ElevenLabs MCP Server: Integrating Text-to-Speech Capabilities through Model Context Protocol

Overview: What is ElevenLabs MCP Server?

The ElevenLabs MCP Server is a dedicated solution that leverages the Model Context Protocol (MCP) to facilitate seamless integration between AI applications and external tools. Specifically, it provides an endpoint for generating audio from text using the ElevenLabs API. This server not only streamlines the process of voice synthesis but also offers a robust feature set including multiple voices, script management, and persistent storage through SQLite databases.

🔧 Core Features & MCP Capabilities

Audio Generation

The ElevenLabs MCP Server supports dynamic generation of audio from text input. Using ElevenLabs' API, users can generate high-quality audio with various customization options such as voice selection, stability settings, and style tuning.

Multi-Voice Management

It handles multiple voices and allows for complex script tasks by segmenting text into parts that can be assigned different voices or styles. This flexibility is crucial for creating more nuanced and expressive audio outputs.

Persistent History Storage

The server uses SQLite for storing job history, enabling users to keep track of past audio generations and easily revisit or reuse content as needed. This feature enhances work efficiency by providing a centralized repository of voiceover jobs.

⚙️ MCP Architecture & Protocol Implementation

The ElevenLabs MCP Server is built with a focus on compatibility and flexibility. It adheres strictly to the Model Context Protocol, which acts as the standard interface for connecting various AI applications with external tools. The server's core structure includes client communication, data handling, and API interaction.

Client Communication

MCP clients, such as Claude Desktop or Continue, communicate with the ElevenLabs MCP Server over a standardized protocol. This ensures interoperability across different platforms while maintaining consistent performance.

Data Handling & Storage

The server manages voiceover jobs efficiently through its integration with SQLite databases. These databases store detailed information about each job, including metadata and output files, allowing for easy retrieval and tracking of audio generations.

🚀 Getting Started with Installation

To install the ElevenLabs MCP Server, developers can choose between two installation methods: using uvx or a development setup. Both approaches require setting up environment variables necessary for the server to function correctly.

Using uvx (Recommended)

For users familiar with uvx, no additional steps are required beyond adding the appropriate configuration to their MCP settings file:

{
  "mcpServers": {
    "elevenlabs": {
      "command": "uvx",
      "args": ["elevenlabs-mcp-server"],
      "env": {
        "ELEVENLABS_API_KEY": "your-api-key",
        "ELEVENLABS_VOICE_ID": "your-voice-id",
        "ELEVENLABS_MODEL_ID": "eleven_flash_v2",
        "ELEVENLABS_STABILITY": "0.5",
        "ELEVENLABS_SIMILARITY_BOOST": "0.75",
        "ELEVENLABS_STYLE": "0.1",
        "ELEVENLABS_OUTPUT_DIR": "output"
      }
    }
  }
}

Development Installation

For those developing or testing the server, follow these steps:

1. Clone the repository.
2. Install dependencies using uv venv.
3. Configure environment variables by copying and editing .env.example to .env.

{
  "mcpServers": {
    "elevenlabs": {
      "command": "uv",
      "args": [
        "--directory",
        "path/to/elevenlabs-mcp-server",
        "run",
        "elevenlabs-mcp-server"
      ],
      "env": {
        "ELEVENLABS_API_KEY": "your-api-key",
        "ELEVENLABS_VOICE_ID": "your-voice-id",
        "ELEVENLABS_MODEL_ID": "eleven_flash_v2",
        "ELEVENLABS_STABILITY": "0.5",
        "ELEVENLABS_SIMILARITY_BOOST": "0.75",
        "ELEIVENLABS_STYLE": "0.1",
        "ELEVENLABS_OUTPUT_DIR": "output"
      }
    }
  }
}

💡 Key Use Cases in AI Workflows

Case Study: Speech Synthesis for Virtual Assistants

In this scenario, the ElevenLabs MCP Server can be used to power a virtual assistant application. Users can define complex scripts with different voices and styles corresponding to various roles (e.g., customer service representative or personal health advisor). This setup ensures that the speech output is natural and contextually appropriate.

Case Study: Corporate Training Videos

Developers can use this server to generate voiceovers for corporate training videos. By segmenting the script into sections, they can apply different voices or styles depending on who the key deliverer of each part is. This allows for a more engaging and detailed learning experience.

🔌 Integration with MCP Clients

The ElevenLabs MCP Server seamlessly interacts with various MCP clients such as Claude Desktop, Continue, and Cursor. The following table outlines the compatibility matrix:

MCP Client	Resources	Tools	Prompts	Status
Claude Desktop	✅	✅	✅	Full Support
Continue	✅	✅	✅	Full Support
Cursor	❌	✅	❌	Tools Only

📊 Performance & Compatibility Matrix

The ElevenLabs MCP Server is optimized for performance and compatibility across a range of use cases. It supports multiple voices, customizable audio settings, and persistent job storage, making it suitable for both simple text-to-speech tasks and complex script-based projects.

For detailed statistics on performance, please refer to the performance_tests directory in the repository.

🛠️ Advanced Configuration & Security

Advanced configurations enable users to fine-tune their voiceover jobs. Users can customize various aspects such as stability settings (ELEVENLABS_STABILITY), similarity boost (ELEIVENLABS_SIMILARITY_BOOST), and style tuning (ELEIVENLABS_STYLE). The output directory is configured via ELEVENLABS_OUTPUT_DIR`.

Security measures include securing API keys and ensuring proper access controls. Users are advised to follow best practices for managing sensitive information.

❓ Frequently Asked Questions (FAQ)

Q: How do I integrate this server with other AI applications?

A: The ElevenLabs MCP Server supports integration with various MCP clients such as Claude Desktop, Continue, and Cursor through its adherence to the Model Context Protocol. Simply configure your MCP settings file accordingly.

Q: Can I use different voices for parts of a script?

A: Yes, you can assign multiple voices to segments in your script. This allows for a more polished and natural result by utilizing different voice characteristics for different roles or contexts within the same piece of content.

Q: How is job history stored, and can it be accessed later?

A: Job history is persisted using SQLite databases. You can retrieve past audio generations using the get_voiceover_history tool, which allows you to specify a job ID if needed.

Q: What are the system requirements for running this server?

A: The server is designed to run on most modern systems with internet access. Ensure that your environment meets the dependencies outlined in the README to avoid any issues.

Q: Can I modify the default voice settings, and how?

A: Yes, you can adjust several parameters such as stability, similarity boost, and style using environment variables defined in your MCP settings file or .env configuration.

👨‍💻 Development & Contribution Guidelines

Contributors are welcome to enhance the ElevenLabs MCP Server. For contributions, please adhere to the guidelines documented in the CONTRIBUTING.md file. This includes cloning the repository, setting up a development environment, and ensuring that your code complies with established coding standards.

🌐 MCP Ecosystem & Resources

The ElevenLabs MCP Server is part of an expanding ecosystem of tools and technologies aimed at improving AI application integration through standard protocols like MCP. Explore additional resources on the Model Context Protocol website or within the GitHub repository for more information.

By following these guidelines, developers can leverage the power of the ElevenLabs MCP Server to build innovative text-to-speech solutions that integrate seamlessly with a wide range of AI applications and tools.