"""MCP retrieval tools used by the agent loop (summaries)."""

from __future__ import annotations

import os

from dotenv import load_dotenv

load_dotenv(override=True)

from mcp.server.fastmcp import FastMCP

from mcp_tools.local_rag_tools import build_paper_context_tool, get_paper_summaries_tool
from mcp_tools.metadata_tools import search_metadata_tool, select_papers_from_search_tool

mcp = FastMCP("waterscope-retrieval")


@mcp.tool()
def search_metadata(
    query: str,
    top_k: int = 10,
    year_from: int | None = None,
    year_to: int | None = None,
) -> str:
    """Search title, abstract, and keywords with optional publication-year bounds."""
    return search_metadata_tool(
        query,
        top_k=top_k,
        year_from=year_from,
        year_to=year_to,
    )


@mcp.tool()
def select_papers_from_search(
    query: str,
    ranks: list[int],
    top_k: int = 10,
    year_from: int | None = None,
    year_to: int | None = None,
) -> str:
    """Return document_ids for 1-based search result ranks."""
    return select_papers_from_search_tool(
        query,
        ranks,
        top_k=top_k,
        year_from=year_from,
        year_to=year_to,
    )


@mcp.tool()
def get_paper_summaries(document_ids: list[str]) -> str:
    """Load pre-written summaries for chat context."""
    return get_paper_summaries_tool(document_ids)


@mcp.tool()
def build_paper_context(document_ids: list[str], question: str = "") -> str:
    """Assemble truncated multi-paper context for WaterScope /agent_run."""
    return build_paper_context_tool(document_ids, question=question)


if __name__ == "__main__":
    transport = os.getenv("MCP_TRANSPORT", "stdio")
    if transport == "streamable-http":
        mcp.run(
            transport="streamable-http",
            host=os.getenv("MCP_HOST", "127.0.0.1"),
            port=int(os.getenv("MCP_PORT", "8001")),
        )
    else:
        mcp.run(transport="stdio")