List Voices

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

Annotations already declare readOnly, idempotent, openWorld, and non-destructive behavior. The description adds useful context: the returned voice_id feeds into xbrush_tts_generate, and omitting model uses the default provider. No contradiction with 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?

The description is short and front-loaded with the primary purpose. The Args block is somewhat redundant with the schema but keeps information inline for quick reading. Overall it is appropriately sized and well organized.

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 tool is simple with one optional parameter and no output schema. The description covers the main usage pattern and integration with xbrush_tts_generate. It does not detail the return format, but that is not critical given the tool's low complexity.

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 model parameter is already well documented with examples and a reference to xbrush_list_models. The description's Args section mostly repeats the schema and adds no new semantic meaning beyond restating optional behavior.

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 lists voices for text-to-speech, optionally filtered by model/provider. It differentiates from sibling tools by explicitly mentioning TTS and linking to xbrush_tts_generate. The verb 'list' and resource 'voices' are specific.

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 tells the agent when to use the tool (to select a voice_id for xbrush_tts_generate) and how to optionally filter by model. It does not explicitly mention exclusions or when to prefer alternatives like xbrush_list_models, but the context is clear.

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