Fish Audio MCP Server
Enables natural language-driven speech synthesis using Fish Audio's Text-to-Speech API, supporting multiple voices, streaming, and flexible configuration.
README
Fish Audio MCP Server
<div align="center"> <img src="./dcos/icon_fish-audio.webp" alt="Fish Audio Logo" width="300" height="300" /> </div>
An MCP (Model Context Protocol) server that provides seamless integration between Fish Audio's Text-to-Speech API and LLMs like Claude, enabling natural language-driven speech synthesis.
What is Fish Audio?
Fish Audio is a cutting-edge Text-to-Speech platform that offers:
- ๐ State-of-the-art voice synthesis with natural-sounding output
- ๐ฏ Voice cloning capabilities to create custom voice models
- ๐ Multilingual support including English, Japanese, Chinese, and more
- โก Low-latency streaming for real-time applications
- ๐จ Fine-grained control over speech prosody and emotions
This MCP server brings Fish Audio's powerful capabilities directly to your LLM workflows.
Features
- ๐๏ธ High-Quality TTS: Leverage Fish Audio's state-of-the-art TTS models
- ๐ Streaming Support: Real-time audio streaming for low-latency applications
- ๐จ Multiple Voices: Support for custom voice models via reference IDs
- ๐ฏ Smart Voice Selection: Select voices by ID, name, or tags
- ๐ Voice Library Management: Configure and manage multiple voice references
- ๐ง Flexible Configuration: Environment variable-based configuration
- ๐ฆ Multiple Audio Formats: Support for MP3, WAV, PCM, and Opus
- ๐ Easy Integration: Simple setup with any MCP-compatible client
Quick Start
Installation
You can run this MCP server directly using npx:
npx @zhoujinandrew/fish-audio-mcp-server
Or install it globally:
npm install -g @zhoujinandrew/fish-audio-mcp-server
Configuration
-
Get your Fish Audio API key from Fish Audio
-
Set up environment variables:
export FISH_API_KEY=your_fish_audio_api_key_here
- Add to your MCP settings configuration:
Single Voice Mode (Simple)
{
"mcpServers": {
"fish-audio": {
"command": "npx",
"args": ["-y", "@zhoujinandrew/fish-audio-mcp-server"],
"env": {
"FISH_API_KEY": "your_fish_audio_api_key_here",
"FISH_MODEL_ID": "s2-pro",
"FISH_REFERENCE_ID": "your_voice_reference_id_here",
"FISH_OUTPUT_FORMAT": "mp3",
"FISH_STREAMING": "false",
"FISH_LATENCY": "balanced",
"FISH_MP3_BITRATE": "128",
"FISH_AUTO_PLAY": "false",
"AUDIO_OUTPUT_DIR": "~/.fish-audio-mcp/audio_output"
}
}
}
}
Multiple Voice Mode (Advanced)
{
"mcpServers": {
"fish-audio": {
"command": "npx",
"args": ["-y", "@zhoujinandrew/fish-audio-mcp-server"],
"env": {
"FISH_API_KEY": "your_fish_audio_api_key_here",
"FISH_MODEL_ID": "s2-pro",
"FISH_REFERENCES": "[{'reference_id':'id1','name':'Alice','tags':['female','english']},{'reference_id':'id2','name':'Bob','tags':['male','japanese']},{'reference_id':'id3','name':'Carol','tags':['female','japanese','anime']}]",
"FISH_DEFAULT_REFERENCE": "id1",
"FISH_OUTPUT_FORMAT": "mp3",
"FISH_STREAMING": "false",
"FISH_LATENCY": "balanced",
"FISH_MP3_BITRATE": "128",
"FISH_AUTO_PLAY": "false",
"AUDIO_OUTPUT_DIR": "~/.fish-audio-mcp/audio_output"
}
}
}
}
Environment Variables
| Variable | Description | Default | Required |
|---|---|---|---|
FISH_API_KEY |
Your Fish Audio API key | - | Yes |
FISH_MODEL_ID |
TTS model to use (s2-pro, s1) |
s2-pro |
Optional |
FISH_REFERENCE_ID |
Default voice reference ID (single reference mode) | - | Optional |
FISH_REFERENCES |
Multiple voice references (see below) | - | Optional |
FISH_DEFAULT_REFERENCE |
Default reference ID when using multiple references | - | Optional |
FISH_OUTPUT_FORMAT |
Default audio format (mp3, wav, pcm, opus) | mp3 |
