text_to_speech

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

No annotations are present, so the description bears full responsibility for behavioral disclosure. It partially addresses behavior by noting that Pro voices may support emotion while Lite voices do not, but does not reveal other traits such as authentication requirements, rate limits, or what happens if emotion is used with a Lite voice.

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 two sentences, front-loaded with the core purpose. Every word is useful, no redundancy or filler.

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?

Given six parameters and the presence of an output schema, the description is incomplete. It does not describe what is returned (e.g., audio file, base64), nor does it explain non-obvious parameters like speed_rate or language format. The agent would need to infer or experiment to use the tool effectively.

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?

With 0% schema description coverage, the description must compensate but only adds minimal context for the emotion parameter. It does not explain text, language, speed_rate, voice_name, or generate_subtitle, leaving the agent to guess their meaning and valid values.

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 clearly states the tool generates speech from text, which is a specific verb and resource. It distinguishes from sibling tools like transcribe_* which do the opposite.

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 implies when to use (to generate speech) but does not explicitly state when not to use or mention alternatives. No exclusions are provided, and the agent must infer usage context from the lack of similar tools.

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