llm_list_models

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

Annotations are absent, so the description carries the full burden. It discloses the HTTP method (GET) but does not explicitly state this is a read-only operation with no side effects, nor does it explain authentication requirements, rate limits, or error behavior.

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 sentence with zero filler and front-loads the core purpose. It is appropriately sized for the tool's simplicity, though it omits usage details.

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?

With no output schema, the description should at least hint at the return format (e.g., list of model IDs) or parameter selection rules. It provides neither, leaving the agent without enough information to call the tool correctly in all cases.

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?

Schema description coverage is 100%, so the schema already documents all three parameters. The description adds no additional meaning about how parameters interact (e.g., whether endpoint_url overrides provider) or when each should be supplied.

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 action ('List available models') and the specific resource ('OpenAI-compatible /v1 endpoint'), and it names the exact HTTP call (GET /v1/models). This distinguishes it from generation, query, and connection-testing siblings.

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?

No guidance on when to use this tool versus alternatives like llm_test_connection, nor how to choose among the three optional parameters (provider, endpoint_url, api_key). The agent is left to infer usage.

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