Model Context Protocol (MCP) is an open standard that allows AI assistants like Claude to interact with external tools and data sources through a standardised interface. Rather than requiring developers to write custom API integration code for every service they want Claude or Cursor to access, MCP servers provide a pre-built interface that the AI tool can discover and use through natural language.
The official ElevenLabs MCP server is an abstraction over the ElevenLabs API that provides a large language model with the context and tools to access the full ElevenLabs AI audio platform. It acts as a developer-friendly local interface that forwards requests to ElevenLabs’ cloud APIs. Rather than writing Python or JavaScript code to call the ElevenLabs API directly, you prompt Claude or Cursor in natural language — ‘generate a speech file from this script in the Rachel voice’ — and the MCP server handles the API call, authentication, and response processing.
Installation and Setup
Prerequisites
Before installing the ElevenLabs MCP server, you need: Python 3.10 or later installed on your system; an ElevenLabs API key (available from elevenlabs.io → Developer → API Key); and a compatible MCP client — Claude Desktop, Cursor, Windsurf, or any other application that supports MCP server connections.
Installation via uvx (Recommended)
The simplest installation method uses uvx, the Python package runner. In your terminal: install uvx if not present with pip install uv, then run uvx elevenlabs-mcp to verify the package is accessible. No separate installation step is needed — uvx handles package resolution at runtime.
Configuration for Claude Desktop
Open your Claude Desktop configuration file. On macOS: ~/Library/Application Support/Claude/claude_desktop_config.json. On Windows: %APPDATA%/Claude/claude_desktop_config.json. Add the ElevenLabs MCP server to the mcpServers section: {“mcpServers”: {“elevenlabs”: {“command”: “uvx”, “args”: [“elevenlabs-mcp”], “env”: {“ELEVENLABS_API_KEY”: “your_api_key_here”}}}}. Restart Claude Desktop. The ElevenLabs tools will appear in Claude’s tool list immediately.
Configuration for Cursor
In Cursor, open Settings → MCP → Add Server. Select the command-based server type. Command: uvx. Arguments: elevenlabs-mcp. Environment variables: ELEVENLABS_API_KEY=your_api_key. Save and restart Cursor. The ElevenLabs MCP tools are now available in Cursor’s AI assistant.
Configuration for Windsurf
Windsurf supports MCP server configuration through its settings panel. Add the ElevenLabs server using the same command and environment variable configuration as Cursor. Windsurf’s integration with ElevenLabs MCP is specifically documented in the ElevenLabs official repository as a supported client.
Troubleshooting: spawn uvx ENOENT
If you encounter the error ‘MCP ElevenLabs: spawn uvx ENOENT’, the issue is that your MCP client cannot find the uvx executable from its launch context. Fix: run which uvx (macOS/Linux) or where uvx (Windows) in your terminal to get the absolute path, then replace ‘uvx’ in the configuration with the full absolute path (e.g., /usr/local/bin/uvx). On macOS: check logs at ~/Library/Logs/Claude/mcp-server-elevenlabs.log. On Windows: %APPDATA%/Claude/logs/mcp-server-elevenlabs.log.
Available Tools
| Tool Category | Tools Available | What You Can Do |
| Text-to-Speech | generate_speech, list_voices, play_audio | Generate speech from text with any voice, customise stability/speed/style |
| Speech-to-Text | transcribe_audio | Transcribe audio files with speaker diarization |
| Voice Management | clone_voice, design_voice, preview_voice, list_voices | Clone from audio files, design from text, preview and manage library |
| Audio Processing | generate_sound_effect, isolate_audio, transform_voice | SFX from text, isolate voice from audio, transform between voices |
| Voice Library | search_voice_library, add_voice, get_voice, delete_voice | Browse 10k+ library voices, add to workspace, manage collection |
| Conversational AI | create_agent, get_agent, update_agent, delete_agent | Build and manage voice agents with custom knowledge and behaviour |
| Outbound Calls | make_outbound_call, list_phone_numbers | Make AI voice calls via Twilio, manage phone number inventory |
| Account | get_user_info, get_api_usage | Monitor subscription, credit usage, account status |
Example Prompts and Workflows
Generate Speech
In Claude Desktop with the ElevenLabs MCP server active, prompt: ‘Generate a speech file from this text: [your script] using the Rachel voice at stability 0.7 and save it as output.mp3.’ Claude calls the ElevenLabs TTS API, applies the voice settings, and saves the file to your local machine — no code required.
