🌦️ Weather MCP RAG Assistant

An AI-powered weather assistant built using Model Context Protocol (MCP), Retrieval-Augmented Generation (RAG), LangChain, ChromaDB, Groq LLM, and OpenWeather API.

The project exposes a custom MCP tool that retrieves real-time weather data and combines it with a RAG pipeline to generate contextual, natural-language weather insights.

🚀 Key Features

• Custom MCP server built using FastMCP

• Real-time weather retrieval using OpenWeather API

• Retrieval-Augmented Generation (RAG) pipeline for contextual responses

• ChromaDB vector store for storing and retrieving weather context

• Sentence Transformer embeddings using all-MiniLM-L6-v2

• Groq-hosted LLM integration through LangChain

• MCP tool integration with VS Code / GitHub Copilot

• Supports dynamic weather queries for different cities

• Secure API-key management using environment variables

Related MCP server: Weather MCP Server

🧠 How It Works

User Query
    ↓
GitHub Copilot / MCP Client
    ↓
Weather MCP Server
    ↓
get_weather(location)
    ↓
OpenWeather API
    ↓
Weather Data Processing
    ↓
ChromaDB Vector Store
    ↓
RAG Retrieval
    ↓
Groq LLM
    ↓
Context-Aware Weather Response

🛠️ Tech Stack

Language: Python

AI / GenAI: LLMs, RAG, Prompt Engineering, Sentence Transformers

Frameworks: LangChain, FastMCP

Vector Database: ChromaDB

LLM Provider: Groq

External API: OpenWeather API

Protocol: Model Context Protocol (MCP)

Development Environment: VS Code, Git, GitHub

📂 Project Structure

Weather-MCP-RAG-Assistant/
│
├── rag/
│   ├── embedding.py
│   ├── llm.py
│   ├── retriever.py
│   └── vector_store.py
│
├── services/
│   └── weather_service.py
│
├── utils/
│   └── parser.py
│
├── config.py
├── server.py
├── requirements.txt
├── env.example
├── .gitignore
└── README.md

⚙️ Setup

1. Clone the repository

git clone https://github.com/rudraniai/Weather-MCP-RAG-Assistant.git
cd Weather-MCP-RAG-Assistant

2. Create a virtual environment

python -m venv .venv

Activate it on Windows:

.venv\Scripts\activate

3. Install dependencies

pip install -r requirements.txt

4. Configure environment variables

Copy the example environment file:

copy env.example .env

Add your credentials to .env:

OPENWEATHER_API_KEY=your_openweather_api_key
GROQ_API_KEY=your_groq_api_key
GROQ_MODEL=your_groq_model
EMBEDDING_MODEL=sentence-transformers/all-MiniLM-L6-v2
CHROMA_DB_DIR=./chroma_db

API credentials are kept outside version control using .gitignore.

5. Run the MCP server

python server.py

🔌 MCP Configuration

Configure the local MCP server in VS Code using:

{
  "servers": {
    "weather-mcp": {
      "type": "stdio",
      "command": "python",
      "args": [
        "PATH_TO_PROJECT/server.py"
      ]
    }
  },
  "inputs": []
}

Replace PATH_TO_PROJECT with the local path to server.py.

After configuration:

1. Open the VS Code Command Palette.

2. Select MCP: List Servers.

3. Select weather-mcp.

4. Start the MCP server.

5. Open GitHub Copilot Chat and allow the MCP tool when requested.

🎥 Project Demo

The following demo shows the custom weather-mcp MCP tool being invoked through GitHub Copilot to retrieve and generate contextual weather information.

💬 Example Usage

Ask Copilot:

Use the weather-mcp MCP tool to get the weather in Pune.

Other examples:

Use the weather-mcp MCP tool to get the weather in Mumbai.

Use the weather-mcp MCP tool to get the weather in Nagpur.

Example information returned by the assistant includes:

• Current temperature

• Feels-like temperature

• Humidity

• Wind speed and direction

• Cloud conditions

• Weather summary

• Air-quality information when available

🔐 Security

Sensitive credentials such as API keys are stored in a local .env file.

The following files/directories are excluded from Git:

.env
chroma_db/
__pycache__/
.vscode/

Never commit real API keys to the repository.

📌 What I Learned

Through this project, I gained hands-on experience with:

• Building and exposing tools through Model Context Protocol (MCP)

• Integrating external APIs with LLM-based applications

• Implementing a RAG pipeline

• Working with embeddings and vector databases

• Using ChromaDB for contextual retrieval

• Connecting LangChain with a hosted LLM

• Integrating a custom MCP server with GitHub Copilot

• Managing environment variables and API credentials securely

🔮 Future Improvements

• Add multi-day weather forecasting

• Add weather alerts and recommendations

• Improve retrieval and contextual memory

• Add additional weather and environmental data sources

• Build a standalone web interface using Streamlit or FastAPI

• Containerize the application using Docker

👩‍💻 Author

Rudrani Gulhane

B.Tech Computer Science Engineering — Artificial Intelligence & Machine Learning

Interested in AI/ML, Generative AI, RAG, LLMs, AI Agents, and MCP-based applications.

📄 License

This project is intended for educational, portfolio, and experimental use.