Advanced TTS MCP Server

A high-quality, feature-rich Text-to-Speech (TTS) server for generating natural and expressive speech with advanced controls.

A high-quality, feature-rich Text-to-Speech MCP server with native TypeScript implementation. Designed for professional applications requiring natural, expressive speech synthesis with advanced controls and zero external dependencies.

- 10 High-Quality Voices- Male and female voices with distinct personalities
- Emotion Control- Neutral, happy, excited, calm, serious, casual, confident
- Dynamic Pacing- Natural, conversational, presentation, tutorial, narrative modes
- Speed & Volume- Precise control from 0.25x to 3.0x speed, 0.1x to 2.0x volume

- Streaming Audio- Real-time synthesis and playback
- Batch Processing- Handle multiple text segments efficiently
- Multiple Formats- WAV, MP3, FLAC, OGG output support
- Natural Speech Enhancement- Automatic pause insertion and emotion markers
- Queue Management- Handle multiple concurrent requests

- 6 Powerful Tools- Complete synthesis, batch processing, voice management
- 2 Rich Resources- Voice capabilities and usage examples
- Real-time Status- Track processing progress and manage requests
- File Management- Save, list, and organize audio outputs

Option 1: Deploy to Smithery.ai (Recommended)

🎯 One-Click Deployment to Smithery Platform
- Deploy Now: VisitSmithery.aiand import this repository
- Configure: Set your preferred voice and speech settings
- Use Instantly: Access via Claude Desktop or any MCP-compatible client

git clone https://github.com/samihalawa/advanced-tts-mcp.git cd advanced-tts-mcp

{ "mcpServers": { "advanced-tts": { "command": "node", "args": ["dist/index.js"], "cwd": "/path/to/advanced-tts-mcp" } } }

# Build TypeScript npm run build # Start server npm start

Restart Claude Desktop and start synthesizing with natural, expressive voices.

# Simple text-to-speech await synthesize_speech( text="Hello! Welcome to Advanced TTS.", voice_id="af_heart" )

# Excited announcement await synthesize_speech( text="This is amazing news! You're going to love this new feature!", voice_id="af_heart", emotion="excited", pacing="conversational", speed=1.1 )

# Tutorial narration await synthesize_speech( text="Step one: Open your browser. Step two: Navigate to the website.", voice_id="am_adam", emotion="calm", pacing="tutorial", speed=0.9 )

# Multiple segments with pauses await batch_synthesize( segments=[ "Welcome to our presentation.", "Today we'll cover three main topics.", "Let's begin with the first topic." ], voice_id="af_sarah", emotion="confident", pacing="presentation", merge_output=True, segment_pause=1.0, save_file=True )

Convert text to natural speech with full control over voice characteristics.

- text- Text to synthesize (max 10,000 chars)
- voice_id- Voice selection (see table above)
- speed- Speech rate (0.25-3.0)
- emotion- Voice emotion (neutral, happy, excited, calm, serious, casual, confident)
- pacing- Speech style (natural, conversational, presentation, tutorial, narrative, fast, slow)
- volume- Audio volume (0.1-2.0)
- output_format- File format (wav, mp3, flac, ogg)
- save_file- Save to file (boolean)
- filename- Custom filename

Process multiple text segments efficiently with optional merging.

- segments- List of text segments
- merge_output- Combine into single file
- segment_pause- Pause between segments (0.0-5.0s)
- All synthesis parameters from above

Retrieve complete voice information and capabilities.

Check processing status for synthesis requests.

- Neutral- Standard, professional tone
- Happy- Upbeat, cheerful expression
- Excited- Enthusiastic, energetic delivery
- Calm- Relaxed, soothing tone
- Serious- Formal, authoritative delivery
- Casual- Relaxed, conversational style
- Confident- Assured, professional tone

- Natural- Balanced, human-like rhythm
- Conversational- Casual discussion pace
- Presentation- Professional speaking rhythm
- Tutorial- Educational, clear delivery
- Narrative- Storytelling pace
- Fast- Quick delivery (1.2x base speed)
- Slow- Deliberate delivery (0.8x base speed)

# Model paths (optional) KOKORO_MODEL_PATH=./kokoro-v1.0.onnx KOKORO_VOICES_PATH=./voices-v1.0.bin # Output settings TTS_OUTPUT_DIR=./audio_output TTS_MAX_QUEUE_SIZE=100 # Audio settings TTS_DEFAULT_VOICE=af_heart TTS_ENABLE_STREAMING=true

config = ServerConfig( model_path="./kokoro-v1.0.onnx", voices_path="./voices-v1.0.bin", output_dir="./audio_output", max_queue_size=100, enable_streaming=True, default_voice="af_heart" )

├── src/advanced_tts/ │ ├── __init__.py # Package initialization │ ├── server.py # MCP server implementation │ ├── engine.py # Kokoro TTS engine wrapper │ ├── models.py # Data models and validation │ └── utils.py # Utility functions ├── pyproject.toml # Project configuration ├── README.md # Documentation └── LICENSE # MIT License

Contributions welcome! Areas for improvement:

- Additional voice models
- Real-time streaming synthesis
- Advanced audio effects
- Multi-language support
- Performance optimizations

- Kokoro TTS- High-quality neural voice synthesis
- MCP Protocol- Seamless AI model integration
- FastMCP- Efficient server framework

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