Read file: pyproject.toml Ran tool Read file: mcpserver/requirements.txt Here’s a brief and clear README for your project, covering the MCP server, usage, and Docker integration:
MCP Crash Course
This project demonstrates how to build and interact with an MCP (Model Context Protocol) server using Python, with support for weather alerts and conversational agents. It also includes Docker support for easy deployment.
Features
-
Weather Alerts Tool:
Get real-time weather alerts for any US state using the National Weather Service API. -
Conversational Agent:
Useslangchain-groqand MCP tools to enable chat-based interactions with memory.Model Used:
llama-3.3-70b-versatile(via ChatGroq) -
MCP Server & Client:
Includes both server and client implementations for tool invocation and chat. -
Docker Support:
Easily build and run the server in a containerized environment.
Project Structure
.
├── main.py
├── pyproject.toml
├── README.md
├── server/
│ ├── weather.py # Weather tool implementation
│ ├── client.py # Chat client using MCPAgent
│ └── weather.json # MCP tool configuration
├── mcpserver/
│ ├── server.py # Example MCP server
│ ├── client-stdio.py # Example stdio client
│ ├── client-sse.py # Example SSE client
│ ├── Dockerfile # Docker setup for server
│ └── requirements.txt # Minimal requirements for Docker
└── .venv/ # Virtual environment (ignored)
Getting Started
1. Clone the repository
git clone https://github.com/Muhammadaffan05/MCP.git
cd MCP
2. Set up a virtual environment (recommended)
uv venv --python=python3.13
source .venv/bin/activate
uv add -r pyproject.toml
3. Configure Environment Variables
Create a .env file in the root or set the following in your environment:
GROQ_API_KEY(for language model access)
4. Run the MCP Server
python mcpserver/server.py
5. Run the Client
python server/client.py
Or try the stdio client:
python mcpserver/client-stdio.py
Docker Usage
Build and run the server in Docker:
cd mcpserver
docker build -t mcp-server .
docker run -p 8000:8000 mcp-server
- The Dockerfile uses Python 3.11, installs dependencies with
uv, and runs the MCP server.
Dependencies
- Python 3.13+ (or 3.11+ for Docker)
- httpx
- langchain-groq
- mcp-use
- mcp[cli]
- nest-asyncio
All dependencies are managed via pyproject.toml and uv.
Notes
.venv/,__pycache__/, and other generated files are gitignored.- For best results, use the latest stable Python version.
- Make sure to set up your API keys and environment variables as needed.
Let me know if you want this saved to your README.md or need any changes!