generate_audio

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

No annotations are provided, so the description carries the full burden of behavioral disclosure. It only states the core function and does not mention output behavior, default display mode, model-specific caveats, resource usage, or how results are returned to the agent.

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 a single, front-loaded sentence with no filler or redundancy. It efficiently conveys the purpose without duplicating schema information, which is appropriate given the rich input 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?

The schema covers the parameters thoroughly, and some parameter descriptions (e.g., display_mode, save_location) indirectly address output handling. However, with no annotations and no output schema, the description alone does not provide enough behavioral or workflow context for such a complex generative tool with 14 parameters.

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 all 14 parameters are already documented with meaningful descriptions and ranges. The description itself adds no parameter-level detail beyond echoing that text is converted to speech, so the baseline score 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 ('generate'), specific resource ('speech audio from text'), and names the method ('TTS models'). This makes it clearly distinguishable from siblings like generate_image, generate_video, and especially generate_music.

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 purpose implies when the tool should be used (text-to-speech), and the sibling list includes generate_music as a contrast. However, there is no explicit statement about when to use this tool over alternatives or exclusions, leaving usage routing to inference.

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