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:
- 🌊 State-of-the-art voice synthesis with natural-sounding output
- 🎯 Voice cloning capabilities to create custom voice models
- 🌍 Multilingual support including English, Japanese, Chinese, and more
- ⚡ Low-latency streaming for real-time applications
- 🎨 Fine-grained control over speech prosody and emotions
This MCP server brings Fish Audio's powerful capabilities directly to your LLM workflows.
Features
- 🎙️ High-Quality TTS: Leverage Fish Audio's state-of-the-art TTS models
- 🌊 Streaming Support: Real-time audio streaming for low-latency applications
- 🎨 Multiple Voices: Support for custom voice models via reference IDs
- 🎯 Smart Voice Selection: Select voices by ID, name, or tags
- 📚 Voice Library Management: Configure and manage multiple voice references
- 🔧 Flexible Configuration: Environment variable-based configuration
- 📦 Multiple Audio Formats: Support for MP3, WAV, PCM, and Opus
- 🚀 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 → name → 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 'こんにちは' 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 — 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 → name → tag againstFISH_REFERENCES. You can also pass raw reference IDs directly (speakers: ["id1", "id2"]). - If you only pass one identifier, it behaves like
reference_id— 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/
├── src/
│ ├── index.ts # MCP server entry point
│ ├── tools/
│ │ └── tts.ts # TTS tool implementation
│ ├── services/
│ │ └── fishAudio.ts # Fish Audio API client
│ ├── types/
│ │ └── index.ts # TypeScript definitions
│ └── utils/
│ └── config.ts # Configuration management
├── tests/ # Test files
├── audio_output/ # Default audio output directory
├── package.json
├── tsconfig.json
└── 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.
推荐服务器
Baidu Map
百度地图核心API现已全面兼容MCP协议,是国内首家兼容MCP协议的地图服务商。
Playwright MCP Server
一个模型上下文协议服务器,它使大型语言模型能够通过结构化的可访问性快照与网页进行交互,而无需视觉模型或屏幕截图。
Magic Component Platform (MCP)
一个由人工智能驱动的工具,可以从自然语言描述生成现代化的用户界面组件,并与流行的集成开发环境(IDE)集成,从而简化用户界面开发流程。
Audiense Insights MCP Server
通过模型上下文协议启用与 Audiense Insights 账户的交互,从而促进营销洞察和受众数据的提取和分析,包括人口统计信息、行为和影响者互动。
VeyraX
一个单一的 MCP 工具,连接你所有喜爱的工具:Gmail、日历以及其他 40 多个工具。
graphlit-mcp-server
模型上下文协议 (MCP) 服务器实现了 MCP 客户端与 Graphlit 服务之间的集成。 除了网络爬取之外,还可以将任何内容(从 Slack 到 Gmail 再到播客订阅源)导入到 Graphlit 项目中,然后从 MCP 客户端检索相关内容。
Kagi MCP Server
一个 MCP 服务器,集成了 Kagi 搜索功能和 Claude AI,使 Claude 能够在回答需要最新信息的问题时执行实时网络搜索。
e2b-mcp-server
使用 MCP 通过 e2b 运行代码。
Neon MCP Server
用于与 Neon 管理 API 和数据库交互的 MCP 服务器
Exa MCP Server
模型上下文协议(MCP)服务器允许像 Claude 这样的 AI 助手使用 Exa AI 搜索 API 进行网络搜索。这种设置允许 AI 模型以安全和受控的方式获取实时的网络信息。