mcp-coqui-tts
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., "@mcp-coqui-tts Clone voice from reference.wav saying 'Welcome to the demo.'"
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.
MCP Coqui TTS Server
A Model Context Protocol (MCP) server that provides text-to-speech synthesis capabilities using Coqui TTS, including voice cloning support.
Features
Text-to-Speech Synthesis: Convert text to natural-sounding speech
Multiple Models: Support for various TTS models and languages
Voice Cloning: Clone voices from audio samples using XTTS models
Long Text Support: Automatic chunking for longer texts
Customizable Output: Control speed, speaker, and language settings
Related MCP server: Kokoro MCP Server
Prerequisites
Before using this MCP server, you need to install Coqui TTS:
pip install TTSFor voice cloning and concatenation features, you'll also need ffmpeg:
# macOS
brew install ffmpeg
# Ubuntu/Debian
sudo apt-get install ffmpeg
# Windows (using chocolatey)
choco install ffmpegInstallation
From npm
npm install -g @s.lfr/mcp-coqui-ttsFrom Source
git clone https://github.com/yourusername/mcp-coqui-tts.git
cd mcp-coqui-tts
npm install
npm linkUsage
With Claude Desktop
Add to your Claude Desktop configuration (~/Library/Application Support/Claude/claude_desktop_config.json on macOS):
{
"mcpServers": {
"coqui-tts": {
"command": "npx",
"args": ["@s.lfr/mcp-coqui-tts"]
}
}
}Or if installed from source:
{
"mcpServers": {
"coqui-tts": {
"command": "node",
"args": ["/path/to/mcp-coqui-tts/index.js"]
}
}
}Available Tools
1. speak
Convert text to speech with customizable parameters.
Parameters:
text(required): The text to convert to speechmodel: TTS model to use (default: "tts_models/en/ljspeech/tacotron2-DDC")output_path: Where to save the audio filespeaker_idx: Speaker index for multi-speaker modelslanguage_idx: Language index for multi-language modelsspeed: Speed factor (1.0 is normal speed)
Example:
{
"text": "Hello, this is a test of the text to speech system.",
"model": "tts_models/en/ljspeech/tacotron2-DDC",
"output_path": "/tmp/output.wav",
"speed": 1.2
}2. list_models
List all available TTS models.
Parameters: None
3. synthesize_long_text
Synthesize longer texts with automatic chunking and concatenation.
Parameters:
text(required): The long text to convertmodel: TTS model to useoutput_path: Where to save the final audiochunk_size: Maximum characters per chunk (default: 500)
Example:
{
"text": "This is a very long text that will be automatically split into chunks...",
"output_path": "/tmp/long_speech.wav",
"chunk_size": 500
}4. clone_voice
Clone a voice from an audio sample (requires XTTS model).
Parameters:
text(required): Text to speak in the cloned voicereference_audio(required): Path to reference audio for voice cloningoutput_path: Where to save the outputlanguage: Language code (default: "en")
Example:
{
"text": "This will be spoken in the cloned voice.",
"reference_audio": "/path/to/sample.wav",
"output_path": "/tmp/cloned_voice.wav",
"language": "en"
}Popular TTS Models
English Models
tts_models/en/ljspeech/tacotron2-DDC- High quality English TTStts_models/en/ljspeech/fast_pitch- Fast English TTStts_models/en/vctk/vits- Multi-speaker English (110 speakers)
Multilingual Models
tts_models/multilingual/multi-dataset/xtts_v2- Supports voice cloningtts_models/multilingual/multi-dataset/your_tts- Multilingual with voice cloning
Other Languages
Run list_models to see all available models for different languages.
Deployment on Smithery
To deploy this MCP server on Smithery:
Fork this repository
Connect your GitHub account to Smithery
Create a new MCP server on Smithery
Select this repository
Deploy
The server will be automatically available for use with any MCP-compatible client.
Development
Running Locally
npm startTesting
You can test the server using the MCP inspector:
npx @modelcontextprotocol/inspector node index.jsTroubleshooting
Common Issues
"tts: command not found"
Make sure Coqui TTS is installed:
pip install TTSEnsure Python/pip binaries are in your PATH
"ffmpeg: command not found"
Install ffmpeg for your operating system (see Prerequisites)
Model download fails
First run may take time as models are downloaded
Check internet connection
Ensure sufficient disk space (~1-5GB per model)
Voice cloning not working
Requires XTTS v2 model
Reference audio should be clear, 5-10 seconds long
WAV format recommended for reference audio
Contributing
Contributions are welcome! Please feel free to submit a Pull Request.
License
MIT
Acknowledgments
Coqui TTS for the excellent TTS library
Model Context Protocol for the MCP SDK
Support
For issues and questions, please open an issue on GitHub.
Available Tools
4 toolsclone_voiceB
Clone a voice from an audio sample (requires XTTS model)
Inputs: text (required), reference_audio (required), language, output_path.
list_modelsA
List available TTS models
Inputs: no named parameters.
speakC
Convert text to speech using Coqui TTS
Inputs: text (required), model, speed, output_path, +2 more.
synthesize_long_textB
Synthesize longer text with automatic chunking
Inputs: text (required), model, chunk_size, output_path.
Tool Schema Changelog
Recent tool additions, removals, and schema changes observed during successful MCP inspections.
Loading tool schema changelog…
TDQS
Scored across 4 tools
speak and synthesize_long_text both perform text-to-speech, but synthesize_long_text is explicitly for longer text with automatic chunking, which sets it apart. clone_voice and list_models are clearly distinct, so overall only minor overlap exists.
Most tools use a verb_noun pattern (list_models, synthesize_long_text, clone_voice), but 'speak' is a bare verb without an object, creating a slight inconsistency. The pattern is still mostly predictable away from this.
With four tools, the server is well-scoped for its TTS purpose: listing models, synthesizing short and long text, and cloning voices. Each tool earns its place, and the count feels neither too thin nor too heavy.
The tool surface covers the core TTS workflows: model discovery, standard and long-form synthesis, and voice cloning. Minor gaps like audio streaming or detailed model management are not essential for a basic TTS server, so the coverage is solid.
Maintenance
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