Voice Recorder MCP Server
An MCP server for recording audio and transcribing it using OpenAI's Whisper model. Designed to work as a Goose custom extension or standalone MCP server.
Features
- Record audio from the default microphone
- Transcribe recordings using Whisper
- Integrates with Goose AI agent as a custom extension
- Includes prompts for common recording scenarios
Installation
# Install from source
git clone https://github.com/DefiBax/voice-recorder-mcp.git
cd voice-recorder-mcp
pip install -e .
Usage
As a Standalone MCP Server
# Run with default settings (base.en model)
voice-recorder-mcp
# Use a specific Whisper model
voice-recorder-mcp --model medium.en
# Adjust sample rate
voice-recorder-mcp --sample-rate 44100
Testing with MCP Inspector
The MCP Inspector provides an interactive interface to test your server:
# Install the MCP Inspector
npm install -g @modelcontextprotocol/inspector
# Run your server with the inspector
npx @modelcontextprotocol/inspector voice-recorder-mcp
With Goose AI Agent
-
Open Goose and go to Settings > Extensions > Add > Command Line Extension
-
Set the name to
voice-recorder -
In the Command field, enter the full path to the voice-recorder-mcp executable:
/full/path/to/voice-recorder-mcpOr for a specific model:
/full/path/to/voice-recorder-mcp --model medium.enTo find the path, run:
which voice-recorder-mcp -
No environment variables are needed for basic functionality
-
Start a conversation with Goose and introduce the recorder with: "I want you to take action from transcriptions returned by voice-recorder. For example, if I dictate a calculation like 1+1, please return the result."
Available Tools
start_recording: Start recording audio from the default microphonestop_and_transcribe: Stop recording and transcribe the audio to textrecord_and_transcribe: Record audio for a specified duration and transcribe it
Whisper Models
This extension supports various Whisper model sizes:
| Model | Speed | Accuracy | Memory Usage | Use Case |
|---|---|---|---|---|
tiny.en | Fastest | Lowest | Minimal | Testing, quick transcriptions |
base.en | Fast | Good | Low | Everyday use (default) |
small.en | Medium | Better | Moderate | Good balance |
medium.en | Slow | High | High | Important recordings |
large | Slowest | Highest | Very High | Critical transcriptions |
The .en suffix indicates models specialized for English, which are faster and more accurate for English content.
Requirements
- Python 3.12+
- An audio input device (microphone)
Configuration
You can configure the server using environment variables:
# Set Whisper model
export WHISPER_MODEL=small.en
# Set audio sample rate
export SAMPLE_RATE=44100
# Set maximum recording duration (seconds)
export MAX_DURATION=120
# Then run the server
voice-recorder-mcp
Troubleshooting
Common Issues
- No audio being recorded: Check your microphone permissions and settings
- Model download errors: Ensure you have a stable internet connection for the initial model download
- Integration with Goose: Make sure the command path is correct
- Audio quality issues: Try adjusting the sample rate (default: 16000)
Contributing
Contributions are welcome! Please feel free to submit a Pull Request.
- Fork the repository
- Create your feature branch (
git checkout -b feature/amazing-feature) - Commit your changes (
git commit -m 'Add some amazing feature') - Push to the branch (
git push origin feature/amazing-feature) - Open a Pull Request
License
This project is licensed under the MIT License - see the LICENSE file for details.