MCP server
by da-okazaki
Convert text to speech with Fish Audio. Use our AI voice generator for real-time, high-quality speech to voice, free for
Converts text to speech using Fish Audio's API with support for multiple voice models, streaming, and various audio formats.
Fish Audio is a community-built MCP server published by da-okazaki that provides AI assistants with tools and capabilities via the Model Context Protocol. Convert text to speech with Fish Audio. Use our AI voice generator for real-time, high-quality speech to voice, free for It is categorized under ai ml.
You can install Fish Audio in your AI client of choice. Use the install panel on this page to get one-click setup for Cursor, Claude Desktop, VS Code, and other MCP-compatible clients. This server runs locally on your machine via the stdio transport.
MIT
Fish Audio is released under the MIT license. This is a permissive open-source license, meaning you can freely use, modify, and distribute the software.
Add new capabilities to Claude beyond text generation
Example
Access external data sources, execute code, interact with tools and services
Transform Claude from chatbot to action-taking agent
Provide Claude with access to relevant context and data
Example
Load project documentation, access knowledge bases, query databases
Get more accurate, context-aware responses
Automate multi-step workflows combining AI and external tools
Example
Research → Summarize → Create document → Send notification
Complete complex tasks end-to-end without manual steps
Share your MCP server with the developer community
Fish Audio is among the better-indexed MCP projects we tried; the explainx.ai summary tracks the official description.
I recommend Fish Audio for teams standardizing on MCP; the explainx.ai page compares cleanly with sibling servers.
Fish Audio reduced integration guesswork — categories and install configs on the listing matched the upstream repo.
Strong directory entry: Fish Audio surfaces stars and publisher context so we could sanity-check maintenance before adopting.
I recommend Fish Audio for teams standardizing on MCP; the explainx.ai page compares cleanly with sibling servers.
According to our notes, Fish Audio benefits from clear Model Context Protocol framing — fewer ambiguous “AI plugin” claims.
Strong directory entry: Fish Audio surfaces stars and publisher context so we could sanity-check maintenance before adopting.
We evaluated Fish Audio against two servers with overlapping tools; this profile had the clearer scope statement.
Fish Audio has been reliable for tool-calling workflows; the MCP profile page is a good permalink for internal docs.
Strong directory entry: Fish Audio surfaces stars and publisher context so we could sanity-check maintenance before adopting.
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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.
Fish Audio is a cutting-edge Text-to-Speech platform that offers:
This MCP server brings Fish Audio's powerful capabilities directly to your LLM workflows.
You can run this MCP server directly using npx:
npx @alanse/fish-audio-mcp-server
Or install it globally:
npm install -g @alanse/fish-audio-mcp-server
Get your Fish Audio API key from Fish Audio
Set up environment variables:
export FISH_API_KEY=your_fish_audio_api_key_here
{
"mcpServers": {
"fish-audio": {
"command": "npx",
"args": ["-y", "@alanse/fish-audio-mcp-server"],
"env": {
"FISH_API_KEY": "your_fish_audio_api_key_here",
"FISH_MODEL_ID": "speech-1.6",
"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"
}
}
}
}
{
"mcpServers": {
"fish-audio": {
"command": "npx",
"args": ["-y", "@alanse/fish-audio-mcp-server"],
"env": {
"FISH_API_KEY": "your_fish_audio_api_key_here",
"FISH_MODEL_ID": "speech-1.6",
"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"
}
}
}
}
| Variable | Description | Default | Required |
|---|---|---|---|
FISH_API_KEY | Your Fish Audio API key | - | Yes |
FISH_MODEL_ID | TTS model to use (s1, speech-1.5, speech-1.6) | s1 | 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 (normal, balanced) | 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 |
You can configure multiple voice references in two ways:
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"
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
Once configured, the Fish Audio MCP server provides two tools to LLMs.
fish_audio_ttsGenerates speech from text using Fish Audio's TTS API.
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 tagstreaming (optional): Enable streaming modeformat (optional): Output format (mp3, wav, pcm, opus)mp3_bitrate (optional): MP3 bitrate (64, 128, 192)normalize (optional): Enable text normalization (default: true)latency (optional): Latency mode (normal, balanced)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 streamingVoice Selection Priority: reference_id > reference_name > reference_tag > default
fish_audio_list_referencesLists all configured voice references.
No parameters required.
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
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
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
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
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
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
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
git clone https://github.com/da-okazaki/mcp-fish-audio-server.git
cd mcp-fish-audio-server
npm install
.env file:cp .env.example .env
# Edit .env with your API key
npm run build
npm run dev
Run the test suite:
npm test
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
The service provides two main methods:
generateSpeech: Standard TTS generation
generateSpeechStream: Streamin
Prerequisites
Time Estimate
15-60 minutes depending on server complexity
Steps
Troubleshooting
✓ Do
✗ Don't
💡 Pro Tips
Architecture
Model Context Protocol standardizes how AI hosts (Claude, Cursor) communicate with external tools and data sources through server implementations.
Protocols
Compatibility
✓ Use when
Use when you need Claude to access external data, execute actions, or integrate with tools. Best for extending AI capabilities beyond conversation.
✗ Avoid when
Avoid when native integrations exist (use official APIs directly), for real-time critical systems, or when security/compliance requires zero external dependencies.