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ElevenLabs MCP Server

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Python
Remote

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

  1. Clone the repository:

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

  1. Install dependencies:

pip install -r requirements.txt

  1. Set up environment variables:

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

  1. 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

  1. Build the Docker image:

docker build -t elevenlabs-mcp-server .

  1. 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

  1. Fork the repository

  2. Create a feature branch

  3. Make your changes

  4. Add tests for new functionality

  5. Ensure all tests pass

  6. Submit a pull request

License

MIT License - see LICENSE file for details.

Support

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

This server cannot be installed

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security - not tested
A
license - permissive license
-
quality - not tested

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