smallest-ai-mcp

smallest-ai-mcp

1
entertainment_and_mediadeveloper_tools

The Smallest AI MCP Server connects the Waves API with MCP-compatible LLMs, focusing on speed and security for voice applications. It allows for seamless voice synthesis and voice cloning workflows.

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Smallest AI MCP Server

Production-grade ModelContextProtocol (MCP) server for the Waves Text-to-Speech and Voice Cloning platform.
Fast, portable, and ready for real-world AI voice workflows.

🚀 Overview

Smallest AI MCP Server provides a seamless bridge between the powerful Waves TTS/Voice Cloning API and any MCP-compatible LLM or agent. It is designed for speed, security, and ease of deployment.

✨ Features

  • 🎤 List and preview voices — Instantly fetch all available voices from Waves.
  • 🗣️ Synthesize speech — Convert text to high-quality WAV audio files.
  • 👤 Clone voices — Create instant/professional voice clones.
  • 🗂️ Manage clones — List and delete your cloned voices.

All features are implemented as MCP tools, with no placeholders or stubs.

⚡ Quickstart

# 1. Clone the repo
$ git clone https://github.com/Akshay-Sisodia/smallest-ai-mcp.git
$ cd smallest-ai-mcp

# 2. Install dependencies
$ pip install -r requirements.txt

# 3. Configure your API key
$ cp .env.example .env
# Edit .env and add your real WAVES_API_KEY

# 4. Start the server
$ python server.py

🐳 Docker Usage

# Build the Docker image
$ docker build -t smallest-ai-mcp .

# Run the container
$ docker run -p 8000:8000 \
    -e WAVES_API_KEY=your_waves_api_key \
    smallest-ai-mcp

🛠️ Tech Stack

🏗️ Production & Deployment

  • Environment: Copy .env.example to .env and add your API key. Never commit secrets to git.
  • Dependencies: Install with pip install -r requirements.txt (Python 3.11+).
  • Docker: Use the provided Dockerfile for containerization.
  • Security: API keys are required at startup and never exposed.
  • License: MIT (see ).

🤝 Contributing

Pull requests and issues are welcome! Please open an issue to discuss major changes.

👤 Maintainer

📄 License

MIT

Groq MCP Client

A Streamlit application that connects to an MCP (Model Context Protocol) server and uses Groq's LLM API for chat conversations with tool execution capabilities.

Features

  • Connect to any MCP server using the official MCP SDK via SSE (Server-Sent Events)
  • Asynchronous communication with the MCP server
  • Chat interface with streaming responses from Groq
  • Tool execution through the MCP server
  • Clean and user-friendly UI

Requirements

  • Python 3.8+
  • Groq API key
  • An MCP server that supports SSE (running on HTTP)
  • MCP SDK (automatically installed with requirements.txt)

Installation

  1. Clone this repository
  2. Install the dependencies:
pip install -r requirements.txt

Usage

  1. Run the application:
streamlit run groq_mcp_client.py
  1. In the Streamlit UI:
    • Enter your Groq API key in the sidebar
    • Enter the URL of your MCP server (default: http://localhost:8000)
    • Click "Connect to MCP Server"
    • Start chatting!

How it works

  1. The application starts and connects to the MCP server using the official MCP SDK via SSE
  2. The MCP server provides a list of available tools
  3. When you send a message:
    • The message is sent to Groq's API
    • If Groq decides to use a tool, the tool call is executed through the MCP server
    • The tool results are sent back to Groq
    • Groq provides a final response

Implementation Details

  • Uses the official MCP SDK for communication with MCP servers
  • Connects via SSE (Server-Sent Events) for HTTP-based servers
  • Implements async/await pattern for efficient server communication
  • Maintains compatibility with the Streamlit UI framework

Customization

You can modify the following aspects of the application:

  • Change the Groq model by modifying the model parameter in the GroqClient.generate_stream method
  • Customize the UI by modifying the Streamlit components
  • Add additional functionality to the MCP client

License

MIT