Optional |
FISH_STREAMING |
Enable streaming mode (HTTP/WebSocket) | false |
Optional |
FISH_LATENCY |
Latency mode (low, balanced, normal) |
balanced |
Optional |
FISH_MP3_BITRATE |
MP3 bitrate (64, 128, 192) | 128 |
Optional |
FISH_AUTO_PLAY |
Auto-play audio and enable real-time playback | false |
Optional |
AUDIO_OUTPUT_DIR |
Directory for audio file output | ~/.fish-audio-mcp/audio_output |
Optional |
Configuring Multiple Voice References
You can configure multiple voice references in two ways:
JSON Array Format (Recommended)
Use the FISH_REFERENCES environment variable with a JSON array:
FISH_REFERENCES='[
{"reference_id":"id1","name":"Alice","tags":["female","english"]},
{"reference_id":"id2","name":"Bob","tags":["male","japanese"]},
{"reference_id":"id3","name":"Carol","tags":["female","japanese","anime"]}
]'
FISH_DEFAULT_REFERENCE="id1"
Individual Format (Backward Compatibility)
Use numbered environment variables:
FISH_REFERENCE_1_ID=id1
FISH_REFERENCE_1_NAME=Alice
FISH_REFERENCE_1_TAGS=female,english
FISH_REFERENCE_2_ID=id2
FISH_REFERENCE_2_NAME=Bob
FISH_REFERENCE_2_TAGS=male,japanese
Usage
Once configured, the Fish Audio MCP server provides two tools to LLMs.
Tool 1: fish_audio_tts
Generates speech from text using Fish Audio's TTS API.
Parameters
text(required): Text to convert to speech (max 10,000 characters)reference_id(optional): Voice model reference IDreference_name(optional): Select voice by namereference_tag(optional): Select voice by tagspeakers(optional, s2-pro only): Ordered list of speaker identifiers for multi-speaker dialogue. Each entry is resolved againstFISH_REFERENCESby id โ name โ tag (or used as a raw reference_id when no references are configured). The index maps to<|speaker:N|>tags intext. See the multi-speaker example below.streaming(optional): Enable streaming modeformat(optional): Output format (mp3, wav, pcm, opus)mp3_bitrate(optional): MP3 bitrate (64, 128, 192)opus_bitrate(optional): Opus bitrate in bps (-1000for auto,24000,32000,48000,64000)sample_rate(optional): Audio sample rate in Hz (defaults to format-native rate)normalize(optional): Enable text normalization (default: true)latency(optional): Latency mode (low,balanced,normal)output_path(optional): Custom output file pathauto_play(optional): Automatically play the generated audiowebsocket_streaming(optional): Use WebSocket streaming instead of HTTPrealtime_play(optional): Play audio in real-time during WebSocket streamingspeed(optional): Speaking rate multiplier (0.5=half speed, 1.0=normal, 2.0=double speed)volume(optional): Volume adjustment in dB (0=no change, positive=louder, negative=quieter)normalize_loudness(optional): Normalize perceived loudness (s2-pro only, default: true)temperature(optional): Expressiveness/emotion control (0=consistent, 1=emotional, default: 0.7)top_p(optional): Nucleus sampling diversity (0..1, default: 0.7)chunk_length(optional): Target text segment size (100-300, default: 300)max_new_tokens(optional): Max audio tokens per text chunk (default: 1024)repetition_penalty(optional): Penalty for repeating audio patterns (default: 1.2)min_chunk_length(optional): Min characters before splitting a chunk (0-100, default: 50)condition_on_previous_chunks(optional): Use prior audio as context for voice consistency (default: true)early_stop_threshold(optional): Early-stop threshold for batch processing (0..1, default: 1)
Voice Selection Priority: reference_id > reference_name > reference_tag > default
Tool 2: fish_audio_list_references
Lists all configured voice references.
Parameters
No parameters required.
