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

No annotations exist, so the description carries the burden. It discloses the HTTP method (GET), the live nature of the data, and the exact behavior of the optional id parameter: it returns one public row identical to that id in the list. This is adequate for a read-only listing operation.

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 compact sentences with no filler. The core purpose is front-loaded, and the optional-id behavior plus the hardcoding warning each earn their place.

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?

For a simple one-parameter read-only tool with no output schema, the description covers purpose, usage timing, optional-id behavior, and a key pitfall. It could explicitly state the response is a model list, but 'GET /v1/models' implies this sufficiently.

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 the schema already documents the id parameter and that omitting it returns the full list. The description adds a useful consistency guarantee ('same object as that id in the list') but does not significantly extend the parameter semantics.

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 names the specific verb and resource ('GET /v1/models') and a concrete purpose: resolving a live model id before inference. This clearly distinguishes it from inference, generation, key, and video sibling tools.

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 explicitly tells the agent to use this before inference and warns against hardcoding model ids. It does not name alternative tools or state when not to use it, but the guidance is clear enough for a simple lookup tool.

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