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ask_about_model

Answer questions about model training data, intended use, limitations, or biases by retrieving relevant excerpts from the model card README.

Instructions

Answer a free-text question using the written model card README.

Use this for things only prose documents: training data, intended use, limitations, known biases, evaluation setup, or usage instructions. Returns the most relevant excerpts with their section headings as citations — base the answer only on these excerpts.

For structured facts (license, size, downloads, benchmark numbers), use get_model_card instead; it is cheaper and more reliable for those.

Args: model_id: Hugging Face model ID, e.g. "openai/whisper-large-v3". question: The question to answer, e.g. "what data was it trained on?"

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
model_idYes
questionYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior5/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Discloses that it returns relevant excerpts with section headings as citations and that answers should be based only on those excerpts. No annotations to contradict.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Concise, well-structured: one-sentence purpose, then usage guidance, then parameter details. No unnecessary words.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Complete for a QA tool: explains purpose, when to use, what returns (excerpts with citations), and parameter details. Output schema exists but is not needed to understand behavior.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters5/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

With 0% schema coverage, the description adds full meaning: explains both parameters, provides format and examples (e.g., 'openai/whisper-large-v3', 'what data was it trained on?').

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the verb 'answer' and resource 'model card README', and distinguishes from sibling tools by noting that get_model_card is for structured facts.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Explicitly says when to use (free-text questions about prose documents) and when not to (structured facts, use get_model_card), providing clear alternatives.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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