Retrieves a comprehensive list of available Text to Speech voices used for generating audio voiceovers. You must call this tool first to obtain the required voice object before you can generate any Text to Speech audio. You can optionally filter the voices by providing a Language_code.
| Name | Required | Description | Default |
|---|---|---|---|
| language_code | No | Optional language code to filter the voices by (e.g. 'en-US', 'af-ZA'). |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the burden. It discloses that the tool is a retrieval operation (non-destructive) and that it returns a list of voices, and emphasizes that obtaining a voice object is a prerequisite for TTS generation. It does not mention potential response size or error behavior, but for a simple list-retrieval tool this is adequate.
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?
Three sentences, each earning its place. The description is front-loaded with the tool's main purpose, followed by the critical prerequisite, then the optional parameter. No unnecessary words or repetition.
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 tool's simplicity (only one optional parameter, no output schema, no annotations), the description fully covers the essential information: what it does, when to use it, and how to use the parameter. It also aligns with sibling tool context by establishing the dependency on generate_tts.
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%; the schema already describes language_code as an optional filter. The description adds no new information about the parameter beyond what the schema provides, so the baseline of 3 is appropriate.
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 retrieves a comprehensive list of Text to Speech voices, with a specific verb ('Retrieves') and resource. It also distinguishes itself from sibling tools by explicitly noting it must be called before generating any audio, which is the key differentiator.
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 provides explicit usage guidance: 'You must call this tool first to obtain the required voice object before you can generate any Text to Speech audio.' This clearly indicates when to use this tool versus generate_tts, and the optional filtering by language_code adds practical context.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.
Each tool has a distinct, non-overlapping purpose: generate_tts creates audio, get_voices fetches voice options, and usage_statistic monitors quota. No ambiguity exists between them.
Tool names are readable and mostly follow a verb_noun pattern (generate_tts, get_voices), but 'usage_statistic' deviates by using a noun phrase without a verb. The style is consistent in snake_case, but the structural pattern is mixed.
Three tools is a well-scoped count for a TTS server, covering the essential operations: listing voices, generating speech, and checking API usage. No unnecessary tools or glaring omissions.
The core workflow (get voices -> generate TTS) is fully covered, with usage monitoring as a useful addition. Minor gaps might include advanced audio format specifications, but nothing that blocks typical usage.