What is Mcp Assistant

MCP_Assistant is an AI assistant built on the Model Context Protocol (MCP) using Groq and LangChain, featuring a Streamlit frontend and FastAPI backend for chat interactions powered by large language models.

Use cases

Use cases include automated customer service chatbots, educational tutoring systems, and interactive virtual assistants for web browsing and information retrieval.

How to use

To use MCP_Assistant, clone the repository, install the dependencies, set up your environment variables, configure optional MCP tools, and run the FastAPI backend followed by the Streamlit frontend.

Key features

Key features include Groq LLM integration with memory-enabled conversation, MCP server integration for Google Search and Playwright, a FastAPI backend for chat API requests, a real-time chat interface via Streamlit, and Docker support for containerized deployment.

Where to use

MCP_Assistant can be used in various fields such as customer support, educational tools, and any application requiring interactive chat capabilities powered by AI.

MCP based AI Assistant

This is an MCP based AI Assistant built using Groq, LangChain, and MCP, with a Streamlit frontend and FastAPI backend. It enables chat interactions powered by large language models, enhanced with optional tools like search agents and browser automation.

Features

• Groq LLM with LangChain and memory enabled conversation
• MCP server such as Google Search and Playwright integration
• FastAPI backend to handle chat API requests
• Streamlit frontend for real-time chat
• Utilized Docker for containerized deployment

Project Structure

MCP_Assistant/
├── app.py 
├── main.py # FastAPI server with chat endpoint
├── streamlit_app.py # streamlit frontend UI
├── browser_mcp.json # MCP servers
├── .env # Environment variables (API keys)
├── requirements.txt # required dependencies
└── README.md # You are here!

Setup Instructions

1. Clone and Install Dependencies

git clone https://github.com/itsabhishekm/MCP_Assistant.git
cd MCP_Assistant
python -m venv .venv
.venv\Scripts\activate   
pip install -r requirements.txt

2. Set Environment Variables

In the .env file in the root past your groq API KEY:
GROQ_API_KEY=your_groq_api_key

3. Configure MCP Tools (Not Required but optional)

If you want to add any other MCP server customize browser_mcp.json:

  "mcpServers": {
    "google-search": {
      "command": "npx",
      "args": ["-y", "@mcp-server/google-search-mcp@latest"]
    },
    "playwright": {
      "command": "npx",
      "args": ["@playwright/mcp@latest"]
    }
  }
}

Or if you don’t want any MCP server, just leave it empty:

{ "mcpServers": {} }

Running the app

1. Start FastAPI Backend

uvicorn main:app --reload --port 8000

2. Launch Streamlit Frontend

streamlit run streamlit_app.py
Visit: http://localhost:8501