Returns
- List of configured voice references with their IDs, names, and tags
- Default reference ID
Examples
Basic Text-to-Speech
User: "Generate speech saying 'Hello, world! Welcome to Fish Audio TTS.'"
Claude: I'll generate speech for that text using Fish Audio TTS.
[Uses fish_audio_tts tool with text parameter]
Result: Audio file saved to ./audio_output/tts_2025-01-03T10-30-00.mp3
Using Custom Voice by ID
User: "Generate speech with voice model xyz123 saying 'This is a custom voice test'"
Claude: I'll generate speech using the specified voice model.
[Uses fish_audio_tts tool with text and reference_id parameters]
Result: Audio generated with custom voice model xyz123
Using Voice by Name
User: "Use Alice's voice to say 'Hello from Alice'"
Claude: I'll generate speech using Alice's voice.
[Uses fish_audio_tts tool with reference_name: "Alice"]
Result: Audio generated with Alice's voice
Using Voice by Tag
User: "Generate Japanese speech saying 'ใใใซใกใฏ' with an anime voice"
Claude: I'll generate Japanese speech with an anime-style voice.
[Uses fish_audio_tts tool with reference_tag: "anime"]
Result: Audio generated with anime voice style
Multi-Speaker Dialogue (s2-pro only)
Multi-speaker synthesis lets a single TTS call produce a dialogue between two or more configured voices. Two requirements:
-
FISH_MODEL_ID=s2-pro(the default since 0.8.0). -
Configure the voices you want to use through
FISH_REFERENCES, for example:FISH_REFERENCES='[ {"reference_id":"id1","name":"Alice","tags":["female","english"]}, {"reference_id":"id2","name":"Bob","tags":["male","japanese"]}, {"reference_id":"id3","name":"Carol","tags":["female","japanese","anime"]} ]' FISH_DEFAULT_REFERENCE="id1"
Then call the tool with the speakers array and embed <|speaker:N|> tags in
your text. The N index lines up with the position in speakers:
User: "Have Alice and Bob greet each other."
Claude: I'll synthesize a two-speaker dialogue using s2-pro.
[Uses fish_audio_tts with:
text: "<|speaker:0|>Good morning, Bob!<|speaker:1|>Morning, Alice โ how are you?<|speaker:0|>Doing great, thanks!",
speakers: ["Alice", "Bob"]
]
Result: Single audio file alternating between Alice's and Bob's voices.
Notes:
- Each entry in
speakersis resolved by id โ name โ tag againstFISH_REFERENCES. You can also pass raw reference IDs directly (speakers: ["id1", "id2"]). - If you only pass one identifier, it behaves like
reference_idโ no multi-speaker mode engaged. - Using
speakerson a non-s2-promodel returns an error; switch the model viaFISH_MODEL_ID=s2-pro.
List Available Voices
User: "What voices are available?"
Claude: I'll list all configured voice references.
[Uses fish_audio_list_references tool]
Result:
- Alice (id: id1) - Tags: female, english [Default]
- Bob (id: id2) - Tags: male, japanese
- Carol (id: id3) - Tags: female, japanese, anime
HTTP Streaming Mode
User: "Generate a long speech in streaming mode about the benefits of AI"
Claude: I'll generate the speech in streaming mode for faster response.
[Uses fish_audio_tts tool with streaming: true]
Result: Streaming audio saved to ./audio_output/tts_2025-01-03T10-35-00.mp3
WebSocket Real-time Streaming
User: "Stream and play in real-time: 'Welcome to the future of AI'"
Claude: I'll stream the speech via WebSocket and play it in real-time.
[Uses fish_audio_tts tool with websocket_streaming: true, realtime_play: true]
Result: Audio streamed and played in real-time via WebSocket
Adjusting Speed, Volume, and Expressiveness
User: "Generate speech saying 'Breaking news!' at 1.5x speed with high emotion"
Claude: I'll generate expressive, fast-paced speech.