Clone a Voice
‘Clone a voice from the audio file at /Users/me/recordings/sample.mp3 and name it My Voice Clone.’ The MCP server handles the Instant Voice Cloning API call and returns the new voice ID for immediate use in subsequent speech generation prompts.
Build a Voice Agent
‘Create an ElevenLabs conversational AI agent named Customer Support Bot with the Rachel voice, and give it this system prompt: [your prompt]. Configure it to handle customer service inquiries for a software product.’ The MCP server creates the agent configuration, sets the voice, and returns the agent ID and connection details for deployment.
Make an Outbound Call
‘Make an outbound call to +1-555-0100 using my customer support agent and deliver this opening message: [message].’ The MCP server triggers the outbound call via Twilio integration — ElevenLabs Conversational AI handles the conversation from that point using the configured agent.
Transcribe Audio
‘Transcribe the audio file at /recordings/meeting.mp3 with speaker diarization enabled and save the transcript to meeting_transcript.txt.’ The MCP server calls Scribe v2, returns the transcript with speaker labels, and saves the output file.
Related: For the complete ElevenLabs Scribe v2 transcription guide, see our ElevenLabs Scribe v2 guide 2026
ElevenLabs MCP vs Direct API: When to Use Each
| Dimension | ElevenLabs MCP Server | Direct API Integration |
| Setup time | Minutes — JSON config in client | Hours — write integration code, handle auth, error handling |
| Best for | Prototyping, exploration, AI-assisted workflows | Production applications, custom logic, automated pipelines |
| Authentication | API key in config file | API key in environment variables or secrets manager |
| Customisation | Limited to available MCP tools | Full API parameter control |
| Rate limiting | Subject to MCP client request handling | Full programmatic control over rate limiting strategy |
| Automation | Not suitable for scheduled/background tasks | Full automation support |
| Who uses it | Developers, creators, power users with Claude/Cursor | Developers building production applications |
The MCP server is ideal for interactive workflows where you want to use ElevenLabs capabilities alongside Claude’s reasoning — asking Claude to generate speech for a script it also wrote, or transcribing audio and then asking Claude to summarise the transcript. For production applications with scheduled jobs, high-volume processing, or custom business logic, the direct ElevenLabs API is more appropriate.
Three Insights Most ElevenLabs MCP Guides Miss
1. The MCP Server Enables Compound AI Workflows That Would Otherwise Require Custom Code
The most powerful use of the ElevenLabs MCP server is not using individual tools in isolation — it is combining them with Claude’s reasoning in compound workflows. Examples: ‘Read this article, summarise it for a podcast listener, then generate speech for the summary in the Adam voice’ — Claude reads, summarises, and generates audio in one prompt. ‘Transcribe this interview recording, identify the key quotes, and generate individual audio clips for each quote in a branded voice’ — one prompt, multiple API calls orchestrated automatically. These compound workflows would require custom orchestration code without MCP. With MCP, they are natural language prompts.
2. Custom MCP Servers Enable Internal Tool Integration Beyond the Official Server
The official ElevenLabs MCP server provides access to ElevenLabs’ own capabilities. The MCP protocol also supports custom servers — teams can build their own MCP servers that combine ElevenLabs tools with internal systems. A company’s content management system, customer database, or product information database can be exposed as MCP tools alongside ElevenLabs, enabling prompts like ‘generate audio narrations for all blog posts published this week from our CMS using our branded voice.’ This custom integration pattern is how 11.ai — ElevenLabs’ own voice assistant — connects to Slack, Notion, and other workflow tools.
3. Voice Timeout and Long-Running Operations Require Client-Side Awareness
Certain ElevenLabs API operations — particularly voice design and audio isolation — take longer to complete than standard MCP tool calls. When using the MCP inspector in development mode, timeout errors may appear despite the underlying operation completing successfully on ElevenLabs’ servers. In production MCP client usage (Claude Desktop, Cursor), this typically does not occur because client timeout handling is more generous. Be aware of this behaviour when developing and testing MCP workflows that use voice design or audio isolation tools — completed operations may appear to fail in dev mode while actually succeeding.
