get_model_info

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

No annotations are provided, so the description must convey behavioral traits on its own. It does disclose the return content in detail (pricing breakdown, capabilities, speed metrics, etc.), but it does not explicitly state that the operation is read-only or non-destructive, nor does it mention error handling, authentication requirements, or whether the data is real-time or cached. This leaves some room for ambiguity.

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?

The description is two sentences, front-loaded with the main action, and every sentence adds value. The first sentence tells exactly what the tool does, and the second efficiently lists the key contents of the model card. No fluff or redundancy.

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?

Given that there is no output schema and no annotations, the description carries the burden of explaining what the tool returns. It does this well by listing many expected fields (description, pricing, capabilities, quality tier, context length, speed metrics, benchmarks, tags, license). However, it is not fully complete because it ends with 'and more' without specifying what else, and it omits error behavior and input constraints beyond the schema. Still, it is adequately complete for a read-only single-parameter tool.

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?

The schema already provides 100% coverage for the single parameter (model_id) with an example format, so the description does not need to add much. The description adds no additional meaning about the parameter itself beyond saying 'any AI model,' which is already implied by the schema. The baseline of 3 applies.

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 uses a specific verb ('Get') and a clear resource ('a comprehensive model card') and states the scope ('for any AI model'). It also enumerates the content (pricing, capabilities, benchmarks, etc.), which distinguishes it from sibling tools like list_models (which lists models) and compare_models (which compares).

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?

The description clearly implies the tool is used when you need detailed information about a single model, but it does not explicitly state when to use it versus alternatives or mention any exclusions (e.g., 'Use compare_models for side-by-side comparisons'). No direct guidance is given, so usage is implied rather than explicit.

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