[Uses fish_audio_tts tool with text, speed: 1.5, temperature: 0.9]
Result: Audio generated with increased speed and expressiveness
Development
Local Development
- Clone the repository:
git clone https://github.com/da-okazaki/mcp-fish-audio-server.git
cd mcp-fish-audio-server
- Install dependencies:
npm install
- Create
.envfile:
cp .env.example .env
# Edit .env with your API key
- Build the project:
npm run build
- Run in development mode:
npm run dev
Testing
Run the test suite:
npm test
Project Structure
mcp-fish-audio-server/
โโโ src/
โ โโโ index.ts # MCP server entry point
โ โโโ tools/
โ โ โโโ tts.ts # TTS tool implementation
โ โโโ services/
โ โ โโโ fishAudio.ts # Fish Audio API client
โ โโโ types/
โ โ โโโ index.ts # TypeScript definitions
โ โโโ utils/
โ โโโ config.ts # Configuration management
โโโ tests/ # Test files
โโโ audio_output/ # Default audio output directory
โโโ package.json
โโโ tsconfig.json
โโโ README.md
API Documentation
Fish Audio Service
The service provides two main methods:
-
generateSpeech: Standard TTS generation
- Returns audio buffer
- Suitable for short texts
- Lower memory usage
-
generateSpeechStream: Streaming TTS generation
- Returns audio stream
- Suitable for long texts
- Real-time processing
Error Handling
The server handles various error scenarios:
- INVALID_API_KEY: Invalid or missing API key
- NETWORK_ERROR: Connection issues with Fish Audio API
- INVALID_PARAMS: Invalid request parameters
- QUOTA_EXCEEDED: API rate limit exceeded
- SERVER_ERROR: Fish Audio server errors
Troubleshooting
Common Issues
-
"FISH_API_KEY environment variable is required"
- Ensure you've set the
FISH_API_KEYenvironment variable - Check that the API key is valid
- Ensure you've set the
-
"Network error: Unable to reach Fish Audio API"
- Check your internet connection
- Verify Fish Audio API is accessible
- Check for proxy/firewall issues
-
"Text length exceeds maximum limit"
- Split long texts into smaller chunks
- Maximum supported length is 10,000 characters
-
Audio files not appearing
- Check the
AUDIO_OUTPUT_DIRpath exists - Ensure write permissions for the directory
- Check the
Contributing
Contributions are welcome! Please feel free to submit a Pull Request.
- Fork the repository
- Create your feature branch (
git checkout -b feature/AmazingFeature) - Commit your changes (
git commit -m 'Add some AmazingFeature') - Push to the branch (
git push origin feature/AmazingFeature) - Open a Pull Request
License
This project is licensed under the MIT License - see the LICENSE file for details.
Acknowledgments
- Fish Audio for providing the excellent TTS API
- Anthropic for creating the Model Context Protocol
- The MCP community for inspiration and examples
Support
For issues, questions, or contributions, please visit the GitHub repository.
Changelog
See CHANGELOG.md for a detailed list of changes.
Recommended Servers
playwright-mcp
A Model Context Protocol server that enables LLMs to interact with web pages through structured accessibility snapshots without requiring vision models or screenshots.
Magic Component Platform (MCP)
An AI-powered tool that generates modern UI components from natural language descriptions, integrating with popular IDEs to streamline UI development workflow.
Audiense Insights MCP Server
Enables interaction with Audiense Insights accounts via the Model Context Protocol, facilitating the extraction and analysis of marketing insights and audience data including demographics, behavior, and influencer engagement.
VeyraX MCP
Single MCP tool to connect all your favorite tools: Gmail, Calendar and 40 more.
graphlit-mcp-server
The Model Context Protocol (MCP) Server enables integration between MCP clients and the Graphlit service. Ingest anything from Slack to Gmail to podcast feeds, in addition to web crawling, into a Graphlit project - and then retrieve relevant contents from the MCP client.
Kagi MCP Server
An MCP server that integrates Kagi search capabilities with Claude AI, enabling Claude to perform real-time web searches when answering questions that require up-to-date information.
E2B
Using MCP to run code via e2b.
Neon Database
MCP server for interacting with Neon Management API and databases
Qdrant Server
This repository is an example of how to create a MCP server for Qdrant, a vector search engine.
Exa Search
A Model Context Protocol (MCP) server lets AI assistants like Claude use the Exa AI Search API for web searches. This setup allows AI models to get real-time web information in a safe and controlled way.