Unlock powerful text to speech and AI voice generator tools with ElevenLabs. Create, clone, and customize speech easily.
by Add new capabilities to Claude beyond text generation
Example: Access external data sources, execute code, interact with tools and services
Transform Claude from chatbot to action-taking agent
Provide Claude with access to relevant context and data
Example: Load project documentation, access knowledge bases, query databases
Get more accurate, context-aware responses
Automate multi-step workflows combining AI and external tools
Example: Research → Summarize → Create document → Send notification
Complete complex tasks end-to-end without manual steps
Time estimate: 15-60 minutes depending on server complexity
Prerequisites
Readwise
Integrate Readwise to retrieve notes and search highlights, enhancing knowledge work—ideal for recovering deleted note o
Deep Research MCP
Use any LLM for deep research. Performs multi-step web search, content analysis, and synthesis for comprehensive researc
Chronulus AI Forecasting
Leverage Chronulus AI Forecasting for predictive analytics: analyze, predict, and visualize time series data with natura
Local RAG
Local RAG enables semantic document search using retrieval augmented generation (RAG) without external API calls.
ImageSorcery
Unlock powerful image manipulation with ImageSorcery: resize, crop, detect objects, and perform optical character recogn
I recommend ElevenLabs for teams standardizing on MCP; the explainx.ai page compares cleanly with sibling servers.
ElevenLabs reduced integration guesswork — categories and install configs on the listing matched the upstream repo.
ElevenLabs has been reliable for tool-calling workflows; the MCP profile page is a good permalink for internal docs.
Useful MCP listing: ElevenLabs is the kind of server we cite when onboarding engineers to host + tool permissions.
We wired ElevenLabs into a staging workspace; the listing’s GitHub and npm pointers saved time versus hunting across READMEs.
ElevenLabs reduced integration guesswork — categories and install configs on the listing matched the upstream repo.
ElevenLabs is a well-scoped MCP server in the explainx.ai directory — install snippets and categories matched our Claude Code setup.
We wired ElevenLabs into a staging workspace; the listing’s GitHub and npm pointers saved time versus hunting across READMEs.
According to our notes, ElevenLabs benefits from clear Model Context Protocol framing — fewer ambiguous “AI plugin” claims.
ElevenLabs is among the better-indexed MCP projects we tried; the explainx.ai summary tracks the official description.
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Claude Desktop configuration
{
"mcpServers": {
"ElevenLabs": {
"command": "uvx",
"args": ["elevenlabs-mcp"],
"env": {
"ELEVENLABS_API_KEY": "<insert-your-api-key-here>"
}
}
}
}Integrates ElevenLabs APIs to generate speech from text, clone voices, transcribe audio, and create custom sound effects. Requires ElevenLabs API key but offers 10k free credits monthly.
ElevenLabs is an official MCP server published by elevenlabs that provides AI assistants with tools and capabilities via the Model Context Protocol. Unlock powerful text to speech and AI voice generator tools with ElevenLabs. Create, clone, and customize speech easily. It is categorized under other, ai ml.
You can install ElevenLabs in your AI client of choice. Use the install panel on this page to get one-click setup for Cursor, Claude Desktop, VS Code, and other MCP-compatible clients. This server runs locally on your machine via the stdio transport.
uv (Python package manager), install with curl -LsSf https://astral.sh/uv/install.sh | sh or see the uv repo for additional install methods.{
"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".
For other clients like Cursor and Windsurf, run:
pip install elevenlabs-mcppython -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:
⚠️ Warning: ElevenLabs credits are needed to use these tools.
Try asking Claude:
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)The ELEVENLABS_MCP_OUTPUT_MODE environment variable supports three modes:
files (default): Save files to disk and return file paths
"env": {
"ELEVENLABS_API_KEY": "your-api-key",
"ELEVENLABS_MCP_OUTPUT_MODE": "files"
}
resources: Return files as MCP resources; binary data is base64-encoded, text is returned as UTF-8 text
"env": {
"ELEVENLABS_API_KEY": "your-api-key",
"ELEVENLABS_MCP_OUTPUT_MODE": "resources"
}
both: Save files to disk AND return as MCP resources
"env": {
"ELEVENLABS_API_KEY": "your-api-key",
"ELEVENLABS_MCP_OUTPUT_MODE": "both"
}
Resource Mode Benefits:
both mode, resources can be fetched later using the elevenlabs://filename URI patternUse Cases:
files: Traditional file-based workflows, local developmentresources: Cloud environments, MCP clients without file system accessboth: Maximum flexibility, caching, and resource sharing scenariosYou 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.
If you want to contribute or run from source:
git clone https://github.com/elevenlabs/elevenlabs-mcp
cd elevenlabs-mcp
uv venv
source .venv/bin/activate
uv pip install -e ".[dev]"
.env.example to .env and add your ElevenLabs API key:cp .env.example .env
# Edit .env and add your API key
./scripts/test.sh
# Or with options
./scripts/test.sh --verbose --fail-fast
Install the server in Claude Desktop: mcp install elevenlabs_mcp/server.py
Debug and test locally with MCP Inspector: mcp dev elevenlabs_mcp/server.py
Logs when running with Claude Desktop can be found at:
%APPDATA%\Claude\logs\mcp-server-elevenlabs.log~/Library/Logs/Claude/mcp-server-elevenlabs.logCertain 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.
If you encounter the error "MCP ElevenLabs: spawn uvx ENOENT", confirm its absolute path by running this command in your terminal:
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.
Steps
Troubleshooting
✓ Do
✗ Don't
💡 Pro Tips
Architecture
Model Context Protocol standardizes how AI hosts (Claude, Cursor) communicate with external tools and data sources through server implementations.
Protocols
Compatibility
✓ Use when
Use when you need Claude to access external data, execute actions, or integrate with tools. Best for extending AI capabilities beyond conversation.
✗ Avoid when
Avoid when native integrations exist (use official APIs directly), for real-time critical systems, or when security/compliance requires zero external dependencies.