list_models

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

No annotations provided, but the description indicates a simple read-only operation with no side effects. It doesn't delve into authentication or data freshness, but for a list tool with no parameters, the behavioral disclosure is adequate.

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 short, direct sentences with no unnecessary words. Every piece of information is valuable and front-loaded.

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 (no parameters, no output schema), the description fully covers what it does and when to use it. No missing information for an agent to correctly invoke it.

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?

No parameters exist in the schema, so the description doesn't need to add param-level details. It exceeds the baseline by noting the scope ('across all providers'), which adds context beyond the empty schema.

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 lists all available models across all providers, with a specific verb and resource. It also suggests a use case ('Run this first to see what you can query'), distinguishing it from sibling tools that query or compare specific models.

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?

Implicitly recommends running this tool first before using other model-related tools (like ask_model, compare_models). While it doesn't explicitly state when not to use it or mention alternatives, the context is clear enough for an agent to understand its role as a discovery tool.

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