| --- |
| license: cc-by-nc-4.0 |
| language: |
| - en |
| tags: |
| - tool-use |
| - function-calling |
| - agent-evaluation |
| - llm-evaluation |
| pretty_name: Tool Selection Quality Benchmark |
| dataset_info: |
| features: |
| - name: tools_list |
| dtype: string |
| - name: messages_history |
| dtype: string |
| - name: binary_label |
| dtype: string |
| - name: label |
| dtype: string |
| - name: failure_reason |
| dtype: string |
| splits: |
| - name: test |
| num_bytes: 39125340 |
| num_examples: 5000 |
| download_size: 9922540 |
| dataset_size: 39125340 |
| configs: |
| - config_name: default |
| data_files: |
| - split: test |
| path: data/test-* |
| --- |
| |
| # 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 |
|
|
| ```python |
| 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. |
|
|
