qualifire/tool-selection-quality-benchmark

Tool Selection Quality Benchmark

A benchmark for evaluating whether an LLM correctly judges the quality of a tool call / function call made by another model - i.e. given a user request, the tools available, and the model's resulting function call (or direct reply), did the model pick the right tool and fill it in correctly?

Each row is one turn to be judged: a message history ending in either a function call or a direct assistant response, paired with the set of tools that were available, and a ground-truth verdict on whether that call was correct and, if not, what specifically went wrong.

Dataset Structure

Column	Type	Description
tools_list	string	Stringified list of the tool/function definitions (name, description, JSON-schema parameters) available to the model for this turn.
messages_history	string	The conversation up to and including the model's response: the user's request and either a function_call (tool name + arguments) or a direct assistant reply.
label	string	Ground-truth verdict - one of VALID_CALL, TOOL_ERROR, PARAM_NAME_ERROR, PARAM_VALUE_ERROR (see taxonomy below).
binary_label	string	Collapsed verdict: CORRECT (VALID_CALL) or INCORRECT (any of the three error labels).
failure_reason	string	Short free-text reason for the failure (e.g. "incorrect tool selected", "hallucinated parameter name"), or "None" when label is VALID_CALL.

Example row

A user asks Cribl to fetch a pipeline's config; the model correctly calls cribl_getPipelineConfig with the right pipelineId/groupName arguments → label: VALID_CALL, binary_label: CORRECT, failure_reason: None.

Label Taxonomy

Verdicts follow a strict, ordered check - tool selection first, then parameter structure, then parameter values - and the first failing check determines the label:

1. Tool Selection - does the chosen tool exist and match the user's intent? If not → TOOL_ERROR (e.g. wrong tool picked, or a tool call made when none was needed, or vice versa).
2. Parameter Structure - are the required parameter names present, and are there no extraneous/hallucinated ones? If not → PARAM_NAME_ERROR.
3. Parameter Values - are the parameter values correctly typed, formatted, and factually consistent with the request? If not → PARAM_VALUE_ERROR.

If none of these trigger - including the case where the user's request needed no tool call at all and the model correctly replied directly - the turn is VALID_CALL.

Dataset Statistics

• 5,000 rows total.

label distribution:

Label	Count
VALID_CALL	2,000
TOOL_ERROR	1,000
PARAM_NAME_ERROR	1,000
PARAM_VALUE_ERROR	1,000

binary_label distribution:

Label	Count
CORRECT	2,000
INCORRECT	3,000

Usage

from datasets import load_dataset

ds = load_dataset("qualifire/tool-selection-quality-benchmark")["test"]
print(ds[0])

A common evaluation pattern is to score a judge model on each row (given tools_list + messages_history) and compare its predicted label - or the collapsed binary_label - against ground truth, both overall and broken down per label.

Considerations for Use

• This benchmark is intended for evaluating tool-use/function-calling judge models - i.e. models whose job is to critique another model's tool call, not models that make tool calls themselves.
• Labels reflect a specific, ordered evaluation rubric (tool → parameter names → parameter values); treat them as a strong baseline rather than an unimpeachable gold standard.