MCP Crash Course
A Python-based MCP (Model Context Protocol) server that enables weather alerts and conversational AI interactions, with Docker support for easy deployment.
What is MCP Crash Course?
MCP Crash Course is a Model Context Protocol (MCP) server that allows AI assistants like Claude, Cursor, and VS Code to python-based mcp (model context protocol) server that enables weather alerts and conversational ai interactions, with docker support for easy deployment.
A Python-based MCP (Model Context Protocol) server that enables weather alerts and conversational AI interactions, with Docker support for easy deployment.
This server falls under the Developer Tools category on MCPgee, the world's largest MCP server directory with 33,000+ servers.
Features
- A Python-based MCP (Model Context Protocol) server that enab
Use Cases
Maintainer
Works with
Installation
Manual Installation
npx mcp-crash-courseConfiguration
Configuration Details
claude_desktop_config.json
Performance
Response Metrics
Resource Usage
How to Set Up and Use MCP Crash Course
MCP Crash Course is a Python-based educational MCP server project that demonstrates how to build an MCP server from scratch, featuring a weather alerts tool backed by the free US National Weather Service API and a conversational AI agent powered by LangChain and the Groq-hosted Llama 3.3 70B model. It includes both a stdio and an HTTP-mode client, plus a full Docker setup for containerized deployment. The project is designed for developers who want a hands-on, runnable example of MCP server and client patterns before building their own integrations.
Prerequisites
- Python 3.11 or newer (Python 3.13 recommended for local development)
- A Groq API key (free at console.groq.com) for the conversational AI agent
- Docker and Docker Compose if using the containerized deployment path
- uv package manager (pip install uv) for local dependency management
- An MCP-capable client or Claude Desktop for testing the server as an MCP tool provider
Clone the repository
Clone the MCP Crash Course repository to your local machine.
git clone https://github.com/Muhammadaffan05/MCP.git
cd MCPCreate a virtual environment and install dependencies
Use uv to create an isolated Python 3.13 environment and install all required packages from pyproject.toml.
uv venv --python=python3.13
source .venv/bin/activate
uv add -r pyproject.tomlSet the required environment variable
Export your Groq API key so the conversational agent can connect to the Llama 3.3 70B model.
export GROQ_API_KEY=your_groq_api_key_hereStart the MCP server
Run the MCP server using Python. It exposes the weather alerts tool and the conversational AI capabilities.
python mcpserver/server.pyLaunch the chat client to interact with the server
In a second terminal, start the chat client which connects to the running MCP server and enables conversation with tool invocation.
python server/client.py
# Or for stdio mode:
python mcpserver/client-stdio.pyAlternative: Run with Docker
Build and run the server in a Docker container if you prefer a containerized setup.
cd mcpserver
docker build -t mcp-server .
docker run -p 8000:8000 -e GROQ_API_KEY=$GROQ_API_KEY mcp-serverMCP Crash Course Examples
Client configuration (Claude Desktop)
Configure Claude Desktop to use this Python MCP server in stdio mode.
{
"mcpServers": {
"mcp-crash-course": {
"command": "python",
"args": ["/path/to/MCP/mcpserver/server.py"],
"env": {
"GROQ_API_KEY": "your_groq_api_key_here"
}
}
}
}Prompts to try
These prompts exercise the weather alerts tool and conversational AI capabilities of the server.
- "Get current weather alerts for California"
- "Are there any active weather alerts in Texas?"
- "What weather warnings are in effect for Florida right now?"
- "Show me all active National Weather Service alerts for New York"Troubleshooting MCP Crash Course
ImportError or missing module when starting the server
Make sure you activated the virtual environment ('source .venv/bin/activate') and ran 'uv add -r pyproject.toml'. The server requires httpx, langchain-groq, mcp[cli], and nest-asyncio.
Groq API errors or model not found responses
Verify that GROQ_API_KEY is exported in your shell. The server uses 'llama-3.3-70b-versatile' via ChatGroq — confirm your Groq account has access to this model at console.groq.com.
Weather alerts tool returns no data
The tool uses the free US National Weather Service API (api.weather.gov) which only covers US states. Pass a two-letter US state code (e.g., 'CA', 'TX') rather than a city or country name.
Frequently Asked Questions about MCP Crash Course
What is MCP Crash Course?
MCP Crash Course is a Model Context Protocol (MCP) server that python-based mcp (model context protocol) server that enables weather alerts and conversational ai interactions, with docker support for easy deployment. It connects AI assistants to external tools and data sources through a standardized interface.
How do I install MCP Crash Course?
Follow the installation instructions on the MCP Crash Course GitHub repository. Clone the repo, install dependencies, and add the server config to your AI client.
Which AI clients work with MCP Crash Course?
MCP Crash Course works with all major MCP-compatible AI clients including Claude Desktop, Claude Code, Cursor, VS Code (GitHub Copilot), Windsurf, and Cline.
Is MCP Crash Course free to use?
Yes, MCP Crash Course is open source and available under the Apache-2.0 license. You can use it freely in both personal and commercial projects.
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