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elevenlabs

The official ElevenLabs MCP server

Publisherelevenlabs
Repositoryelevenlabs-mcp
LanguagePython
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227
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Available tools
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Transport typestdio
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LicenseMIT
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  • Connect tools to AI workflows

    ElevenLabs exposes MCP capabilities that can be used by compatible AI clients and agents.

  • 0 available tools

    Browse the callable actions below, including names and descriptions when provided by the server.

  • Ready-to-copy setup

    Use the installation snippets to configure this server in your preferred MCP client.

  • Open source signals

    1.4K stars and 227 forks from the linked repository.

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Quickstart with Claude Desktop

  1. Get your API key from ElevenLabs. There is a free tier with 10k credits per month.
  2. Install uv (Python package manager), install with curl -LsSf https://astral.sh/uv/install.sh | sh or see the uv repo for additional install methods.
  3. Go to Claude > Settings > Developer > Edit Config > claude_desktop_config.json to include the following:
{
  "mcpServers": {
    "ElevenLabs": {
      "command": "uvx",
      "args": ["elevenlabs-mcp"],
      "env": {
        "ELEVENLABS_API_KEY": "<insert-your-api-key-here>"
      }
    }
  }
}

If you're using Windows, you will have to enable "Developer Mode" in Claude Desktop to use the MCP server. Click "Help" in the hamburger menu at the top left and select "Enable Developer Mode".

Other MCP clients

For other clients like Cursor and Windsurf, run:

  1. pip install elevenlabs-mcp
  2. python -m elevenlabs_mcp --api-key={{PUT_YOUR_API_KEY_HERE}} --print to get the configuration. Paste it into appropriate configuration directory specified by your MCP client.

That's it. Your MCP client can now interact with ElevenLabs through these tools:

Example usage

⚠️ Warning: ElevenLabs credits are needed to use these tools.

Try asking Claude:

  • "Create an AI agent that speaks like a film noir detective and can answer questions about classic movies"
  • "Generate three voice variations for a wise, ancient dragon character, then I will choose my favorite voice to add to my voice library"
  • "Convert this recording of my voice to sound like a medieval knight"
  • "Create a soundscape of a thunderstorm in a dense jungle with animals reacting to the weather"
  • "Turn this speech into text, identify different speakers, then convert it back using unique voices for each person"

Optional features

File Output Configuration

You can configure how the MCP server handles file outputs using these environment variables in your claude_desktop_config.json:

  • ELEVENLABS_MCP_BASE_PATH: Specify the base path for file operations with relative paths (default: ~/Desktop)
  • ELEVENLABS_MCP_OUTPUT_MODE: Control how generated files are returned (default: files)

Output Modes

The ELEVENLABS_MCP_OUTPUT_MODE environment variable supports three modes:

  1. files (default): Save files to disk and return file paths

    json
    "env": {
      "ELEVENLABS_API_KEY": "your-api-key",
      "ELEVENLABS_MCP_OUTPUT_MODE": "files"
    }
  2. resources: Return files as MCP resources; binary data is base64-encoded, text is returned as UTF-8 text

    json
    "env": {
      "ELEVENLABS_API_KEY": "your-api-key",
      "ELEVENLABS_MCP_OUTPUT_MODE": "resources"
    }
  3. both: Save files to disk AND return as MCP resources

    json
    "env": {
      "ELEVENLABS_API_KEY": "your-api-key",
      "ELEVENLABS_MCP_OUTPUT_MODE": "both"
    }

Resource Mode Benefits:

  • Files are returned directly in the MCP response as base64-encoded data
  • No disk I/O required - useful for containerized or serverless environments
  • MCP clients can access file content immediately without file system access
  • In both mode, resources can be fetched later using the elevenlabs://filename URI pattern

Use Cases:

  • files: Traditional file-based workflows, local development
  • resources: Cloud environments, MCP clients without file system access
  • both: Maximum flexibility, caching, and resource sharing scenarios

Data residency keys

You can specify the data residency region with the ELEVENLABS_API_RESIDENCY environment variable. Defaults to "us".

Note: Data residency is an enterprise only feature. See the docs for more details.

Contributing

If you want to contribute or run from source:

  1. Clone the repository:
bash
git clone https://github.com/elevenlabs/elevenlabs-mcp
cd elevenlabs-mcp
  1. Create a virtual environment and install dependencies using uv:
bash
uv venv
source .venv/bin/activate
uv pip install -e ".[dev]"
  1. Copy .env.example to .env and add your ElevenLabs API key:
bash
cp .env.example .env
# Edit .env and add your API key
  1. Run the tests to make sure everything is working:
bash
./scripts/test.sh
# Or with options
./scripts/test.sh --verbose --fail-fast
  1. Install the server in Claude Desktop: mcp install elevenlabs_mcp/server.py

  2. Debug and test locally with MCP Inspector: mcp dev elevenlabs_mcp/server.py

Troubleshooting

Logs when running with Claude Desktop can be found at:

  • Windows: %APPDATA%\Claude\logs\mcp-server-elevenlabs.log
  • macOS: ~/Library/Logs/Claude/mcp-server-elevenlabs.log

Timeouts when using certain tools

Certain ElevenLabs API operations, like voice design and audio isolation, can take a long time to resolve. When using the MCP inspector in dev mode, you might get timeout errors despite the tool completing its intended task.

