Piper TTS MCP Server
Provides a Docker configuration for easily setting up and running the Piper TTS service, with pre-configured voice models and automatic startup.
Click on "Deploy Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type
@followed by the MCP server name and your instructions, e.g., "@Piper TTS MCP Server read today's news headlines with volume at 0.3"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
🎤 Piper TTS MCP Server
A Model Context Protocol (MCP) server that integrates Piper TTS for high-quality text-to-speech functionality. This server provides a speak tool that converts text to speech and plays it directly through your speakers with customizable volume control.
✨ Features
🔊 High-quality text-to-speech using Piper TTS
🎚️ Volume control (0.01 to 1.00)
⚡ Memory-based audio playback (no temporary files)
🔧 Customizable voice parameters (speaker, speed, voice variation)
🚀 Fast and lightweight MCP integration
🔇 Silent operation (no console output)
Related MCP server: Kokoro TTS MCP Server
📋 Requirements
⚠️ Important Prerequisites:
Python 3.12+ installed on your system
Piper TTS service running on
localhost:5000
You can set this up using the included Docker configuration or by running Piper TTS separately.
🚀 Quick Start
1. 🐳 Start Piper TTS Service
Clone this repo and start the TTS service:
git clone https://github.com/CryptoDappDev/piper-tts-mcp.git
cd piper-tts-mcpOption A: Using Docker Compose (Recommended)
docker compose up -dOption B: Using Docker Build
docker build -t piper-tts-mcp .
docker run -p 5000:5000 piper-tts-mcp2. 🔧 Configure MCP Client
Add the server to your MCP configuration (e.g., .mcp.json for Claude Desktop):
{
"mcpServers": {
"speak": {
"command": "uv",
"args": [
"--directory",
"/path/to/your/piper-tts-mcp",
"run",
"server.py"
]
}
}
}3. 🎉 Enjoy!
The speak tool is now available in your MCP client!
🛠️ Usage
The MCP server provides a speak tool with the following parameters:
Parameter | Type | Default | Description |
| string | required | Text to convert to speech |
| int | 0 | Voice speaker ID |
| float | 1.1 | Speech speed (lower = faster) |
| float | 0.667 | Voice variation control |
| float | 0.333 | Pronunciation variation |
| float | 0.15 | Volume level (0.01 to 1.00) |
Example Usage
# Basic usage
speak("Hello, world!")
# With custom volume
speak("This is louder!", volume=0.5)
# With custom voice settings
speak("Fast and varied speech",
length_scale=0.8,
volume=0.3,
noise_scale=0.8)🔧 Development
Dependencies
Python 3.12+
mcp[cli]- MCP frameworkrequests- HTTP client for TTS APIpygame- Audio playback
Local Development
# Install dependencies
uv sync
# Run the server
uv run server.py📦 Docker Configuration
The included Docker setup provides:
Piper TTS service on port 5000
Pre-configured voice models
Automatic startup
🎙️ Voice Models
The default voice model used in this repository is en_GB-cori-high (British English, female voice).
🔍 Exploring Voice Options
Voice Samples: Listen to different voice models at Piper Voice Samples
Piper Documentation: Learn more about Piper TTS at rhasspy/piper
🛠️ Changing Voice Models
To use a different voice model:
Choose a voice from the voice samples page
Update the Dockerfile - Replace
en_GB-cori-highwith your chosen voice model:# Download your preferred voice model RUN python3 -m piper.download_voices your-chosen-voice-model # Update the server command CMD ["sh", "-c", "python3 -m piper.http_server -m your-chosen-voice-model"]Rebuild the Docker image:
docker build -t piper-tts-mcp . docker compose up -d
🎨 Creating Custom Voices
Creating your own custom voice requires additional effort and research. Please refer to the Piper documentation for guidance on voice training and customization.
🤝 Contributing
Contributions are welcome! Please feel free to submit issues and pull requests.
📄 License
This project is open source and available under the MIT License.
🙏 Acknowledgments
Piper TTS - Fast, local neural text to speech
Model Context Protocol - Standardized protocol for AI model interactions
Made with ❤️ for the MCP community
Available Tools
1 toolspeakA
Convert text to speech and play it through the speakers.
Args:
text: The text to convert to speech
speaker_id: Voice speaker ID (default: 0)
length_scale: Speech speed control (default: 1.1, lower = faster)
noise_scale: Voice variation control (default: 0.667)
noise_w_scale: Pronunciation variation control (default: 0.333)
volume: Volume level from 0.01 to 1.00 (default: 0.15)
Returns:
Success or error message
Inputs: text (required), speaker_id, length_scale, noise_scale, +2 more. Returns: result
Tool Schema Changelog
Recent tool additions, removals, and schema changes observed during successful MCP inspections.
Loading tool schema changelog…
TDQS
Scored across 1 tool
With only one tool, there is no possibility of confusion or overlap between tools. The single 'speak' tool has a clearly defined and distinct purpose of converting text to speech.
The single tool name 'speak' follows a clear verb-based pattern that directly describes its function. With only one tool, consistency is inherently perfect as there are no other tools to compare against.
A single tool feels thin for a TTS server that could reasonably support additional functionality like listing available voices, checking synthesis status, or managing audio output. While the core functionality is present, the tool surface is minimal.
The server provides basic text-to-speech conversion but lacks complementary tools that would create a complete TTS workflow. There are no tools for voice management, synthesis monitoring, or audio file handling, leaving significant gaps in the domain coverage.
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