Which integrations are available for this server?

Provides a complete integration for ElevenLabs Conversational AI, enabling management of agents, webhook and client-side tools, and knowledge base document processing from text or URL sources with RAG support.

How do I use ElevenLabs MCP Server?

1. Click on "Install Server". 2. Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state. 3. In the chat, type @ followed by the MCP server name and your instructions, e.g., "@ElevenLabs MCP Server Add the FAQ from https://example.com/support to my knowledge base" That's it! The server will respond to your query, and you can continue using it as needed. Here is a step-by-step guide with screenshots.

ElevenLabs MCP Server

A complete Model Context Protocol (MCP) server for ElevenLabs Conversational AI, providing seamless integration with agents, tools, and knowledge base management.

Features

Agent Management: Create, update, delete, and list ElevenLabs conversational AI agents
Tools Integration: Manage webhook and client-side tools for agent functionality
Knowledge Base: Handle document upload, URL scraping, and text-based knowledge sources
RAG Support: Compute and manage Retrieval-Augmented Generation indices
Real-time Updates: Subscribe to resource changes and notifications
Claude Desktop Integration: Easy setup for Claude Desktop users
Cloud Deployment: Docker container ready for remote deployment

Installation

Local Development

Clone the repository:

git clone https://github.com/anthropics/elevenlabs-mcp-server.git
cd elevenlabs-mcp-server

Install dependencies:

pip install -r requirements.txt

Set up environment variables:

cp .env.example .env
# Edit .env with your ElevenLabs API key

Install the package:

pip install -e .

Production Installation

pip install elevenlabs-mcp-server

Configuration

Environment Variables

Create a .env file with the following variables:

ELEVENLABS_API_KEY=your-elevenlabs-api-key-here
ELEVENLABS_BASE_URL=https://api.elevenlabs.io/v1
MCP_SERVER_NAME=elevenlabs-mcp-server
MCP_SERVER_VERSION=1.0.0
REQUEST_TIMEOUT=30
MAX_RETRIES=3
LOG_LEVEL=INFO

Claude Desktop Integration

Add the following to your Claude Desktop configuration file:

macOS: ~/Library/Application Support/Claude/claude_desktop_config.json Windows: %APPDATA%/Claude/claude_desktop_config.json

{
  "mcpServers": {
    "elevenlabs": {
      "command": "python",
      "args": ["-m", "elevenlabs_mcp.server"],
      "env": {
        "ELEVENLABS_API_KEY": "your-elevenlabs-api-key-here"
      }
    }
  }
}

Usage

Starting the Server

# Using the installed command
elevenlabs-mcp-server

# Or using Python module
python -m elevenlabs_mcp.server

Available Tools

Agent Management

create_agent: Create a new conversational AI agent
get_agent: Retrieve agent configuration by ID
list_agents: List all agents with pagination
update_agent: Update existing agent configuration
delete_agent: Delete an agent

Tool Management

create_tool: Create webhook or client-side tools
get_tool: Retrieve tool configuration by ID
list_tools: List all tools with optional filtering
update_tool: Update existing tool configuration
delete_tool: Delete a tool

Knowledge Base Management

create_knowledge_base_from_text: Create knowledge base from text content
create_knowledge_base_from_url: Create knowledge base from URL scraping
get_knowledge_base_document: Retrieve document details
list_knowledge_base_documents: List all knowledge base documents
update_knowledge_base_document: Update document metadata
delete_knowledge_base_document: Delete a document
compute_rag_index: Compute RAG index for enhanced retrieval
get_document_content: Get full document content and chunks

Example Usage

Creating an Agent

{
  "conversation_config": {
    "agent": {
      "language": "en",
      "prompt": {
        "prompt": "You are a helpful customer service agent.",
        "built_in_tools": ["language_detection", "end_call"]
      },
      "first_message": "Hello! How can I help you today?"
    },
    "asr": {
      "quality": "high",
      "provider": "elevenlabs"
    },
    "tts": {
      "model_id": "eleven_turbo_v2",
      "voice_id": "21m00Tcm4TlvDq8ikWAM"
    }
  },
  "name": "Customer Service Agent"
}

Creating a Webhook Tool

{
  "tool_type": "webhook",
  "name": "weather_lookup",
  "description": "Get current weather information",
  "url": "https://api.weather.com/v1/current",
  "method": "GET",
  "parameters": [
    {
      "name": "location",
      "type": "string",
      "description": "City name for weather lookup",
      "required": true
    }
  ]
}

Creating Knowledge Base from Text

{
  "text": "This is important company information about our products...",
  "name": "Company Product Guide",
  "description": "Comprehensive guide to our product offerings"
}

Resources

The server exposes the following MCP resources:

elevenlabs://agents: List all agents
elevenlabs://tools: List all tools
elevenlabs://knowledge-base: List all knowledge base documents

Cloud Deployment

Docker

Build the Docker image:

docker build -t elevenlabs-mcp-server .

