ElevenLabs MCP Server

What does “ElevenLabs” MCP Server do?

The ElevenLabs MCP Server is a Model Context Protocol (MCP) server that integrates the ElevenLabs text-to-speech API with AI development workflows. It functions as a bridge, enabling AI assistants and agents to generate high-quality audio from text, manage voice options, and keep track of audio generation history—all via standardized MCP interfaces. The server supports multiple voices, script part management, and persistent storage using SQLite, making it suitable for robust voice synthesis tasks. Additionally, it ships with a sample SvelteKit-based MCP client for managing and interacting with these features through a web interface. By exposing voice generation as tools and resources, ElevenLabs MCP Server enhances automation, accessibility, and context-awareness in AI-powered applications.

List of Prompts

No explicit prompt templates are listed in the repository or documentation.

List of Resources

• Voice History: Persistent storage of generated voice/audio history using SQLite, allowing users to track and replay previously generated audio.
• Voice Options: Provides access to available ElevenLabs voice models and configurations for selection and management.
• Audio File Downloads: Enables users to download generated audio files for offline use or integration into other applications.

List of Tools

• Generate Audio from Text: Converts input text into speech using the ElevenLabs API, supporting multiple voice models and customization parameters.
• Multi-part Script Management: Allows users to generate and manage audio for scripts divided into multiple parts, streamlining long-form voice synthesis tasks.
• Voice History Playback: Facilitates playback of previously generated audio directly from the stored history, enhancing user experience and productivity.

Use Cases of this MCP Server

• Text-to-Speech Automation: Quickly convert text-based content to high-quality audio for podcasts, audiobooks, or accessibility features, reducing manual workload.
• Voice Assistant Development: Integrate realistic voice responses in AI-driven assistants for more natural human-computer interactions.
• Content Localization: Generate audio outputs in different voices or accents to localize products and services without hiring multiple voice actors.
• Scripted Dialogue Generation: Manage and produce multipart scripts for games, storytelling, or e-learning platforms efficiently.
• Voice Generation History Management: Track and reuse previously generated audio clips, streamlining revision workflows and ensuring consistency.

How to set it up

Windsurf

1. Ensure you have Node.js installed.

2. Locate the Windsurf MCP configuration file (e.g., windsurf_mcp_settings.json).

3. Add the ElevenLabs MCP Server configuration:

   {
     "mcpServers": {
       "elevenlabs": {
         "command": "uvx",
         "args": ["elevenlabs-mcp-server"],
         "env": {
           "ELEVENLABS_API_KEY": "your-api-key",
           "ELEVENLABS_VOICE_ID": "your-voice-id"
         }
       }
     }
   }
   

4. Save the file and restart Windsurf.

5. Verify ElevenLabs MCP Server appears as a tool in the interface.

Claude

1. Prerequisite: Install Node.js.

2. Open your Claude MCP configuration file (e.g., cline_mcp_settings.json).

3. Add ElevenLabs MCP Server using:

   {
     "mcpServers": {
       "elevenlabs": {
         "command": "uvx",
         "args": ["elevenlabs-mcp-server"],
         "env": {
           "ELEVENLABS_API_KEY": "your-api-key",
           "ELEVENLABS_VOICE_ID": "your-voice-id"
         }
       }
     }
   }
   

4. Save and restart Claude Desktop.

5. Confirm ElevenLabs MCP Server is available as a tool.

Cursor

1. Make sure Node.js is installed on your system.

2. Open Cursor’s MCP server configuration file.

3. Insert the following JSON configuration:

   {
     "mcpServers": {
       "elevenlabs": {
         "command": "uvx",
         "args": ["elevenlabs-mcp-server"],
         "env": {
           "ELEVENLABS_API_KEY": "your-api-key",
           "ELEVENLABS_VOICE_ID": "your-voice-id"
         }
       }
     }
   }
   

4. Save the configuration and restart Cursor.

5. Check for ElevenLabs MCP Server’s availability.

Cline

1. Prerequisite: Node.js installed.

2. Access Cline’s MCP configuration file (e.g., cline_mcp_settings.json).

3. Add ElevenLabs MCP Server:

   {
     "mcpServers": {
       "elevenlabs": {
         "command": "uvx",
         "args": ["elevenlabs-mcp-server"],
         "env": {
           "ELEVENLABS_API_KEY": "your-api-key",
           "ELEVENLABS_VOICE_ID": "your-voice-id"
         }
       }
     }
   }
   

4. Save and restart Cline.

5. Confirm ElevenLabs MCP Server is accessible.

Securing API Keys:
Store all sensitive values such as your API key in environment variables via the env field in your JSON configuration:

"env": {
  "ELEVENLABS_API_KEY": "your-api-key",
  "ELEVENLABS_VOICE_ID": "your-voice-id"
}

Never hardcode secrets in public files.

How to use this MCP inside flows

Using MCP in FlowHunt

To integrate MCP servers into your FlowHunt workflow, start by adding the MCP component to your flow and connecting it to your AI agent:

Click on the MCP component to open the configuration panel. In the system MCP configuration section, insert your MCP server details using this JSON format:

{
  "elevenlabs": {
    "transport": "streamable_http",
    "url": "https://yourmcpserver.example/pathtothemcp/url"
  }
}

Once configured, the AI agent is now able to use this MCP as a tool with access to all its functions and capabilities. Replace “elevenlabs” with the actual name of your MCP server and the URL with your server’s address.

Overview

| Roots Support | ⛔ | Not mentioned |

I would rate this MCP server 7/10. It has a clear purpose, practical tools and resources, and solid setup documentation, but lacks info on prompt templates, roots, and sampling support.

MCP Score