ElevenLabs Scribe MCP Server: Optimize Performance, Scale Seamlessly

What is ElevenLabs Scribe MCP Server: Optimize Performance, Scale Seamlessly?

Imagine a transcription powerhouse that doesn’t just transcribe audio—it thinks while it works. The Scribe MCP Server is the Swiss Army knife of real-time speech-to-text systems, engineered for professionals who demand precision and scalability. Built on ElevenLabs’ industry-leading Scribe API, this open-source server uses the Model Control Protocol (MCP) to keep complex conversations on track, automatically handling everything from audio streams to file conversions. Think of it as your AI scribe that remembers context like a seasoned note-taker and adapts seamlessly whether you’re crunching through a 20-hour podcast backlog or live-streaming a conference call.

How to Use ElevenLabs Scribe MCP Server: Optimize Performance, Scale Seamlessly?

Getting started is as smooth as a well-caffeinated workflow:

1. Boot up: Clone the repo and install dependencies like a seasoned dev—no magic incantations required
2. Plug in your API key: Let the Scribe know who’s boss by configuring your ElevenLabs credentials
3. Stream or drop files: Hook up audio streams via WebSocket or toss files into the processing queue like they're overdue bills
4. Watch the magic: Transcripts flow in real-time with context-aware continuity—no dropped threads, ever

Need to scale? Just spin up more instances—this thing grows like a tech startup during a funding round.

Key Features of ElevenLabs Scribe MCP Server: Optimize Performance, Scale Seamlessly?

• MCP Context Mastery: Maintains conversational coherence across hours of audio, like a human assistant who actually remembers what was said last Tuesday
• Format Juggler: Automatically converts 20+ audio formats on the fly—no more "unsupported codec" headaches
• Real-Time Resilience: Gracefully handles network hiccups with built-in retry logic—your podcast interview won’t turn into a disaster reel
• Scalability with a Side of Simplicity: Horizontal scaling via Docker containers means you can handle 10 streams or 1000 without rewriting code
• Debugging Superpowers: Built-in analytics dashboard shows transcription confidence scores, latency metrics, and even identifies speaker patterns

Use Cases of ElevenLabs Scribe MCP Server: Optimize Performance, Scale Seamlessly?

This isn’t just another transcription tool—it’s a problem solver for real-world headaches:

• Enterprise Zoom Calls: Turn 50+ attendee conference chaos into searchable, context-aware transcripts that actually make sense
• Podcast Production: Automate show notes and chapter markers with timestamp precision, leaving more time for ad-read prep
• Customer Support: Power live chatbots with real-time transcription that remembers previous interactions, reducing "repeat the last thing you said" loops
• Field Research: Process hundreds of interview recordings in native formats without manual pre-processing—say goodbye to late-night codec wars

FAQ from ElevenLabs Scribe MCP Server: Optimize Performance, Scale Seamlessly?

• Q: Will this work with my obscure audio device?
  A: Probably. The format converter handles everything from AMR-NB ringtones to 24-bit studio recordings—test it, you’ll be surprised
• Q: Can I customize the context window?
  A: Absolutely. The MCP API lets you set memory depth from "short-term memory" (5 mins) to "encyclopedic recall" (entire session)
• Q: What happens if the API goes offline?
  A: Transcripts queue up locally and auto-reprocess when the connection returns—no data loss, even in the cloud’s stormiest days
• Q: Is this GDPR-compliant?
  A: The server never sends raw audio to ElevenLabs by default—transcripts stay local unless you explicitly route them

ElevenLabs Scribe MCP Server

A Model Control Protocol (MCP) server implementation for ElevenLabs' Scribe speech-to-text API, providing real-time transcription capabilities with advanced context management and bidirectional streaming.

Features

• Real-time Transcription : Stream audio directly from your microphone and get instant transcriptions
• File-based Transcription : Upload audio files for batch processing
• MCP Protocol Support : Full implementation of the Model Control Protocol for better context management
• WebSocket Support : Real-time bidirectional communication
• Context Management : Maintain conversation context for improved transcription accuracy
• Multiple Audio Formats : Support for various audio formats with automatic conversion
• Language Detection : Automatic language detection and confidence scoring
• Event Detection : Identify speech and non-speech audio events

Installation

1. Clone the repository:

git clone https://github.com/aromanstatue/MCP-Elevenlab-Scribe-ASR.git
cd MCP-Elevenlab-Scribe-ASR

2. Create and activate a virtual environment:

python -m venv venv
source venv/bin/activate  # On Windows: venv\Scripts\activate

3. Install dependencies:

pip install -e .

4. Create a .env file with your ElevenLabs API key:

ELEVENLABS_API_KEY=your-api-key-here

Usage

Starting the Server

python -m elevenlabs_scribe_mcp_server.main

The server will start on port 8000 by default (or the next available port).

Using the Example Client

1. File Transcription:

python examples/client_example.py --file path/to/audio.wav

2. Microphone Transcription:

python examples/client_example.py --mic

API Endpoints

1. REST API:

• POST /transcribe: Upload an audio file for transcription
• GET /health: Health check endpoint

2. WebSocket API:

• ws://localhost:8000/ws/transcribe: Real-time audio transcription

MCP Protocol

The server implements the Model Control Protocol (MCP) with the following message types:

1. INIT: Initialize a new transcription session
2. START: Begin audio streaming
3. AUDIO: Send audio data
4. TRANSCRIPTION: Receive transcription results
5. ERROR: Error messages
6. STOP: End audio streaming
7. DONE: Complete session

Development

Running Tests

pytest tests/

Project Structure

elevenlabs-scribe-mcp-server/
├── elevenlabs_scribe_mcp_server/
│   ├── __init__.py
│   ├── main.py              # FastAPI server
│   └── mcp/
│       ├── __init__.py
│       ├── protocol.py      # MCP protocol handler
│       ├── types.py         # Protocol types
│       └── elevenlabs.py    # ElevenLabs implementation
├── examples/
│   └── client_example.py    # Example client
├── tests/
│   └── test_transcribe.py   # Test suite
├── pyproject.toml           # Project metadata
└── README.md

Requirements

• Python 3.8+
• FastAPI
• Uvicorn
• PyAudio (for microphone support)
• aiohttp
• python-dotenv
• pydantic

Contributing

1. Fork the repository
2. Create your feature branch (git checkout -b feature/amazing-feature)
3. Commit your changes (git commit -m 'Add amazing feature')
4. Push to the branch (git push origin feature/amazing-feature)
5. Open a Pull Request

License

MIT License - see LICENSE file for details.

Acknowledgments

• ElevenLabs for their excellent Scribe API
• FastAPI for the modern web framework
• The Python community for the amazing tools and libraries