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

With no annotations provided, the description carries the full burden. It discloses the scope ('all supported') and the data returned (pricing, context windows). The verb 'List' implies a read-only operation, and no hidden side effects or external behaviors are suggested, providing adequate transparency for a simple listing tool.

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 main action ('List') and the object ('all supported AI models'), followed by relevant details. There is no filler or redundant information.

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 tool with no parameters and no output schema, the description fully specifies its purpose and what it returns. It mentions the key attributes (pricing and context windows) and is sufficiently complete for its simple scope.

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 the schema provides no additional constraints. The description adds no parameter info because there are none. According to the rubric, a 0-parameter tool gets a baseline score of 4, which is appropriate here.

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 function with a specific verb ('List'), a clear resource ('all supported AI models'), and includes the specific attributes returned (pricing and context windows). This distinguishes it from sibling tools like count_tokens and estimate_cost, which perform different operations.

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 the tool is used when you need an overview of available models with their pricing and context windows. However, it lacks explicit guidance on when to prefer this over get_model_pricing or any exclusions, so the usage context is only implied, not fully defined.

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