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search_voices

Search your ElevenLabs voice library by name, description, labels, or category to find specific voices for text-to-speech projects.

Instructions

Search for existing voices, a voice that has already been added to the user's ElevenLabs voice library.
Searches in name, description, labels and category.

Args:
    search: Search term to filter voices by. Searches in name, description, labels and category.
    sort: Which field to sort by. `created_at_unix` might not be available for older voices.
    sort_direction: Sort order, either ascending or descending.

Returns:
    List of voices that match the search criteria.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
searchNo
sortNoname
sort_directionNodesc

Implementation Reference

  • The handler function for the 'search_voices' tool. It uses the ElevenLabs client to search voices based on the provided parameters and returns a list of McpVoice objects.
    @mcp.tool(
        description="""
        Search for existing voices, a voice that has already been added to the user's ElevenLabs voice library.
        Searches in name, description, labels and category.
    
        Args:
            search: Search term to filter voices by. Searches in name, description, labels and category.
            sort: Which field to sort by. `created_at_unix` might not be available for older voices.
            sort_direction: Sort order, either ascending or descending.
    
        Returns:
            List of voices that match the search criteria.
        """
    )
    def search_voices(
        search: str | None = None,
        sort: Literal["created_at_unix", "name"] = "name",
        sort_direction: Literal["asc", "desc"] = "desc",
    ) -> list[McpVoice]:
        response = client.voices.search(
            search=search, sort=sort, sort_direction=sort_direction
        )
        return [
            McpVoice(id=voice.voice_id, name=voice.name, category=voice.category)
            for voice in response.voices
        ]
  • Pydantic BaseModel defining the structure of McpVoice, used as the return type for search_voices.
    class McpVoice(BaseModel):
        id: str
        name: str
        category: str
        fine_tuning_status: Optional[Dict] = None
  • The @mcp.tool decorator registers the search_voices function as an MCP tool.
    @mcp.tool(
Behavior2/5

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

No annotations are provided, so the description carries full burden. It mentions searching across multiple fields and returns a list, but lacks behavioral details like pagination, rate limits, authentication needs, or error handling. For a search tool with zero annotation coverage, this leaves significant gaps in understanding how it behaves.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is well-structured with clear sections (purpose, args, returns) and uses 4 sentences efficiently. Each sentence adds value: the first states purpose, the second clarifies scope, and the args/returns sections provide necessary details without redundancy. It could be slightly more concise by integrating the search scope into the purpose sentence.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness3/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given 3 parameters with 0% schema coverage and no annotations or output schema, the description does an adequate job explaining parameters and return value. However, it lacks context on behavioral aspects like pagination, error cases, or performance limits, which are important for a search tool. It's minimally viable but has clear gaps.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 0%, so the description must compensate. It explains all three parameters: 'search' filters across multiple fields, 'sort' specifies sorting field with a note about 'created_at_unix' availability, and 'sort_direction' defines order. This adds meaningful context beyond the bare schema, though it doesn't detail default behaviors or constraints fully.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool searches for existing voices in the user's ElevenLabs voice library, specifying the verb 'search' and resource 'voices'. It distinguishes from siblings like 'get_voice' (singular retrieval) and 'voice_clone' (creation), though not explicitly named. However, it doesn't fully differentiate from 'search_voice_library', which appears similar.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description implies usage when searching across multiple voice attributes (name, description, labels, category), suggesting it's for broader queries. It doesn't explicitly state when to use this vs. alternatives like 'get_voice' (for specific voice retrieval) or 'search_voice_library' (unclear distinction), and provides no exclusions or prerequisites.

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

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