weather-mcp-a2a

# 🌤️ Weather MCP Agent: Global Intelligence System [](https://appudtzei3tyyttd6xjhwur.streamlit.app/) [](https://opensource.org/licenses/MIT) [](https://www.python.org/downloads/) [](https://groq.com/) [](https://modelcontextprotocol.io/) > **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 <img width="1888" height="789" alt="image" src="https://github.com/user-attachments/assets/9c6ee46b-c429-4771-b341-bbe7ea3bc4f5" /> *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). ### 5. 🏗️ 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. #### System Visualization  *Figure 1: High-Level System Architecture bridging User, Agent, and MCP Tools.*  *Figure 2: Detailed Data Flow from User Input to Tool Execution.* ### 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](https://console.groq.com/) (Free) ### 1. Clone the Repository ```bash git clone https://github.com/Ratnesh-181998/weather-mcp-a2a.git cd weather-mcp-a2a ``` ### 2. Set Up Virtual Environment ```bash python -m venv venv # Windows venv\Scripts\activate # Mac/Linux source venv/bin/activate ``` ### 3. Install Dependencies ```bash pip install -r requirements.txt ``` ### 4. Configure Secrets Create a `.env` file in the root directory: ```env GROQ_API_KEY=your_actual_api_key_here ``` ### 5. Run the App ```bash 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. ```bash # 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](LICENSE) file for details. --- ## 📞 Contact & Community **Ratnesh Kumar Singh** *Data Scientist (AI/Ml Engineer 4+ Yrs Exp)* * 💼 **LinkedIn**: [ratneshkumar1998](https://www.linkedin.com/in/ratneshkumar1998/) * 🐙 **GitHub**: [Ratnesh-181998](https://github.com/Ratnesh-181998) * 🌐 **Live Demo**: [Streamlit Cloud App](https://weather-mcp-a2a-agent-to-agent3a95dbsjhgfhd3yussfz3e.streamlit.app/) ### Project Links * 📖 **Documentation**: [GitHub Wiki](https://github.com/Ratnesh-181998/weather-mcp-a2a/wiki) * 🐛 **Issue Tracker**: [GitHub Issues](https://github.com/Ratnesh-181998/weather-mcp-a2a/issues) * 💬 **Discussions**: [GitHub Discussions](https://github.com/Ratnesh-181998/weather-mcp-a2a/discussions) --- <p align="center"> <img src="https://capsule-render.vercel.app/api?type=waving&color=0:00d4ff,100:9b59b6&height=120&section=footer"/> </p>