gptproto_model_describeRetrieve a model's live methods, native parameters, enums, response type, and async polling contract to make documented API requests.
Return a model's live methods, paths, native parameters, enums, response type, and asynchronous polling contract.
| Name | Required | Description | Default |
|---|---|---|---|
| model | Yes | Exact provider/model ID |
Changes observed during successful MCP inspections.
v0.1.0Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint, idempotentHint, and destructiveHint=false, so the safety profile is covered. The description adds the live/async-polling aspect, but no extra behavioral caveats such as rate limits, caching, or failure modes. With annotations doing most of the work, 3 is appropriate.
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?
A single focused sentence with no filler. The main action and the full scope of the return value are given front-loaded, and every listed item adds useful detail.
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?
Despite lacking an output schema, the description enumerates the key return categories (methods, paths, native parameters, enums, response type, async polling contract), which is enough for an agent to understand what the tool provides. It could add error or usage context, but for a one-parameter read-only describe tool this is almost complete.
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% and the single 'model' parameter is already described as 'Exact provider/model ID.' The tool description does not add parameter-level semantics beyond what the schema provides, so the high-coverage baseline of 3 applies.
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 ('Return') with a precise resource — a model's live methods, paths, native parameters, enums, response type, and polling contract. This clearly differentiates it from siblings like gptproto_models_list (listing) and gptproto_request (making calls).
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 states what the tool returns but gives no guidance on when to use it versus siblings such as gptproto_models_list or gptproto_request. There is no explicit context, prerequisite, or exclusion to help an agent choose among the sibling tools.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.