openai_chat

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

No annotations are provided, so the description carries the full behavioral burden, yet it says nothing about cost, latency, rate limits, token/context limits, auth requirements, or whether calls are billed or stateful. It only names supported model families, which is closer to schema duplication than behavioral disclosure.

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 sentences, front-loaded with the core action; nothing is padded. The second sentence is mildly redundant with the enum in the schema but is not wasteful enough to penalize further.

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 simple two-parameter call with fully covered schema and no output schema, the description is minimally adequate. However, with zero annotations it should at least flag cost/rate-limit behavior or model-selection tradeoffs, which it omits.

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 both parameters (model enum and messages array with role/content) are already documented, establishing a baseline of 3. The description's mention of 'GPT-4o, o1, and GPT-5 series' merely restates a subset of the enum and adds no format, default, or selection semantics.

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?

States a clear verb (send) and resource (messages to OpenAI) with the model-selection dimension named. It distinguishes itself from any generic LLM-call sibling by naming the vendor, but there are no siblings to differentiate against and it doesn't clarify what the tool returns.

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, when not to, or how to choose among the listed model families (e.g. cheap vs reasoning vs codex variants). The reader must infer usage entirely.

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