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MatthewDailey

Rime MCP

by MatthewDailey
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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

Related MCP server: AivisSpeech MCP Server

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

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

Available Tools

1 tool
speakA

Speak text aloud using Rime's text-to-speech API. Should be used when user asks you to speak or to announce and explain when you finish a command

User configuration:

WHO_TO_ADDRESS: user

WHEN_TO_SPEAK: when asked to speak or when finishing a command

VOICE: cove

GUIDANCE: Use the speak tool to convert text to speech when the user requests audio output or when providing verbal responses

ParametersJSON Schema
NameRequiredDescriptionDefault
textYesThe text to speak aloud
speakerNoThe voice to use (defaults to 'cove')
speedAlphaNoSpeech speed multiplier (default: 1.0)
reduceLatencyNoWhether to optimize for lower latency (default: false)

TDQS

A3.8/5.0
Behavior3/5

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

No annotations are provided, so the description carries the full burden. It mentions the API ('Rime's text-to-speech API') and usage context, but lacks details on behavioral traits such as rate limits, authentication requirements, error handling, or output format. The description does not contradict annotations, but it provides only basic operational context without deeper behavioral insights.

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 somewhat verbose and includes redundant sections like 'User configuration:' with WHO_TO_ADDRESS, WHEN_TO_SPEAK, VOICE, and GUIDANCE, which could be integrated more efficiently. While it provides useful information, the structure is not optimally front-loaded, and some sentences (e.g., the configuration headers) do not add significant value beyond the core description.

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?

Given the tool's moderate complexity (4 parameters, no output schema, no annotations), the description is fairly complete. It covers purpose, usage guidelines, and basic context, but lacks details on behavioral aspects like performance or errors. Without annotations or output schema, it does enough to guide usage but could be more comprehensive for full transparency.

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 the schema fully documents all parameters. The description does not add any parameter-specific information beyond what the schema provides (e.g., it mentions 'VOICE: cove' but the schema already describes the 'speaker' parameter with a default). Baseline score of 3 is appropriate as the schema handles parameter documentation effectively.

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

Purpose4/5

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

The description clearly states the tool's purpose: 'Speak text aloud using Rime's text-to-speech API.' It specifies the verb ('speak') and resource ('text'), and distinguishes it from potential alternatives by mentioning the specific API. However, since there are no sibling tools, the differentiation aspect is not applicable, preventing a perfect score.

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

Usage Guidelines5/5

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

The description provides explicit usage guidelines: 'Should be used when user asks you to speak or to announce and explain when you finish a command' and 'Use the speak tool to convert text to speech when the user requests audio output or when providing verbal responses.' It clearly defines when to use the tool, including specific scenarios, making it highly actionable for an AI agent.

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

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TDQS

A3.7/5.0
Disambiguation5/5

With only one tool, there is no possibility of ambiguity or overlap between tools. The speak tool has a single, clearly defined purpose for text-to-speech conversion.

Naming Consistency5/5

A single tool inherently has perfect naming consistency, as there are no other tools to compare it against. The name 'speak' follows a clear verb pattern appropriate for its function.

Tool Count2/5

A single tool is too few for most MCP server purposes, even for a text-to-speech service. This feels thin and limited, lacking complementary tools like volume control, voice selection, or speech status checks that would enhance functionality.

Completeness2/5

The tool surface is severely incomplete for a text-to-speech domain. While the speak tool covers the core output function, there are obvious gaps such as no tools for managing voices, adjusting speech parameters, stopping speech, or checking speech status, which limits agent capabilities.

Maintenance

ActivityInactive
ResponsivenessSyncing

Resources

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