typecast-api-mcp-server

MCP Server for typecast-api, enabling seamless integration with MCP clients. This project provides a standardized way to interact with Typecast API through the Model Context Protocol.

About

This project implements a Model Context Protocol server for Typecast API, allowing MCP clients to interact with the Typecast API in a standardized way.

Supported Models

ssfm-v30 Features

• 7 Emotion Presets: normal, happy, sad, angry, whisper, toneup, tonedown
• Smart Mode: AI automatically infers emotion from context using previous_text and next_text
• 37 Languages: Extended language support

Feature Implementation Status

Quick Voice Cloning

The MCP server exposes two tools for temporary custom voice workflows:

• clone_voice: creates a quick-cloned custom voice from a local WAV or MP3 file.
• delete_cloned_voice: deletes a cloned voice ID that starts with uc_.

Quick cloning constraints:

• Voice name must be 1-30 characters.
• Audio sample must be WAV or MP3.
• Audio sample must be 25 MB or smaller.
• Use ssfm-v30 unless you have a specific compatibility reason.

Typical flow:

1. Run clone_voice with name, audio_file_path, and optional model.
2. Use the returned next_step_voice_id and next_step_model in text_to_speech, text_to_speech_stream, or text_to_speech_with_timestamps.
3. Run delete_cloned_voice when the temporary cloned voice is no longer needed.

Voice Recommendations

Use recommend_voices when you know the desired style, mood, language, or use case but do not know the exact voice ID yet. It calls GET /v1/voices/recommendations and returns candidates sorted by score.

The recommendation response intentionally contains only voice_id, voice_name, and score. If an agent needs details about a recommended voice, call get_voice for each returned ID or get_voices for a broader filtered list before using the ID in TTS.

Contributing

Contributions are always welcome! Feel free to submit a Pull Request.

License

MIT License

Setup

Environment Variables

Set the following environment variables:

TYPECAST_API_KEY=<your-api-key>
TYPECAST_OUTPUT_DIR=<your-output-directory> # default: ~/Downloads/typecast_output

Usage with Claude Desktop / Cursor

You can add the following to your claude_desktop_config.json or Cursor MCP settings:

Recommended: Using uvx (No installation required)

{
  "mcpServers": {
    "typecast-api-mcp-server": {
      "command": "uvx",
      "args": [
        "--from",
        "git+https://github.com/neosapience/typecast-api-mcp-server.git",
        "typecast-api-mcp-server"
      ],
      "env": {
        "TYPECAST_API_KEY": "YOUR_API_KEY",
        "TYPECAST_OUTPUT_DIR": "PATH/TO/YOUR/OUTPUT/DIR"
      }
    }
  }
}

This method automatically fetches and runs the server from GitHub without manual cloning.

Note for Linux users: If you're running on Linux, you need to add the XDG_RUNTIME_DIR environment variable to the env section:

"env": {
  "TYPECAST_API_KEY": "YOUR_API_KEY",
  "TYPECAST_OUTPUT_DIR": "PATH/TO/YOUR/OUTPUT/DIR",
  "XDG_RUNTIME_DIR": "/run/user/1000"
}

Alternative: Local Installation

If you prefer to clone and run locally:

Git Clone

git clone https://github.com/neosapience/typecast-api-mcp-server.git
cd typecast-api-mcp-server

Dependencies

This project requires Python 3.10 or higher and uses uv for package management.

# Create virtual environment and install packages
uv venv
uv pip install -e .

Local Configuration

{
  "mcpServers": {
    "typecast-api-mcp-server": {
      "command": "uv",
      "args": [
        "--directory",
        "/PATH/TO/YOUR/PROJECT",
        "run",
        "typecast-api-mcp-server"
      ],
      "env": {
        "TYPECAST_API_KEY": "YOUR_API_KEY",
        "TYPECAST_OUTPUT_DIR": "PATH/TO/YOUR/OUTPUT/DIR"
      }
    }
  }
}

Replace /PATH/TO/YOUR/PROJECT with the actual path where your project is located.

Manual Execution

You can also run the server manually:

uv run python app/main.py