Used for storing API keys and configuration settings for the MCP server
Enables the creation of multi-tool AI assistants using LangChain MCP Adapters to connect tools and services
Integrates with the system to create conversational AI agents that can interact with multiple MCP tools
Click on "Install Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type
@followed by the MCP server name and your instructions, e.g., "@MCP Server with LangChain and AI Toolscalculate 15% of 200 and tell me the weather in Tokyo"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
🧠 MCP Server with LangChain and AI Tools
This project demonstrates how to build a multi-tool AI assistant using the Model Context Protocol (MCP), LangChain, and Groq’s Qwen model. It includes:
📐 A local Math MCP Server
🌤️ A simulated Weather MCP Server
🤖 A conversational AI agent (MCP client) that talks to both
🧰 Features
Uses LangChain MCP Adapters to connect tools
Powered by Groq's Qwen LLM
Handles local and remote tool servers via MCP
Interactive CLI chat with tool usage detection
Related MCP server: MCP Server For Local
🏁 Prerequisites
Python >= 3.11
uvfor project/environment management (https://github.com/astral-sh/uv)Internet connection for loading LLM (Groq)
⚙️ Setup Instructions
1. Create Project
Set Python version in .python-version and pyproject.toml to >=3.11
2. Create Virtual Environment
3. Add Dependencies
Install them
Project Structure
mcp_project/ │ ├── math_server.py # MCP server for math tools ├── weather_server.py # MCP server for weather API simulation ├── client.py # MCP client with AI agent ├── requirements.txt ├── .python-version └── .env # For storing Groq API key (GROQ_API_KEY)