design_voice

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

With no annotations, the description carries the full burden. It transparently discloses side effects (previews saved to output dir, save_as consumes a custom-voice slot), reproducibility via seed, parameter ranges, and the return format including error handling. This goes well beyond minimal expectations.

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 front-loaded with purpose, then a brief workflow, then structured Args and Returns sections. Every sentence adds value, and the length is justified by the complexity of the tool.

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 the lack of annotations and output schema, the description is remarkably complete: it covers all parameters, side effects, workflow steps, and return format. No critical gaps are apparent.

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 0%, so the description fully compensates by explaining every parameter: description, save_as (with slot consumption), text (including character range), model (with defaults), guidance_scale (range and effect), loudness (range and default), and seed (reproducibility). This is exemplary.

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 designs a brand-new custom voice from a text description (ElevenLabs Voice Design) and provides a concrete example. This distinguishes it from sibling tools like clone_voice, convert_voice, or preview_voices.

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 gives a clear workflow: describe the voice, get previews, audition them, then keep one via save_voice or pass save_as to auto-save. It implies when to use this tool versus alternatives, but does not explicitly state exclusions or name alternative tools as comparisons.

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