MCP Weather Application (Client-Server)

What is MCP Weather Application (Client-Server)?

The MCP Weather Application is a client-server system built with the MCP framework. The server fetches weather data from the National Weather Service (NWS) API via two tools (get_alerts, get_forecast), while the client uses LangChain with the Groq API (Llama 3 model) to process natural language queries and invoke those tools as needed.

How to use MCP Weather Application (Client-Server)?

Detailed setup instructions, including dependency installation and API key configuration, are in each component’s README (weather/README.md, mcp-client/README.md). To run the client and automatically start the server, execute uv run client.py ../weather/weather.py from the mcp-client/ directory. The client connects to the server via stdio.

Key features of MCP Weather Application (Client-Server)

• Two MCP tools: get_alerts and get_forecast
• Fetches live data from the National Weather Service API
• Client uses LangChain and Groq for natural language understanding
• Full client-server architecture with stdio MCP transport
• Automatically selects and invokes the correct weather tool

Use cases of MCP Weather Application (Client-Server)

• Ask for current weather alerts for a given US state
• Request a detailed forecast for a specific latitude/longitude
• Use natural language to get weather information without coding
• Demonstrate MCP client-server interaction with an LLM orchestrator

FAQ from MCP Weather Application (Client-Server)

What tools does the server provide?

The server exposes two tools: get_alerts (for weather alerts by state) and get_forecast (for a forecast at given coordinates).

Which API does the server use?

It uses the National Weather Service (NWS) API to fetch weather alerts and forecasts.

What LLM powers the client’s natural language understanding?

The client uses Groq’s Llama 3 model via LangChain to interpret user queries and decide when to call a server tool.

How does