List Models

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

The annotations already provide readOnlyHint=true and openWorldHint=true, establishing the safety profile. The description adds no additional behavioral context, such as whether a cached list is used by default or any potential side effects. It does not contradict the annotations but also does not enrich them.

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 a single, concise sentence that front-loads the action and resource. Every word earns its place, with zero redundancy or filler.

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 read-only listing tool, the description is adequate. The annotations and schema cover safety and parameter semantics, leaving no obvious gaps. However, without an output schema, the description does not explicitly state what a 'list' returns, though it is implied.

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 input schema documents both parameters with descriptions and covers 100% of parameters. The description does not add any semantic detail beyond the schema, so the baseline score of 3 is appropriate.

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 ('List') and resource ('available models for LLM providers'), clearly indicating the tool's function. It is easily distinguished from sibling tools, which are all duck-related.

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 implicitly indicates when to use the tool (whenever a list of available models is needed), but it does not explicitly mention any alternatives or exclusions. Since sibling tools are unrelated, this clarity is sufficient, though it lacks explicit guidance on provider selection or fetch_latest behavior.

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