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

With no annotations provided, the description carries the full disclosure burden. It adds genuinely useful behavioral context beyond the schema: the call is 'buffered' (implying it waits for the full response rather than streaming) and it returns cost trace headers, which hints at billing behavior. However, it does not disclose auth requirements, idempotency, potential latency implications of buffering, 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?

Three tight fragments — endpoint/mode, prerequisite, and return value — with zero filler. The most identifying information (endpoint and buffered mode) is front-loaded, and each sentence contributes distinct operational knowledge an agent needs.

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 relatively simple 4-parameter tool, the description covers the essentials: endpoint, buffering mode, model prerequisite, and return shape. But with no output schema and no annotations, it leaves gaps: no detail on what 'cost trace headers' actually affect, no guidance on which sibling generation tool to select for which kind of generation, and no clarification of the message format beyond the schema's terse 'OpenAI-style messages'.

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 50% — model and messages are described, while max_tokens and temperature are bare properties with no description. The description adds real value for the model parameter by specifying it must be a 'live model id from get_models', which is not in the schema. Messages are covered by the schema's 'OpenAI-style messages'. The two undocumented parameters remain unexplained in both schema and description, so the description only partially compensates.

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 states a specific verb and resource ('POST /v1/chat/completions') and clarifies the operation mode ('buffered'). It also names the key dependency ('Pass a live model id from get_models') and the return shape ('completion plus cost trace headers'). It distinguishes from siblings like generate_image and generate_video by naming the chat endpoint, though it does not explicitly position itself against them.

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 instruction to pass a live model id from get_models is a clear prerequisite that informs the agent it must first call a sibling tool. However, there is no explicit guidance on when to choose chat_completions over the other generation tools (generate_image, generate_video, video_complete) or when not to use it. Usage context is implied by the endpoint name rather than stated.

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