This project demonstrates a simple client-server implementation using the Model Context Protocol (MCP), which is a standardized way to connect large language models with tools and data.

This example shows how to:

• Create an MCP server with custom tools
• Connect to the server using an MCP client
• Call tools and get responses from the server

Click the image above to watch a tutorial on MCP implementation.

.
├── pyproject.toml
├── README.md
├── src
│   ├── client
│   │   └── mcp_client.py      # MCP client implementation
│   └── server
│       └── example_server.py  # MCP server with tools
└── uv.lock

The server exposes two tools:

1. calculate_bmi - A simple calculator that computes Body Mass Index
2. fetch_weather - An async tool that retrieves weather data from an external API

The client connects to the server via stdio, initializes a session, and calls the server's tools.

• Python 3.9+
• uv (Python package manager)

# Install dependencies
uv install -e .

1. Start the client (which will automatically start the server):

uv run src/client/mcp_client.py

The client will:

1. Connect to the server
2. List available tools
3. Call the BMI calculator with sample data
4. Call the weather tool with sample coordinates

Available tools: meta=None nextCursor=None tools=[...]
BMI calculation result: 22.857142857142858
Weather data: {"current_weather":{"temperature":14.2,"windspeed":12.6, ...}}

( run command below and then visit http://localhost:5173 )

  ❯ mcp dev src/server/example_server.py
Starting MCP inspector...
Proxy server listening on port 3000

🔍 MCP Inspector is up and running at http://localhost:5173 🚀
New SSE connection
Query parameters: {
  transportType: 'stdio',
  command: 'uv',
  args: 'run --with mcp mcp run src/server/example_server.py',

This project uses:

• Model Context Protocol Python SDK
• MCP Official Documentation

This project is licensed under the MIT License - see the LICENSE file for details.