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

Annotations already declare it is not read-only, is open-world (https sources), and non-destructive. The description adds billing per 10 seconds and the noise-cleaning behavior expectation, but it still omits whether the result is synchronous or requires polling (e.g., get_audio_status) and what artifact is returned.

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?

Front-loads the core transformation, then moves through required inputs, defaults, optional cleanup, billing, and the alternative tool. Every sentence contributes; no 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?

Covers the required inputs, model default, noise-cleanup guidance, cost, and the key sibling alternative, which is strong for a 6-param tool. However, with no output schema it does not tell the agent what is returned or whether the job is async, a moderate remaining 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?

Schema description coverage is 100%, so the schema already documents all six parameters. The description largely repeats the source/voice/model definitions and the noise flag, adding only a when-to-use cue for remove_background_noise; stability and similarity are left to the schema.

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?

States a specific transformation—re-voicing a spoken recording while preserving pauses, timing, emphasis and feeling—and names the sibling it is not (generate_speech). An agent can tell exactly what this tool produces and how it differs from text-to-speech.

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?

Explicitly says to use this for re-voicing a real performance and to use generate_speech for speaking written words from scratch. It also gives when to enable remove_background_noise and how to obtain voice ids from list_voices, leaving little inference.

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