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Rime MCP

Rime MCP

Local
github.com/MatthewDaileyMedia & Design27Updated 74w ago

About Rime MCP

Rime MCP is an MCP server that converts text to speech using the Rime API and plays the audio through the system player. It suits users who want AI assistants to report results aloud.

Key features

  1. 01speak tool that synthesizes and plays text
  2. 02Uses the Rime voice synthesis API
  3. 03Environment variables customize tool guidance and default voice
  4. 04Playback on macOS, Windows and Linux

Use cases

  1. 01A coding agent announcing results aloud after a task
  2. 02Assigning different voices for different languages or contexts
  3. 03Receiving assistant replies as speech in Claude Desktop

MCP support

This project is an MCP server that exposes a speak tool to MCP clients for converting text to audio playback.

Configuration

Local server config is the same for every client. Copy it into your client’s MCP config file.

mcp.json · Universal config
{
"mcpServers": {
"rime": {
"command": "npx",
"args": [
"-y",
"@smithery/cli"
]
}
}
}
Run commandnpx -y @smithery/cli

Local servers need a runtime on your machine (e.g. Node.js 18+). Config file locations differ by client; see your client’s docs.

Packages
npm@smithery/cli

Tools

No tool list yet

Local servers expose tools only once running on your machine, and the maintainer has not provided a tool list.

Details

From the project README

Rime MCP

rime

A Model Context Protocol (MCP) server that provides text-to-speech capabilities using the Rime API. This server downloads audio and plays it using the system's native audio player.

Features

  • Exposes a speak tool that converts text to speech and plays it through system audio
  • Uses Rime's high-quality voice synthesis API

Requirements

  • Node.js 16.x or higher
  • A working audio output device
  • macOS: Uses afplay

There's sample code from Claude for the following that is not tested 🤙✨

  • Windows: Built-in Media.SoundPlayer (PowerShell)
  • Linux: mpg123, mplayer, aplay, or ffplay

MCP Configuration

"ref": {
  "command": "npx",
  "args": ["rime-mcp"],
  "env": {
      RIME_API_KEY=your_api_key_here

      # Optional configuration
      RIME_GUIDANCE="<guide how the agent speaks>"
      RIME_WHO_TO_ADDRESS="<your name>"
      RIME_WHEN_TO_SPEAK="<tell the agent when to speak>"
      RIME_VOICE="cove" 
  }
}

All of the optional env vars are part of the tool definition and are prompts to

All voice options are listed here.

You can get your API key from the Rime Dashboard.

The following environment variables can be used to customize the behavior:

  • RIME_GUIDANCE: The main description of when and how to use the speak tool
  • RIME_WHO_TO_ADDRESS: Who the speech should address (default: "user")
  • RIME_WHEN_TO_SPEAK: When the tool should be used (default: "when asked to speak or when finishing a command")
  • RIME_VOICE: The default voice to use (default: "cove")

Example use cases

Demo of Rime MCP in Cursor

Example 1: Coding agent announcements
"RIME_WHEN_TO_SPEAK": "Always conclude your answers by speaking.",
"RIME_GUIDANCE": "Give a brief overview of the answer. If any files were edited, list them."
Example 2: Learn how the kids talk these days
RIME_GUIDANCE="Use phrases and slang common among Gen Alpha."
RIME_WHO_TO_ADDRESS="Matt"
RIME_WHEN_TO_SPEAK="when asked to speak"
Example 3: Different languages based on context
RIME_VOICE="use 'cove' when talking about Typescript and 'antoine' when talking about Python"

Development

  1. Install dependencies:
npm install
  1. Build the server:
npm run build
  1. Run in development mode with hot reload:
npm run dev

License

MIT

Badges

Rime MCP server Smithery Badge
Installing via Smithery

To install Rime Text-to-Speech Server for Claude Desktop automatically via Smithery:

npx -y @smithery/cli install @MatthewDailey/rime-mcp --client claude

Comments

Markdown supported

New to MCP?

From what the protocol is to installing your first server, these answers cover most first questions.

An open protocol that defines how AI applications connect to external tools and data in a uniform way. Clients talk to the model; MCP servers provide tools, resources and prompt templates. They exchange standard messages, so one server can be reused by many clients.