This shouldn't occur when using a client like Claude.

MCP ElevenLabs: spawn uvx ENOENT

If you encounter the error "MCP ElevenLabs: spawn uvx ENOENT", confirm its absolute path by running this command in your terminal:

bash
which uvx

Once you obtain the absolute path (e.g., /usr/local/bin/uvx), update your configuration to use that path (e.g., "command": "/usr/local/bin/uvx"). This ensures that the correct executable is referenced.

Installation

TypingMind
Prerequisites:

Node.js 18+

{
  "mcpServers": {
    "ElevenLabs": {
      "command": "uvx",
      "args": [
        "elevenlabs-mcp"
      ],
      "env": {
        "ELEVENLABS_API_KEY": "<insert-your-api-key-here>"
      }
    }
  }
}

Use ElevenLabs MCP with multiple AI models

TypingMind connects MCP tools at the workspace level, so once ElevenLabs is connected, you can use it with different AI models in TypingMind instead of setting it up separately for each model. This MCP runs locally through the TypingMind MCP connector on your device.

Setup guide to use the local connector

Use this when the MCP server needs access to local files, apps, or private resources on your computer.

1

Open the MCP settings

In TypingMind, go to Settings, Advanced Settings, then Model Context Protocol and choose Setup Connector.

  1. Open TypingMind in your browser.
  2. Click the Settings icon.
  3. Go to Advanced Settings.
  4. Open the Model Context Protocol section.
  5. Click Setup Connector and choose This Device.
TypingMind MCP connector setup screen with This Device selected
2

Run the connector command

Choose This Device, copy the command from TypingMind, and run it in Terminal. Keep the process running while you use MCP.

  1. Copy the setup command shown by TypingMind.
  2. Open Terminal on macOS or Windows Terminal on Windows.
  3. Paste and run the command.
  4. Approve the package install if Terminal asks you to proceed.
  5. Keep the Terminal window running while using MCP tools.
3

Add ElevenLabs as a server

When the connector status is Ready, click Edit Servers and paste the MCP server configuration.

  1. Wait until the connector status shows Ready.
  2. Click Edit Servers.
  3. Paste the ElevenLabs MCP server configuration.
  4. Save the server list.
  5. Refresh if you want to confirm the connector is still ready.
TypingMind MCP settings showing active server and Edit Servers button
{
  "mcpServers": {
    "elevenlabs": {
      "command": "npx",
      "args": [
        "-y",
        "elevenlabs-mcp"
      ]
    }
  }
}
4

Use it across models

Save the server list, open Plugins, enable the ElevenLabs MCP tools, then select any supported AI model in TypingMind and use the tools in chat or assign them to an AI agent.

  1. Open the Plugins page in TypingMind.
  2. Enable the ElevenLabs MCP tools.
  3. Start a chat and choose the AI model you want to use.
  4. Use the MCP tools in chat or assign them to an AI agent.
  5. Switch to another AI model whenever needed without reconnecting MCP.
TypingMind chat using enabled MCP tools with a selected AI model
Can you use ElevenLabs to help me with this task?
ElevenLabs
Sure. I read it.
Here is what I found using ElevenLabs.

Frequently asked questions

What is the ElevenLabs MCP server used for?

ElevenLabs is an MCP server that lets compatible AI clients connect to external tools and context. In TypingMind, you can add this MCP server once and make its tools available in your AI workspace.

Can I use ElevenLabs MCP with multiple AI models in TypingMind?

Yes. TypingMind connects MCP tools at the workspace level, so you can use ElevenLabs with different AI models such as Claude, ChatGPT, Gemini, or other models you have configured in TypingMind without setting up the MCP server separately for each model.

Why use ElevenLabs MCP with TypingMind?

TypingMind is one of the best frontends for LLM chat because it brings multiple AI models, prompts, plugins, AI agents, API keys, and MCP tools into one workspace. With ElevenLabs connected, you can use its MCP tools across your preferred models while keeping your chat workflow organized in TypingMind.

How do I connect ElevenLabs MCP to TypingMind?

ElevenLabs runs through the TypingMind local MCP connector. This is best when the MCP server needs access to local files, desktop apps, command-line tools, or private resources on your computer.

What tools does ElevenLabs MCP provide in TypingMind?

ElevenLabs exposes MCP capabilities that can be enabled from the TypingMind Plugins page and used in chat or assigned to AI agents.

Do I need to share my API keys with TypingMind to use ElevenLabs MCP?

No. TypingMind is local-first and lets you keep your model providers, API keys, prompts, and MCP configuration under your control. If ElevenLabs requires authentication, add the required headers, OAuth settings, or local configuration for that MCP server when you create the connection.

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