Run the container:

docker run -e ELEVENLABS_API_KEY=your-api-key elevenlabs-mcp-server

Docker Compose

version: '3.8'
services:
  elevenlabs-mcp:
    build: .
    environment:
      - ELEVENLABS_API_KEY=your-api-key
      - LOG_LEVEL=INFO
    ports:
      - "8000:8000"
    restart: unless-stopped

Cloud Platforms

Deploy to your preferred cloud platform:

AWS: Use ECS, EKS, or Lambda
Google Cloud: Use Cloud Run, GKE, or Cloud Functions
Azure: Use Container Instances, AKS, or Functions
Heroku: Use container deployment
Railway: Connect your GitHub repository

API Reference

Agent Configuration Schema

{
  "conversation_config": {
    "agent": {
      "language": "en",
      "prompt": {
        "prompt": "System prompt for the agent",
        "tool_ids": ["tool_id_1", "tool_id_2"],
        "built_in_tools": ["language_detection", "end_call"]
      },
      "first_message": "Initial greeting message"
    },
    "asr": {
      "quality": "high",
      "provider": "elevenlabs",
      "user_input_audio_format": "pcm_16000"
    },
    "tts": {
      "model_id": "eleven_turbo_v2",
      "voice_id": "voice_id_here"
    }
  },
  "platform_settings": {
    "evaluation_config": {
      "success_threshold": 0.7
    }
  }
}

Tool Configuration Schema

Webhook Tool

{
  "type": "webhook",
  "name": "tool_name",
  "description": "Tool description",
  "url": "https://api.example.com/endpoint",
  "method": "POST",
  "headers": {
    "Authorization": "Bearer token"
  },
  "parameters": [
    {
      "name": "param_name",
      "type": "string",
      "description": "Parameter description",
      "required": true
    }
  ]
}

Client Tool

{
  "type": "client",
  "name": "tool_name",
  "description": "Tool description",
  "parameters": [
    {
      "name": "param_name",
      "type": "string",
      "description": "Parameter description",
      "required": true
    }
  ],
  "wait_for_response": false
}

Error Handling

The server provides comprehensive error handling with structured error responses:

{
  "error": "Descriptive error message",
  "details": {
    "status_code": 400,
    "error_type": "validation_error"
  }
}

Development

Running Tests

# Install development dependencies
pip install -e ".[dev]"

# Run tests
pytest

# Run tests with coverage
pytest --cov=elevenlabs_mcp --cov-report=html

Code Quality

# Format code
black src/ tests/

# Sort imports
isort src/ tests/

# Lint code
flake8 src/ tests/

# Type checking
mypy src/

Contributing

Fork the repository
Create a feature branch
Make your changes
Add tests for new functionality
Ensure all tests pass
Submit a pull request

License

MIT License - see LICENSE file for details.

Support

Documentation: GitHub README
Issues: GitHub Issues
ElevenLabs API: Official Documentation
MCP Protocol: Specification

Changelog

v1.0.0

Initial release
Full agent management support
Tools and knowledge base integration
Claude Desktop configuration
Docker deployment support
Comprehensive error handling
Complete API coverage

ElevenLabs MCP Server

Features

Installation

Local Development

Production Installation

Configuration

Environment Variables

Claude Desktop Integration

Usage

Starting the Server

Available Tools

Agent Management

Tool Management

Knowledge Base Management

Example Usage

Creating an Agent

Creating a Webhook Tool

Creating Knowledge Base from Text

Resources

Cloud Deployment

Docker

Docker Compose

Cloud Platforms

API Reference

Agent Configuration Schema

Tool Configuration Schema

Webhook Tool

Client Tool

Error Handling

Development

Running Tests

Code Quality

Contributing

License

Support

Changelog

v1.0.0

Resources

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