获取可用 TTS 音色

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

Beyond the readOnlyHint/openWorldHint annotations, the description discloses the concrete endpoint and query shape (GET /v1/tts/voices?model=...), plus a full fallback policy: on upstream failure, timeout, or invalid response it silently returns built-in OpenAI voices and flags source=fallback. That fallback semantics is exactly the kind of non-obvious behavior an agent cannot get from the annotations.

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 tightly packed sentences: the purpose first, then the call strategy and fallback contract. Every clause carries information and nothing is repeated from the title or schema.

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?

With no output schema, the description carries the return-value burden and does state that results are structured and include a source marker. It does not describe the shape of the voice entries themselves (identifiers, locale, gender), which the sibling text_to_speech would need, leaving a small gap.

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?

The tool takes zero parameters, so the baseline is 4. The description still adds value by explaining that the model argument sent to the endpoint is derived from the current OAI_TTS_MODEL configuration rather than supplied by the caller, which clarifies why the schema is empty.

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 names a specific verb and resource ('获取当前 OAI_TTS_MODEL 的可用音色') and scopes it to the configured model, so an agent immediately knows this enumerates TTS voices. It never names the sibling text_to_speech, so the distinction is inferred from the resource rather than stated, keeping it just short of a 5.

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?

Usage is only implied: an agent infers this is the tool to call before text_to_speech to discover a valid voice identifier. There is no explicit when-to-use statement, no when-not condition, and no named alternative, so it lands at the minimum-viable tier.

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