ElevenLabs MCP Server in 2027
The ElevenLabs MCP server will expand alongside both ElevenLabs’ product capabilities and the MCP protocol’s adoption. As ElevenLabs adds new products — video generation, ElevenMusic generation, ElevenFlows pipeline execution — these will be exposed through the MCP server. The MCP protocol itself is gaining adoption beyond Claude and Cursor: as more AI development tools adopt MCP as their standard integration layer, the ElevenLabs MCP server becomes accessible to a broader range of development environments without additional configuration work. And as enterprise teams begin building more sophisticated AI-powered workflows, the ElevenLabs MCP server’s role as a compound workflow enabler will become more significant.
Key Takeaways
- ElevenLabs MCP server connects Claude, Cursor, and Windsurf directly to the full ElevenLabs platform — TTS, STT, voice cloning, sound effects, agents, and outbound calls — via natural language prompts.
- Installation: add JSON config with your API key to your MCP client. Command: uvx elevenlabs-mcp. No additional setup required.
- 20+ tools available covering all ElevenLabs capabilities. Best for interactive AI-assisted workflows and compound multi-step audio production.
- MCP enables compound workflows: one prompt can generate speech for a script Claude also wrote — without any custom integration code.
- Use MCP for interactive exploration and prototyping. Use the direct API for production applications with automation, scheduling, and custom business logic.
Conclusion
The ElevenLabs MCP server is the fastest path to integrating ElevenLabs audio capabilities into an AI-assisted development workflow. For developers using Claude Desktop or Cursor who regularly need to generate speech, transcribe audio, or build voice agent configurations as part of their work, the five-minute MCP setup eliminates the context switching that previously required opening the ElevenLabs dashboard for every audio operation. The compound workflow potential — where Claude reasons, writes, and generates audio in a single prompt — is the highest-value capability and the clearest demonstration of why the MCP integration layer matters for AI-assisted content and development work.
Frequently Asked Questions
What is the ElevenLabs MCP server?
An official Model Context Protocol server that connects MCP-compatible AI tools (Claude Desktop, Cursor, Windsurf) to the full ElevenLabs audio platform — enabling TTS, STT, voice cloning, agent creation, and outbound calls through natural language prompts without custom API integration code.
How do I install the ElevenLabs MCP server?
Install uvx (pip install uv), then add the ElevenLabs server configuration to your MCP client’s config file with your API key. Command: uvx, Arguments: elevenlabs-mcp, Environment: ELEVENLABS_API_KEY=your_key. Restart your MCP client.
Does the ElevenLabs MCP server work with Claude?
Yes — it works with Claude Desktop specifically (not Claude.ai web). Configure it in claude_desktop_config.json. On macOS: ~/Library/Application Support/Claude/claude_desktop_config.json. On Windows: %APPDATA%/Claude/claude_desktop_config.json.
Can the ElevenLabs MCP server make phone calls?
Yes — the make_outbound_call tool triggers outbound AI voice calls via Twilio integration. You need a Twilio account and phone number configured in ElevenLabs, plus a conversational AI agent to handle the conversation.
Is the ElevenLabs MCP server free?
The MCP server itself is free and open-source. API calls made through it consume ElevenLabs credits at the standard rates for each operation — the same credits as making those calls directly through the dashboard or API.
Methodology
MCP server capabilities from ElevenLabs official MCP server repository at github.com/elevenlabs/elevenlabs-mcp. Tool list and configuration from official README documentation. Setup instructions from ElevenLabs blog post ‘Introducing ElevenLabs MCP’ (April 2025). Troubleshooting from official GitHub repository issues and documentation. Client compatibility from ElevenLabs official documentation and Glama.ai MCP server listing. This article was drafted with AI assistance and reviewed by the editorial team at ElevenLabsMagazine.com.
References
ElevenLabs. (2025). Introducing ElevenLabs MCP. https://elevenlabs.io/blog/introducing-elevenlabs-mcp
ElevenLabs. (2026). ElevenLabs MCP GitHub repository. https://github.com/elevenlabs/elevenlabs-mcp
Glama.ai. (2026). ElevenLabs MCP server listing. https://glama.ai/mcp/servers/@elevenlabs/elevenlabs-mcp
