list_models

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

With no annotations, the description carries the full burden. 'List' clearly implies a read-only operation, and it discloses the specific information returned (capabilities, pricing, web search costs). It doesn't hide side effects because none are expected for a listing tool, and the description gives sufficient behavioral context.

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 sentences, front-loaded with the action and content, then a clear usage directive. Every word contributes value with zero redundancy. This is an exemplar of concise, structured writing.

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 low-complexity tool with optional filters and no output schema, the description is complete. It names the output fields (capabilities, pricing, web search costs) and the usage context. It doesn't elaborate on filtering, but the schema covers that, and the description doesn't need to explain return structure beyond what it lists.

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 description mentions capabilities and pricing which aligns with the capability filter, but it doesn't explicitly explain the provider or capability parameters. Since the schema has 100% coverage with descriptive parameter descriptions, the baseline of 3 is appropriate; the description adds no additional parameter meaning.

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') and resource ('available AI models'), and specifies the content (capabilities, pricing, web search costs). It distinguishes itself from sibling tools like ask/search/research by being about model selection rather than executing tasks.

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 explicit guidance: 'Use this to choose the right model for your task.' This establishes when to use the tool, though it doesn't explicitly mention alternatives or when not to use it. The context is clear enough for an agent to know this is the model-discovery step.

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