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mamertofabian

ElevenLabs MCP Server

list_voices

Retrieve available voice options with metadata from ElevenLabs text-to-speech service to select appropriate voices for audio generation.

Instructions

Get a list of all available ElevenLabs voices with metadata

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Implementation Reference

  • Handler for the 'list_voices' tool within the call_tool dispatcher. Retrieves voices from database cache, refreshes via ElevenLabs API if needed, marks default voice, and returns JSON list.
    elif name == "list_voices":
        try:
            # Get voices from cache
            voices, needs_refresh = await self.db.get_voices()
            
            # Refresh cache if needed
            if needs_refresh:
                try:
                    fresh_voices = await asyncio.to_thread(self.api.get_voices)
                    await self.db.upsert_voices(fresh_voices)
                    voices = fresh_voices
                except Exception as e:
                    logging.error(f"Error refreshing voices: {e}")
                    # Continue with cached data if refresh fails
                    if not voices:
                        raise  # Re-raise if we have no data at all
            
            # Ensure default voice is marked
            for voice in voices:
                voice["is_default"] = voice["voice_id"] == self.api.voice_id
            
            return [types.TextContent(
                type="text",
                text=json.dumps(voices, indent=2)
            )]
        except Exception as e:
            return [types.TextContent(
                type="text",
                text=json.dumps({"error": str(e)}, indent=2)
            )]
  • Registration of the 'list_voices' tool in the list_tools() response, including description and empty input schema.
    types.Tool(
        name="list_voices",
        description="Get a list of all available ElevenLabs voices with metadata",
        inputSchema={
            "type": "object",
            "properties": {},  # No parameters needed
            "required": []
        }
    ),
  • Input schema for 'list_voices' tool: empty object with no required properties.
    inputSchema={
        "type": "object",
        "properties": {},  # No parameters needed
        "required": []
    }
Behavior2/5

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

With no annotations provided, the description carries full burden for behavioral disclosure. It states what the tool does but doesn't describe traits like whether it's read-only (implied by 'Get'), rate limits, authentication needs, error conditions, or what 'metadata' includes. For a tool with zero annotation coverage, this leaves significant behavioral gaps.

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

Conciseness5/5

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

The description is a single, efficient sentence that front-loads the core purpose ('Get a list...') with no wasted words. Every part earns its place by specifying the resource and scope ('all available ElevenLabs voices with metadata').

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 the tool's simplicity (0 parameters, no output schema, no annotations), the description is minimally adequate. It explains what the tool does but lacks details on behavior, output format, or usage context. For a read operation with no structured output documentation, more completeness would help the agent understand what to expect.

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

Parameters4/5

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

The tool has 0 parameters with 100% schema description coverage, so the schema fully documents the lack of inputs. The description adds no parameter information beyond this, which is appropriate. Baseline for 0 parameters is 4, as no compensation is needed for schema gaps.

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 verb ('Get') and resource ('list of all available ElevenLabs voices with metadata'), making the purpose specific and understandable. It doesn't explicitly distinguish from sibling tools like 'get_voiceover_history' or 'get_audio_file', but the focus on voices rather than audio files or history provides implicit differentiation. A 5 would require explicit sibling comparison.

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

Usage Guidelines2/5

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

The description provides no guidance on when to use this tool versus alternatives. It doesn't mention prerequisites, timing considerations, or compare it to sibling tools like 'get_voiceover_history' for historical data or 'generate_audio_simple' for voice selection. The agent must infer usage from the purpose alone.

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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