List Voices For A Hedra Model

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

Annotations already declare read-only and idempotent behavior, so the description adds beyond that by detailing error handling ('Error [NOT_FOUND]...' when model has no voice input) and the scoping nuance (voices differ by provider). This provides useful behavioral context not present in 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 well-organized with clear sections for Args, Returns, and Error Handling. While slightly verbose for a simple one-param tool, every sentence adds information and the structure aids scanning.

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?

For a simple read-only tool with one parameter, the description covers purpose, usage timing, parameter meaning, return format, and error behavior. No output schema exists, so the description appropriately explains the return shape, making it fully complete.

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 coverage is 100% for the single parameter, but the description adds meaningful examples ('hedra-avatar', 'elevenlabs-v3') and clarifies the parameter is the model's public id. This supplements the schema's examples, providing extra semantic value.

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 text-to-speech voices for a specific model, using a specific verb and resource. It distinguishes itself from siblings like hedra_list_models (which lists models) and hedra_get_model (which retrieves model details), making its unique purpose unambiguous.

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 explicitly says 'Use this before submitting a job to a speech or avatar model that takes a voice_id input,' providing clear when-to-use guidance. It does not explicitly name alternatives or exclusions, but the context is sufficiently clear for selecting this tool.

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