Server Practice

A simple practice project demonstrating how to build and use Model Context Protocol (MCP) servers with Python, LangGraph, and Groq.

Overview

This project contains:

• A Math MCP Server using the stdio transport.
• A Weather MCP Server using the streamable-http transport.
• A LangGraph ReAct agent that connects to multiple MCP servers using MultiServerMCPClient.

Project Structure

.
├── client.py
├── mathserver.py
├── weatherserver.py
├── .env
├── requirements.txt
└── README.md

Features

Math Server (stdio)

Provides the following tools:

• add(a, b)
• multiply(a, b)

Weather Server (streamable-http)

Provides the following tool:

• get_weather(location)

Currently returns a mock weather response.

Client

The client:

• Connects to multiple MCP servers.
• Automatically discovers available tools.
• Uses a Groq LLM with LangGraph's ReAct agent.
• Selects and invokes the appropriate tool based on the user's query.

Tech Stack

• Python
• MCP (Model Context Protocol)
• LangGraph
• LangChain MCP Adapters
• Groq
• python-dotenv

Installation

Clone the repository:

git clone https://github.com/shreenithi23/mcp-server-practice.git
cd mcp-server-practice

Create a virtual environment:

python -m venv .venv

Activate it:

macOS/Linux

source .venv/bin/activate

Windows

.venv\Scripts\activate

Install the required packages:

pip install -r requirements.txt

Environment Variables

Create a .env file:

GROQ_API_KEY=your_groq_api_key

Running the Project

1. Start the Weather Server

python weatherserver.py

The Math server is automatically launched by the client using the stdio transport.

2. Run the Client

python client.py

Example Queries

What's (3 + 5) x 12?

What's the weather in California?

Learning Objectives

This project demonstrates:

• Building MCP servers using FastMCP
• Exposing Python functions as MCP tools
• Using different MCP transports (stdio and streamable-http)
• Connecting multiple MCP servers with MultiServerMCPClient
• Creating an AI agent with LangGraph's ReAct agent
• Integrating Groq LLMs with MCP

Notes

• The Weather server currently returns mock weather data.
• The Math server is started automatically by the client.
• Store API keys in a .env file.
• Do not commit .env to GitHub.

License

This project is intended for learning and experimentation with the Model Context Protocol (MCP).