GPT-5: GPT‑5 is OpenAI’s most advanced and unified AI model, combining fast, real-time.
Scored across 1 tool
With only one tool, there is no possibility of overlap or confusion. The purpose of post_chat_completions is clearly defined and distinct by default.
The single tool name follows a clear verb_noun snake_case pattern (post_chat_completions), which is internally consistent and readable. There are no other names to conflict.
A single tool feels thin for a server named 'gpt-5', which suggests a broader API surface. However, the tool is not trivial and could serve a narrowly scoped purpose, so it sits at the borderline.
The tool covers the core chat completion generation, but there are no supporting operations (e.g., listing models, retrieving usage, or managing conversations). Agents may hit dead ends if they need any lifecycle or auxiliary capability beyond generating a response.
Creates a model response for the given chat conversation Billing per call: Credits: metered.
| 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?
No annotations are provided, so the description carries the full burden. It discloses that the tool creates a response and that billing is metered, but it does not mention streaming behavior, latency, rate limits, or what happens with invalid requests. The example in the schema hints at stream:false, but the description itself is thin.
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 purpose. The billing note is a minor addition that earns its place. It is not verbose, though it could be more informative without becoming long.
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 tool with no annotations, no output schema, and a single nested body parameter, the description is too sparse. It does not explain the response format, error behavior, or how to use the body parameter beyond the schema example. An agent would need to infer too much.
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 the single 'body' parameter. The description adds no parameter-level meaning beyond the schema, but the schema's example is rich and includes a concrete request body. Baseline 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 states a specific verb and resource: 'Creates a model response for the given chat conversation.' This clearly identifies the tool's function. However, it does not differentiate from siblings because no siblings are listed, and the title 'Chat Completions' is generic.
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 provides no guidance on when to use this tool versus alternatives, and no sibling tools are listed. The billing note ('Credits: metered') is the only contextual hint, but it does not explain usage scenarios or exclusions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Recent tool additions, removals, and schema changes observed during successful MCP inspections.
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