Overview

The RealtimeTTS MCP Server enables AI models to convert text into speech in real-time. This server is built on the powerful RealtimeTTS Python library, which is designed for low-latency text-to-speech applications. It supports a wide range of TTS engines, making it a versatile solution for adding voice capabilities to AI agents.

Created by:

Developed by KoljaB

Key Features

⚡

Low-Latency Conversion

Almost instantaneous text-to-speech conversion, ideal for real-time interactions

🔊

High-Quality Audio

Generates clear and natural-sounding speech

🔄

Multiple TTS Engines

Supports OpenAI TTS, ElevenLabs, Azure, Coqui TTS, and more

🌍

Multilingual Support

Provides speech synthesis in multiple languages

Available Tools

Quick Reference

Tool	Purpose	Category
`synthesize`	Convert text to speech	Core
`stream`	Stream synthesized audio	Core
`set_engine`	Select the TTS engine	Configuration
`get_engines`	List available engines	Discovery

Detailed Usage

synthesize▶

Convert a string of text into speech and play it.

use_mcp_tool({
  server_name: "text_to_speech",
  tool_name: "synthesize",
  arguments: {
    text: "Hello, world! This is a test."
  }
});

stream▶

Stream synthesized audio in real-time as it's generated.

use_mcp_tool({
  server_name: "text_to_speech",
  tool_name: "stream",
  arguments: {
    text: "This is a streaming test to demonstrate real-time audio synthesis."
  }
});

set_engine▶

Select the TTS engine to use for speech synthesis.

use_mcp_tool({
  server_name: "text_to_speech",
  tool_name: "set_engine",
  arguments: {
    engine: "elevenlabs"
  }
});

get_engines▶

Get a list of available TTS engines.

use_mcp_tool({
  server_name: "text_to_speech",
  tool_name: "get_engines",
  arguments: {}
});

Installation

{
  "mcpServers": {
    "text_to_speech": {
      "command": "pip",
      "args": [
        "install",
        "realtimetts[all]"
      ]
    }
  }
}

Common Use Cases

1. Voice-Enabled AI Assistants

Provide voice output for AI assistants and chatbots.

// Let the assistant speak its response
use_mcp_tool({
  server_name: "text_to_speech",
  tool_name: "synthesize",
  arguments: {
    text: "I'm sorry, I didn't understand that. Could you please rephrase?"
  }
});

2. Accessibility

Make applications more accessible by providing audio versions of text content.

// Read the content of an article aloud
use_mcp_tool({
  server_name: "text_to_speech",
  tool_name: "synthesize",
  arguments: {
    text: articleContent
  }
});

3. Real-Time Notifications

Create audible notifications for events in your applications.

// Announce a new message
use_mcp_tool({
  server_name: "text_to_speech",
  tool_name: "synthesize",
  arguments: {
    text: "You have a new message from Jane."
  }
});

Sources

Ollama Deep Researcher: AI Model for Web Search & LLM Synthesis

Ollama Deep Researcher MCP servers enable AI models to perform advanced topic research using web search and LLM synthesis, powered by a local MCP server.

Pinecone MCP Server

Integrate Pinecone with your AI assistants using the Model Context Protocol (MCP) for enhanced vector database interactions.

Confluence MCP Server

Confluence MCP servers provide interfaces for LLMs to interact with Atlassian Confluence workspaces. These servers enable AI models to manage documentation, collaborate on content, and automate knowledge management tasks.

Text to Speech MCP Server