post_chat_completionsCreates a model response for the given chat conversation Billing per call: Credits: metered (~0 avg).
| Name | Required | Description | Default |
|---|---|---|---|
| body | No | JSON request body. Example: {"model":"GPT-5","stream":false,"messages":[{"role":"user","content":"There are ten birds in a tree. A hunter shoots one. How many are left in the tree?"}]} |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Since no annotations are provided, the description carries the full behavioral disclosure burden. It does disclose billing/cost behavior and that the tool generates a model response, but it does not mention side effects, auth requirements, response format, streaming, or rate limits. Some transparency is present, but not comprehensive.
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 short and front-loaded with the core action. The billing note is useful but awkwardly appended without a period, slightly reducing readability. No words are wasted, though formatting could be improved.
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?
The tool has a single flexible body parameter and a helpful example, but there is no output schema and the description does not explain return values, error behavior, or usage constraints. Billing is covered, but broader operational context is thin.
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?
There is only one parameter, 'body', and the schema provides a description plus a concrete example, giving 100% schema_description_coverage. The description text adds no extra parameter-level detail beyond billing, so the baseline score of 3 is appropriate.
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 uses a specific verb and resource: 'Creates a model response for the given chat conversation.' This clearly identifies the tool's purpose and is not a tautology. Even without sibling tools to differentiate from, the action and target are unambiguous.
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 intended use is implied: this tool is for generating chat completion responses. However, there is no explicit guidance on when to use it versus alternatives, nor any exclusions or prerequisites. With no sibling tools listed, this is acceptable but leaves usage boundaries unstated.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.
With only one tool, there is no possibility of confusion or overlap. The single tool clearly covers its intended purpose without competing alternatives.
The tool name 'post_chat_completions' follows a clean verb_noun snake_case convention, matching the style expected for HTTP method and resource. Even in isolation, it is predictable and self-descriptive.
A single tool is on the low end of the spectrum, making the server feel minimal but not unreasonable for a narrowly scoped chat completion endpoint. It could benefit from a couple of supporting tools (e.g., model listing or health check) to feel fully featured.
The server exposes only one operation with no ability to list models, retrieve request logs, or check service status. While the core generation is present, the absence of any auxiliary endpoints leaves notable gaps for real-world usage.