🌤️ Weather MCP Agent: Global Intelligence System

Next-Generation Agentic AI powered by the Model Context Protocol (MCP) and Llama 3 (Groq). A real-time, multi-modal weather intelligence system that bridges the gap between Large Language Models and deterministic data tools.

🚀 Overview

The Weather MCP Agent is a state-of-the-art implementation of the Model Context Protocol (MCP), designed to demonstrate the future of AI interoperability. Unlike traditional chatbots that hallucinate data, this agent uses a standardized protocol to "connect" to live tools—fetching real-time weather forecasts, alerts, and atmospheric analytics for any city on Earth.

Built on Streamlit for a reactive UI and powered by Groq's LPU for near-instant inference, this system showcases how Agentic AI can orchestrate complex workflows (Geocoding -> Weather API -> Conversational Synthesis) in milliseconds.

🌐🎬 Live Demo

🚀 Try it now:

• Streamlit Profile - https://share.streamlit.io/user/ratnesh-181998
• Project Demo - https://weather-mcp-a2a-agent-to-agent3a95dbsjhgfhd3yussfz3e.streamlit.app/

🌟 Key Capabilities

• 🌍 Global Coverage: Instant weather intelligence for 100,000+ cities worldwide.
• ⚡ Hyper-Fast Inference: Uses Llama 3 70B on Groq LPUs for sub-second reasoning.
• 🔌 Standardized Tooling: Built 100% on the open-source MCP standard.
• 🗣️ Multi-Modal Input: Supports both Text and Voice (WebRTC) interaction.
• 🧠 Smart Context: Maintains conversation history and context-aware responses.

🎮 Interface & Features by Tab

The application is structured into 5 professional modules, each serving a specific purpose in the Agentic workflow:

Live Interface Preview

Figure:High-Definition view of the Smart Weather Dashboard.

1. 🚀 Project Demo (Interactive Core)

The command center of the application.

• AI Chat Interface: Real-time conversation with the Agent.
• Quick Action Grid: One-click execution for 16+ common scenarios.
• Smart City Extraction: NLP-powered logic covers complex queries.
• Voice Input: Speak naturally to the agent.

⚡ See it in Action:

2. ℹ️ About Project (Educational Hub)

A detailed breakdown of the paradigm shift in AI.

• Evolution Timeline: Visualizing the shift from Static LLMs -> Tool-Use Agents -> MCP Ecosystems.
• Protocol Comparison: Why MCP is superior to proprietary plugin architectures.
• Interactive Simulations: step-by-step walkthroughs of the agent's decision-making process.

3. 🛠️ Tech Stack (Under the Hood)

Transparency in engineering.

• AI Core: Llama 3.3 70B (Reasoning), LangChain (Orchestration).
• Frontend: Streamlit Async Runtime, Custom CSS theming.
• Connectivity: mcp-use Client, requests library, RESTful APIs (Open-Meteo, NWS).

4. 🏗️ Architecture (System Design)

Enterprise-grade visualization of the system.

• Data Flow: User -> Streamlit -> Agent -> MCP Client -> Tool -> Response.
• Graphviz Charts: Dynamically generated DAGs (Directed Acyclic Graphs) of the agent's logic.
• Network Topology: Visualizing how the Host, Client, and Server interact.

Detailed Graph Visualization

📂 Detailed Architecture Diagrams

• Diagram 1: Basic MCP Architecture
• Diagram 2: LLM + MCP Tool Selection Flow
• Diagram 3: Multi-Tool MCP Server
• Diagram 4: A2A Multi-Agent Architecture
• Diagram 5: MCP vs A2A Combined System

5. 📋 System Logs (Observability)

Production-ready monitoring.

• Real-time Event Stream: Live tracking of every thought, tool call, and API response.
• Status Codes: Visual indicators for SUCCESS, ERROR, and INFO.
• Audit Trails: Downloadable JSON/TXT logs for debugging and analytics.

🛠️ Technology Stack

| Component | Technology | Purpose | | :--- | :--- | :--- | | Orchestration | LangChain | Manages the ReAct (Reason+Act) loop and prompt engineering. | | Protocol | MCP (Model Context Protocol) | The universal standard for connecting AI models to external tools. | | Inference Engine | Groq LPU | Provides the speed necessary for real-time agentic workflows. | | LLM | Llama 3.3 70B | The "Brain" capable of complex tool selection and JSON parsing. | | Frontend | Streamlit | Delivers a responsive, Python-native web interface. | | Data Source | Open-Meteo API | Provides high-precision weather data without API keys. | | Audio | SpeechRecognition / WebRTC | Handles voice-to-text conversion. |

⚙️ Installation & Local Setup

Follow these steps to run the agent on your local machine.

Prerequisites

• Python 3.10+
• A Groq API Key (Free)

1. Clone the Repository

git clone https://github.com/Ratnesh-181998/weather-mcp-a2a.git
cd weather-mcp-a2a

2. Set Up Virtual Environment

python -m venv venv
# Windows
venv\Scripts\activate
# Mac/Linux
source venv/bin/activate

3. Install Dependencies

pip install -r requirements.txt

4. Configure Secrets

Create a .env file in the root directory:

GROQ_API_KEY=your_actual_api_key_here

5. Run the App

streamlit run Weather_streamlit_app.py

🐳 Large File Support (Git LFS)

This repository may contain large assets (images/diagrams). We use Git LFS to manage them efficiently.

# Install Git LFS
git lfs install

# Track large files
git lfs track "*.png"
git lfs track "*.jpg"

# Push to remote
git add .
git commit -m "Add large visual assets"
git push origin main

🤝 Contributing

Contributions are welcome! Please follow these steps:

1. Fork the repository.
2. Create a feature branch (git checkout -b feature/AmazingFeature).
3. Commit your changes (git commit -m 'Add some AmazingFeature').
4. Push to the branch (git push origin feature/AmazingFeature).
5. Open a Pull Request.

📜 License

This project is licensed under the MIT License - see the LICENSE file for details.

📞 Contact & Community

Ratnesh Kumar Singh
Data Scientist (AI/Ml Engineer 4+ Yrs Exp)

• 💼 LinkedIn: ratneshkumar1998
• 🐙 GitHub: Ratnesh-181998
• 🌐 Live Demo: Streamlit Cloud App

Project Links

• 📖 Documentation: GitHub Wiki
• 🐛 Issue Tracker: GitHub Issues
• 💬 Discussions: GitHub Discussions