🌦️ MCP Weather Assistant

A Model Context Protocol (MCP) project that connects a Groq-hosted LLM to a custom Weather MCP Server, allowing you to ask natural-language questions about weather and get real-time, tool-grounded answers sourced from the National Weather Service (NWS) API.

How It Works

User
  ↓
"Are there any weather alerts for CA?"
  ↓
Groq LLM
  ↓
Decides to call get_alerts_tool
  ↓
MCP Client
  ↓
MCP Weather Server
  ↓
National Weather Service API
  ↓
Real-time weather alerts
  ↓
Groq LLM
  ↓
Natural-language answer

1. The user asks a question in plain English.

2. The Groq LLM interprets the question and decides which tool to call (e.g. get_alerts_tool, get_forecast_tool).

3. The MCP Client sends that tool call to the MCP Weather Server over the Model Context Protocol.

4. The server queries the National Weather Service API for live data.

5. Results flow back through the client to the LLM.

6. The LLM turns the raw data into a clear, natural-language response for the user.

Related MCP server: mcp-weather

Features

• 🌦️ Real-time weather alerts by U.S. state

• 📍 Weather forecasts by location (lat/long)

• 🔌 Built on the Model Context Protocol (MCP) — tools are exposed in a standardized way any MCP-compatible client can use

• ⚡ Powered by Groq for fast LLM inference

• 🧩 Clean separation between the LLM client and the weather tool server

Project Structure

.
├── client/            # MCP client — connects to Groq LLM and the MCP server
├── server/             # MCP weather server — exposes get_alerts_tool, get_forecast_tool, etc.
├── requirements.txt     # or package.json, depending on stack
└── README.md

(Adjust this tree to match your actual folder layout.)

Prerequisites

• Python 3.10+ (or Node.js, depending on your implementation)

• A Groq API key

• Internet access (the server calls the public NWS API — no API key required for NWS)

Installation

git clone https://github.com/Krishna-Dhawangale/MCP.git
cd MCP
pip install -r requirements.txt

Configuration

Create a .env file in the project root:

GROQ_API_KEY=your_groq_api_key_here

Usage

1. Start the MCP weather server:

   python server/weather_server.py

2. Run the client:

   python client/main.py

3. Ask a question:

   > Are there any weather alerts for CA?

   The client will route the query to the LLM, which calls the appropriate tool on the MCP server and returns a natural-language answer.

Available Tools

Roadmap

• Add support for more locations (international)

• Add caching for repeated queries

• Add unit tests for tool handlers

• Support additional MCP clients beyond Groq

Contributing

Contributions are welcome! Feel free to open an issue or submit a pull request.