openai_models_list

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It states the tool lists models via a GET request, implying a read-only operation, but doesn't cover aspects like authentication needs, rate limits, error handling, or the format of returned data. This leaves significant gaps in understanding how the tool behaves in practice.

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, efficient sentence that front-loads the core action ('List models') and includes essential technical details (the backend and endpoint). There is no wasted verbiage, making it highly concise and well-structured for quick understanding.

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 lack of annotations and output schema, the description is incomplete for a tool that interacts with an external API. It doesn't explain what the return value looks like (e.g., list of model objects), potential errors, or authentication requirements, which are critical for an AI agent to use this tool effectively in real-world scenarios.

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 input schema has 0 parameters with 100% coverage, so no parameter documentation is needed. The description appropriately doesn't discuss parameters, focusing instead on the tool's purpose and endpoint. This aligns with the baseline expectation for tools with no parameters.

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 models') and the resource ('from OpenAI-compatible backend'), with the specific API endpoint ('GET /v1/models') providing technical context. It distinguishes from siblings like 'openai_chat_completions' and 'openai_embeddings_create' by focusing on model listing rather than chat or embedding operations, though it doesn't explicitly name these alternatives.

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 for retrieving available models from an OpenAI-compatible API, but it doesn't provide explicit guidance on when to use this tool versus alternatives (e.g., for checking model availability before making chat completions). No exclusions or prerequisites are mentioned, leaving usage context somewhat inferred rather than clearly defined.

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