list_models

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It describes the return data (model IDs, types, etc.) and implies a read-only operation, but doesn't cover aspects like rate limits, pagination, or error handling. This is adequate but has clear gaps for a tool with zero annotation coverage.

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 appropriately sized and front-loaded, with two concise sentences that each serve a clear purpose: the first explains what the tool does and returns, and the second provides usage guidance. There's no wasted text.

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 the tool's complexity (simple list operation with 0 parameters) and the lack of annotations and output schema, the description is minimally complete. It covers the purpose and usage but lacks details on behavioral traits like performance or limitations. This is adequate but leaves room for improvement.

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 tool has 0 parameters, and the schema description coverage is 100% (though empty). The description doesn't need to add parameter information, so it meets the baseline expectation. No additional value is required, but it doesn't exceed expectations either.

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 states the tool's purpose with a specific verb ('List') and resource ('all registered models with their metadata'), making it easy to understand what the tool does. However, it doesn't explicitly differentiate this tool from its sibling tools (like 'list_drift_alerts' or 'list_skills'), which would be needed for a perfect score.

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 provides clear context on when to use this tool ('Use this to discover what models are available before asking questions'), which helps guide the agent. It doesn't explicitly state when not to use it or name specific alternatives among siblings, so it falls short of a perfect score.

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