About Zonos TTS MCP for Linux
A Linux-focused MCP integration that lets Claude generate speech with Zonos TTS. It supports multiple languages and emotions, with GPU and logging adjustments for Linux use.
Key features
- 01Text-to-speech from Claude
- 02Multiple language support
- 03Neutral, happy, sad, and angry emotions
- 04PulseAudio and PipeWire playback
- 05GPU and logging optimizations
Use cases
- 01Have Claude speak generated text on Linux
- 02Generate speech with a selected language and emotion
MCP support
Exposes the `speak_response` tool to MCP clients, which sends text and optional language and emotion settings to Zonos TTS for speech generation.
Configuration
Local server config is the same for every client. Copy it into your client’s MCP config file.
Tools
Local servers expose tools only once running on your machine, and the maintainer has not provided a tool list.
Details
From the project READMEZonos TTS MCP for Linux
A Model Context Protocol integration for Zonos TTS, allowing Claude to generate speech directly on Linux systems.
Overview
This project enables text-to-speech capabilities for Claude AI using the Zonos TTS system. The integration uses the Model Context Protocol (MCP) to allow Claude to generate natural-sounding speech in multiple languages and emotions.
Key Features
- Text-to-speech through Claude
- Multiple emotions support (neutral, happy, sad, angry)
- Multi-language support
- Proper audio playback through PulseAudio/PipeWire
- Compatible with Linux environments
Requirements
- Node.js
- PulseAudio or PipeWire with PulseAudio compatibility layer
- Running instance of Zonos API (PhialsBasement/zonos-api)
- Working audio output device
- Claude Desktop app
Setup
Installing via Smithery
To install the original UNMODIFIED version of Zonos TTS Integration for Claude Desktop automatically via Smithery:
npx -y @smithery/cli install @PhialsBasement/zonos-tts-mcp --client claude
Manual installation for Linux with GPU optimizations
-
Make sure you have Zonos API running with the API implementation (PhialsBasement/zonos-api)
-
Clone this repository:
git clone https://github.com/angrysky56/Zonos-TTS-MCP-Linux.git
cd Zonos-TTS-MCP-Linux
- Install dependencies:
npm install @modelcontextprotocol/sdk axios zod
- Build the MCP server:
npm run build
- Add to Claude's config file:
Edit your Claude config file (search for files containing "mcpServers" in your home directory) and add this to the
mcpServerssection:
"zonos-tts-mcp": {
"command": "node",
"args": [
"/path/to/your/Zonos-TTS-MCP-Linux/dist/server.js"
]
}
Replace /path/to/your/Zonos-TTS-MCP-Linux with the actual path where you installed this project.
Troubleshooting
Error Messages in Console
If you encounter error messages like:
Unexpected token 'P', 'Playing au'... is not valid JSON
This project includes fixes for these common issues:
- Console output has been redirected to a log file (
/tmp/zonos-tts-mcp.log) instead of standard output to prevent JSON parsing errors. - The TTS parameters have been optimized for better performance.
Performance Optimization
This fork includes performance optimizations to reduce GPU load:
- Using more efficient sampling parameters (
top_pandmin_p) - Improved log handling to prevent interference with MCP communication
Using with Claude
Once configured, Claude can use the speak_response tool:
speak_response(
text="Your text here",
language="en-us", # optional, defaults to en-us
emotion="happy" # optional: "neutral", "happy", "sad", "angry"
)
Credit
This project is a modified version of PhialsBasement/zonos-tts-mcp, adapted for improved Linux compatibility and performance.
The Zonos TTS system is developed by Zyphra and is available through their Zonos API.
License
See the original project for license information.
Notes
- Make sure both the Zonos API server and this MCP server are running
- Audio playback requires proper PulseAudio/PipeWire configuration
- If you experience high GPU usage, you may want to adjust the TTS parameters in the server code
Comments
New to MCP?
From what the protocol is to installing your first server, these answers cover most first questions.
An open protocol that defines how AI applications connect to external tools and data in a uniform way. Clients talk to the model; MCP servers provide tools, resources and prompt templates. They exchange standard messages, so one server can be reused by many clients.