suggest_models

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

No annotations are provided, so the description carries the full burden. It implies a read-only lookup but says nothing about whether the model must exist, how many suggestions are returned, ranking/ordering criteria, or any rate/limit behavior.

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?

A single front-loaded sentence with zero filler; the purpose and the input relationship are conveyed immediately.

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 should describe the shape of returned suggestions (list of ids? objects? ordering?), but it does not. For a discovery tool with an undocumented parameter and no annotations, this leaves the agent guessing about results and edge cases.

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 description coverage is 0% for the single model_id parameter, so the description must compensate. It only implies the parameter is a model identifier and gives no format, namespace, or example (e.g., whether it is a provider-qualified or internal id).

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?

States a specific verb ('suggest') and resource ('sibling models from the same family as the given model id'), which is a clear, non-tautological purpose. It does not, however, contrast itself with the sibling list_models, which sounds like it may return related data.

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?

There is no guidance on when to call this versus list_models or list_availability, no statement of prerequisites, and no indication of when the tool is inappropriate (e.g., for a model id that has no siblings).

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