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

With no annotations, the description carries the burden. It discloses a key behavioral trait: missing benchmark scores are marked as untested rather than represented as low values, preventing misinterpretation. It also says it aligns fields. It does not mention read-only semantics or error handling, but the comparison verb and output description make the non-mutating nature clear.

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

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

Two sentences: the first fronts the purpose, the second gives usage guidance and a crucial semantic clarification. No filler words; every clause contributes.

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

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

There is no output schema, so the description must convey what the tool returns. It lists price, context, benchmark scores per subject, and monthly cost, and clarifies missing-score handling. It could be more specific about the return format (e.g., table vs object), but for the stated purpose it is adequately complete.

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

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

Schema coverage is 100%, so baseline is 3. The description adds meaning by mapping 'two or more models' to the ids parameter and 'at a given volume' to monthlyMillionTokens, explaining the purpose of the cost-estimation parameter without repeating schema details.

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

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

The description states a specific verb ('put side by side'), resource ('models'), and enumerates the comparison dimensions (price, context, benchmark scores, monthly cost). It explicitly contrasts with get_model by saying 'Use this instead of calling get_model repeatedly', which distinguishes it from siblings.

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

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

It gives explicit usage guidance: when comparing multiple models, use this instead of calling get_model repeatedly. It also explains the benefit—aligned fields and marking untested subjects—so the agent knows when the tool is the right choice versus the single-model getter.

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