kokoro-tts-mcp

<a href="https://glama.ai/mcp/servers/@mberg/kokoro-tts-mcp"> <img width="380" height="200" src="https://glama.ai/mcp/servers/@mberg/kokoro-tts-mcp/badge" alt="Kokoro Text to Speech Server MCP server" /> </a>

Kokoro Text to Speech (TTS) MCP Server

Kokoro Text to Speech MCP server that generates .mp3 files with option to upload to S3.

Uses: https://huggingface.co/spaces/hexgrad/Kokoro-TTS

Configuration

• Clone to a local repo.
• Download the Kokoro Onnx Weights for kokoro-v1.0.onnx and voices-v1.0.bin and store in the same repo.

Add the following to your MCP configs. Update with your own values.

  "kokoro-tts-mcp": {
      "command": "uv",
      "args": [
        "--directory",
        "/path/toyourlocal/kokoro-tts-mcp",
        "run",
        "mcp-tts.py"
      ],
      "env": {
        "TTS_VOICE": "af_heart",
        "TTS_SPEED": "1.0",
        "TTS_LANGUAGE": "en-us",
        "AWS_ACCESS_KEY_ID": "",
        "AWS_SECRET_ACCESS_KEY": "",
        "AWS_REGION": "us-east-1",
        "AWS_S3_FOLDER": "mp3",
        "S3_ENABLED": "true",
        "MP3_FOLDER": "/path/to/mp3"
      } 
    }

Install ffmmeg

This is needed to convert .wav to .mp3 files

For mac:

brew install ffmpeg

To run locally add these to your .env file. See env.example and copy to .env and modify with your own values.

Supported Environment Variables

• AWS_ACCESS_KEY_ID: Your AWS access key ID
• AWS_SECRET_ACCESS_KEY: Your AWS secret access key
• AWS_S3_BUCKET_NAME: S3 bucket name
• AWS_S3_REGION: S3 region (e.g., us-east-1)
• AWS_S3_FOLDER: Folder path within the S3 bucket
• AWS_S3_ENDPOINT_URL: Optional custom endpoint URL for S3-compatible storage
• MCP_HOST: Host to bind the server to (default: 0.0.0.0)
• MCP_PORT: Port to listen on (default: 9876)
• MCP_CLIENT_HOST: Hostname for client connections to the server (default: localhost)
• DEBUG: Enable debug mode (set to "true" or "1")
• S3_ENABLED: Enable S3 uploads (set to "true" or "1")
• MP3_FOLDER: Path to store MP3 files (default is 'mp3' folder in script directory)
• MP3_RETENTION_DAYS: Number of days to keep MP3 files before automatic deletion
• DELETE_LOCAL_AFTER_S3_UPLOAD: Whether to delete local MP3 files after successful S3 upload (set to "true" or "1")
• TTS_VOICE: Default voice for the TTS client (default: af_heart)
• TTS_SPEED: Default speed for the TTS client (default: 1.0)
• TTS_LANGUAGE: Default language for the TTS client (default: en-us)

Running the Server Locally

Preferred method use UV `` uv run mcp-tts.py ``

Using the TTS Client

The mcp_client.py script allows you to send TTS requests to the server. It can be used as follows:

Connection Settings

When running the server and client on the same machine:

• Server should bind to 0.0.0.0 (all interfaces) or 127.0.0.1 (localhost only)
• Client should connect to localhost or 127.0.0.1

Basic Usage

python mcp_client.py --text "Hello, world!"

Reading Text from a File

python mcp_client.py --file my_text.txt

Customizing Voice and Speed

python mcp_client.py --text "Hello, world!" --voice "en_female" --speed 1.2

Disabling S3 Upload

python mcp_client.py --text "Hello, world!" --no-s3

Command-line Options

python mcp_client.py --help

MP3 File Management

The TTS server generates MP3 files that are stored locally and optionally uploaded to S3. You can configure how these files are managed:

Local Storage

• Set MP3_FOLDER in your .env file to specify where MP3 files are stored
• Files are kept in this folder unless automatically deleted

Automatic Cleanup

• Set MP3_RETENTION_DAYS=30 (or any number) to automatically delete files older than that number of days
• Set DELETE_LOCAL_AFTER_S3_UPLOAD=true to delete local files immediately after successful S3 upload

S3 Integration

• Enable/disable S3 uploads with S3_ENABLED=true or DISABLE_S3=true
• Configure AWS credentials and bucket settings in the .env file
• S3 uploads can be disabled per-request using the client's --no-s3 option