List models

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

No annotations, and description lacks details on behavior such as whether results are cached, sorted, or limited. Only lists returned fields.

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?

Single sentence with all key information, no 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?

Covers what is returned and that it includes all public models. No output schema, but description suffices for a zero-param listing 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?

No parameters; description adds value by enumerating returned fields (IQ, dimensions, emotional reasoning, etc.), though baseline is 4 for 0-param tools.

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 clearly specifies 'list public AI models' with concrete attributes (IQ, scores, reasoning, rank, cost). Distinguishes from siblings like compare_models and get_model.

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?

Implies use for obtaining a full list of models, but no explicit when-to-use/not-use or alternatives like compare_models for comparisons.

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