MCP-Use is an open-source library that lets you connect any LLM to any MCP server, giving your agents tool access like web browsing, file operations, and more — all without relying on closed-source clients. In this tutorial, we’ll use langchain-groq and MCP-Use’s built-in conversation memory to build a simple chatbot that can interact with tools via MCP.  Step 1: Setting Up the Environment Installing uv package manager We will first set up our environment and start with installing the uv package manager. For Mac or Linux: curl -LsSf https://astral.sh/uv/install.sh | sh For Windows (PowerShell): powershell -ExecutionPolicy ByPass -c "irm https://astral.sh/uv/install.ps1 | iex" Creating a new directory and activating a virtual environment We will then create a new project directory and initialize it with uv uv init mcp-use-demo cd mcp-use-demo We can now create and activate a virtual environment. For Mac or Linux: uv venv source .venv/bin/activate For Windows: uv venv .venv\Scripts\activate Installing Python dependencies We will now install the required dependencies uv add mcp-use langchain-groq python-dotenv Step 2: Setting Up the Environment Variables Groq API Key To use Groq’s LLMs: Visit Groq Console and generate an API key. Create a .env file in your project directory and add the following line: GROQ_API_KEY=<YOUR_API_KEY>  Replace <YOUR_API_KEY> with the key you just generated. Brave Search API Key This tutorial uses the Brave Search MCP Server. Get your Brave Search API key from: Brave Search API Create a file named mcp.json in the project root with the following content: { "mcpServers": { "brave-search": { "command": "npx", "args": [ "-y", "@modelcontextprotocol/server-brave-search" ], "env": { "BRAVE_API_KEY": "<YOUR_BRAVE_SEARCH_API>" } } } } Replace <YOUR_BRAVE_SEARCH_API> with your actual Brave API key. Node JS Some MCP servers (including Brave Search) require npx, which comes with Node.js. Download the latest version of Node.js from nodejs.org Run the installer. Leave all settings as default and complete the installation Using other servers If you’d like to use a different MCP server, simply replace the contents of mcp.json with the configuration for that server. Step 3: Implementing the chatbot and integrating the MCP server Create an app.py file in the directory and add the following content: Importing the libraries from dotenv import load_dotenv from langchain_groq import ChatGroq from mcp_use import MCPAgent, MCPClient import os import sys import warnings warnings.filterwarnings("ignore", category=ResourceWarning) This section loads environment variables and imports required modules for LangChain, MCP-Use, and Groq. It also suppresses ResourceWarning for cleaner output. Setting up the chatbot async def run_chatbot(): """ Running a chat using MCPAgent's built in conversation memory """ load_dotenv() os.environ["GROQ_API_KEY"] = os.getenv("GROQ_API_KEY") configFile = "mcp.json" print("Starting chatbot...") # Creating MCP client and LLM instance client = MCPClient.from_config_file(configFile) llm = ChatGroq(model="llama-3.1-8b-instant") # Creating an agent with memory enabled agent = MCPAgent( llm=llm, client=client, max_steps=15, memory_enabled=True, verbose=False ) This section loads the Groq API key from the .env file and initializes the MCP client using the configuration provided in mcp.json. It then sets up the LangChain Groq LLM and creates a memory-enabled agent to handle conversations. Implementing the chatbot # Add this in the run_chatbot function print("\n-----Interactive MCP Chat----") print("Type 'exit' or 'quit' to end the conversation") print("Type 'clear' to clear conversation history") try: while True: user_input = input("\nYou: ") if user_input.lower() in ["exit", "quit"]: print("Ending conversation....") break if user_input.lower() == "clear": agent.clear_conversation_history() print("Conversation history cleared....") continue print("\nAssistant: ", end="", flush=True) try: response = await agent.run(user_input) print(response) except Exception as e: print(f"\nError: {e}") finally: if client and client.sessions: await client.close_all_sessions() This section enables interactive chatting, allowing the user to input queries and receive responses from the assistant. It also supports clearing the chat history when requested. The assistant’s responses are displayed in real-time, and the code ensures that all MCP sessions are closed cleanly when the conversation ends or is interrupted. Running the app if __name__ == "__main__": import asyncio try: asyncio.run(run_chatbot()) except KeyboardInterrupt: print("Session interrupted. Goodbye!") finally: sys.stderr = open(os.devnull, "w") This section runs the asynchronous chatbot loop, managing continuous interaction with the user. It also handles keyboard interruptions gracefully, ensuring the program exits without errors when the user terminates the session. You can find the entire code here Step 4: Running the app To run the app, run the following command uv run app.py This will start the app, and you can interact with the chatbot and use the server for the session Arham IslamI am a Civil Engineering Graduate (2022) from Jamia Millia Islamia, New Delhi, and I have a keen interest in Data Science, especially Neural Networks and their application in various areas.Arham Islamhttps://www.marktechpost.com/author/arhamislam/Implementing an AgentQL Model Context Protocol (MCP) ServerArham Islamhttps://www.marktechpost.com/author/arhamislam/Implementing An Airbnb and Excel MCP ServerArham Islamhttps://www.marktechpost.com/author/arhamislam/How to Create a Custom Model Context Protocol (MCP) Client Using GeminiArham Islamhttps://www.marktechpost.com/author/arhamislam/Implementing Persistent Memory Using a Local Knowledge Graph in Claude Desktop