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zjandrew

Fish Audio MCP Server

by zjandrew

Fish Audio MCP Server

npm version License: MIT

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.

Related MCP server: ElevenLabs MCP Server

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

  1. Get your Fish Audio API key from Fish Audio

  2. Set up environment variables:

export FISH_API_KEY=your_fish_audio_api_key_here
  1. 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:

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 ID

  • reference_name (optional): Select voice by name

  • reference_tag (optional): Select voice by tag

  • speakers (optional, s2-pro only): Ordered list of speaker identifiers for multi-speaker dialogue. Each entry is resolved against FISH_REFERENCES by id → name → tag (or used as a raw reference_id when no references are configured). The index maps to <|speaker:N|> tags in text. See the multi-speaker example below.

  • streaming (optional): Enable streaming mode

  • format (optional): Output format (mp3, wav, pcm, opus)

  • mp3_bitrate (optional): MP3 bitrate (64, 128, 192)

  • opus_bitrate (optional): Opus bitrate in bps (-1000 for 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 path

  • auto_play (optional): Automatically play the generated audio

  • websocket_streaming (optional): Use WebSocket streaming instead of HTTP

  • realtime_play (optional): Play audio in real-time during WebSocket streaming

  • speed (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:

  1. FISH_MODEL_ID=s2-pro (the default since 0.8.0).

  2. 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 speakers is resolved by id → name → tag against FISH_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 speakers on a non-s2-pro model returns an error; switch the model via FISH_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

  1. Clone the repository:

git clone https://github.com/da-okazaki/mcp-fish-audio-server.git
cd mcp-fish-audio-server
  1. Install dependencies:

npm install
  1. Create .env file:

cp .env.example .env
# Edit .env with your API key
  1. Build the project:

npm run build
  1. 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:

  1. generateSpeech: Standard TTS generation

    • Returns audio buffer

    • Suitable for short texts

    • Lower memory usage

  2. 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

  1. "FISH_API_KEY environment variable is required"

    • Ensure you've set the FISH_API_KEY environment variable

    • Check that the API key is valid

  2. "Network error: Unable to reach Fish Audio API"

    • Check your internet connection

    • Verify Fish Audio API is accessible

    • Check for proxy/firewall issues

  3. "Text length exceeds maximum limit"

    • Split long texts into smaller chunks

    • Maximum supported length is 10,000 characters

  4. Audio files not appearing

    • Check the AUDIO_OUTPUT_DIR path exists

    • Ensure write permissions for the directory

Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

  1. Fork the repository

  2. Create your feature branch (git checkout -b feature/AmazingFeature)

  3. Commit your changes (git commit -m 'Add some AmazingFeature')

  4. Push to the branch (git push origin feature/AmazingFeature)

  5. 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.

Available Tools

2 tools
fish_audio_list_referencesA

List all configured voice references

ParametersJSON Schema
NameRequiredDescriptionDefault

No parameters

TDQS

A3.8/5.0
Behavior2/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

The description is minimal and does not disclose any behavioral traits beyond the basic operation. Since no annotations are provided, the description carries full burden, but it omits details like whether this is read-only, authentication requirements, or what 'configured' entails.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single sentence with no extra words. It is front-loaded and every word earns its place.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a simple list tool with no parameters, the description is sufficient to convey the purpose. However, it lacks details about the output format (e.g., array of references) which would improve completeness.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The tool has zero parameters and schema coverage is 100%. Description does not add parameter-level meaning but is not required to. Per guidelines, baseline is 4 for no parameters.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states 'List all configured voice references' with a specific verb 'list' and resource 'voice references'. It effectively distinguishes from sibling tool fish_audio_tts, which implies text-to-speech synthesis.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides no explicit guidance on when to use this tool versus fish_audio_tts. The context is implied by the resource 'voice references', but no alternatives or exclusions are mentioned.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

