List Models

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

There are no annotations provided, so the description carries the full burden. It clarifies that the operation lists models available to the caller's key, but does not disclose additional behavioral details such as network call behavior, rate limits, or return format. The word 'List' strongly implies a non-destructive read, so it's adequate but not rich.

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, front-loaded sentence with no filler. Every word contributes the purpose and scope.

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 simplicity—zero parameters, no output schema—the description adequately specifies what the tool does and the scope ('for your key'). It could mention what the list contains (e.g., model IDs or names), but the operation is straightforward enough that the description is complete for this level of complexity.

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 zero parameters, so there is no parameter information required. The baseline for zero-parameter tools is 4, and the description doesn't need to add parameter details.

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 the specific verb 'List' to name the action and identifies the resource as 'available NaN API models' scoped to 'your key.' This clearly distinguishes it from sibling tools like generate_image or text_to_speech, which perform different actions.

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 implies usage when you need to see which models your key can access, but it does not explicitly state when to use this tool versus alternatives or mention any contextual preconditions. For a simple list tool, the context is reasonably clear, but it lacks explicit guidance.

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