __init__(self, a: int) -> None:
+                self.a = a
+
+            def __repr__(self) -> str:
+                return f"MyCls(a={self.a})"
+
+        class Model(BaseModel):
+            a: int
+
+
+        ta = TypeAdapter(
+            Annotated[MyCls, ValidateAs(Model, lambda v: MyCls(a=v.a))]
+        )
+
+        print(ta.validate_python({'a': 1}))
+        #> MyCls(a=1)
+        ```
+    """
+
+    # TODO: make use of PEP 747
+    def __init__(self, from_type: type[_FromTypeT], /, instantiation_hook: Callable[[_FromTypeT], Any]) -> None:
+        self.from_type = from_type
+        self.instantiation_hook = instantiation_hook
+
+    def __get_pydantic_core_schema__(self, source: Any, handler: GetCoreSchemaHandler) -> core_schema.CoreSchema:
+        schema = handler(self.from_type)
+        return core_schema.no_info_after_validator_function(
+            self.instantiation_hook,
+            schema=schema,
+        )

.mcp-venv/lib/python3.13/site-packages/pydantic/generics.py

@@ -0,0 +1,5 @@
+"""The `generics` module is a backport module from V1."""
+
+from ._migration import getattr_migration
+
+__getattr__ = getattr_migration(__name__)

.mcp-venv/lib/python3.13/site-packages/pydantic/json.py

@@ -0,0 +1,5 @@
+"""The `json` module is a backport module from V1."""
+
+from ._migration import getattr_migration
+
+__getattr__ = getattr_migration(__name__)