fish_audio_ttsB

Generate speech from text using Fish Audio TTS API

ParametersJSON Schema
NameRequiredDescriptionDefault
textYesText to convert to speech
reference_idNoVoice model reference ID (optional)
reference_nameNoVoice model name to search for (optional)
reference_tagNoVoice model tag to search for (optional)
streamingNoEnable HTTP streaming mode (optional)
websocket_streamingNoEnable WebSocket streaming mode (optional)
realtime_playNoEnable real-time audio playback during streaming (optional)
formatNoOutput audio format (optional)mp3
mp3_bitrateNoMP3 bitrate in kbps (optional)
opus_bitrateNoOpus bitrate in bps; -1000 = auto. Only applies when format=opus.
sample_rateNoAudio sample rate in Hz. Defaults to format-native rate when omitted.
normalizeNoEnable text normalization (optional)
latencyNoLatency mode: low=lowest latency, balanced=reduced latency, normal=best qualitybalanced
output_pathNoCustom output file path (optional)
auto_playNoAutomatically play the generated audio (optional)
speedNoSpeaking rate multiplier (0.5=half speed, 1.0=normal, 2.0=double speed)
volumeNoVolume adjustment in dB (0=no change, positive=louder, negative=quieter)
normalize_loudnessNoNormalize output loudness for consistent perceived volume (s2-pro only)
temperatureNoExpressiveness/emotion control (0=consistent and calm, 1=varied and emotional)
top_pNoNucleus sampling diversity (0..1)
chunk_lengthNoTarget text segment size for processing (100-300)
max_new_tokensNoMaximum audio tokens to generate per text chunk
repetition_penaltyNoPenalty for repeating audio patterns; values >1.0 reduce repetition
min_chunk_lengthNoMinimum characters before splitting into a new chunk (0-100)
condition_on_previous_chunksNoUse previous audio as context for voice consistency across chunks
early_stop_thresholdNoEarly stopping threshold for batch processing (0..1)
speakersNoMulti-speaker mode (s2-pro only). Ordered list of speaker identifiers — each entry is resolved against FISH_REFERENCES by id, then name, then tag (or treated as a raw reference_id if no references are configured). The order maps to speaker tags `<|speaker:0|>`, `<|speaker:1|>`, ... in `text`. Provide at least 2 entries to engage multi-speaker; a single entry is equivalent to `reference_id`.

TDQS

B3/5.0
Behavior2/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations are provided, so the description must convey behavioral traits. It only says 'generate speech', omitting key aspects like streaming behavior, output format handling, or whether it is safe/idempotent. The minimal description fails to compensate for absent annotations.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness3/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single short sentence, which is concise but lacks structure. It would benefit from additional details without being verbose. It is adequate but not optimal.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness2/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool's complexity (27 parameters, no output schema), the description is insufficient. It does not explain return values, rate limits, or how to handle outputs. For a sophisticated TTS API, more context is needed.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100%, so baseline is 3. The description adds no extra parameter meaning; it merely restates the tool's purpose. It does not exceed the schema's own descriptions.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the verb 'generate speech' and resource 'text using Fish Audio TTS API', making the primary purpose evident. It also inherently distinguishes from the sibling tool 'fish_audio_list_references' which lists voice models.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

No guidance on when to use this tool vs alternatives, no prerequisites, and no conditions for optimal usage. The sibling tool exists but no differentiation or context is provided.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Tool Schema Changelog

Recent tool additions, removals, and schema changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. 2 tool updatesv0.8.0
    • First observedfish_audio_list_references
    • First observedfish_audio_tts

TDQS

B3.4/5.0
Disambiguation5/5

The two tools have completely distinct purposes: one manages references, the other generates speech. No overlap or ambiguity.

Naming Consistency5/5

Both tools follow the consistent 'fish_audio_verb_noun' pattern with snake_case, making them predictable and easy to understand.

Tool Count3/5

With only 2 tools, the server feels thin for a TTS service, but it may be appropriate for a minimal integration. The count is borderline but not extreme.

Completeness2/5

The server lacks essential operations like creating/deleting references, listing voices, or setting voice parameters. Users cannot fully manage the TTS workflow, leading to significant gaps.

Maintenance

ActivityInactive
ResponsivenessNo issues

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