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MCP verified MIT Self-run

Weather Mcp A2a

mcp-ratnesh-181998-weather-mcp-a2a · by Ratnesh-181998

MCP-based agentic AI system enabling LLMs to fetch and reason over real-time global weather data using standardized tool invocation. Built with MCP Server/Client, Llama 3, and external APIs to deliver low-latency, hallucination-free weather intelligence through protocol-driven workflows.

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Install

$ agentstack add mcp-ratnesh-181998-weather-mcp-a2a

✓ scanned · ✓ verified — works with Claude Code, Cursor, and more.

Security review

✓ Passed

No issues found. Passed automated security review. · v0.1.0 How review works →

  • ✓ Prompt-injection patterns
  • ✓ Secret / credential exfiltration
  • ✓ Dangerous shell & filesystem operations
  • ✓ Untrusted network calls
  • ✓ Known-malicious package signatures

What it can access

  • ✓ Network access No
  • ✓ Filesystem access No
  • ✓ Shell / process execution No
  • ● Environment & secrets Used
  • ✓ Dynamic code execution No

From automated source analysis of v0.1.0. “Used” means the capability is present in the source — more access means more to trust, not that it’s unsafe.

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About

🌤️ 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

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

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](LICENSE) file for details.

📞 Contact & Community

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

Project Links

📜 License

Licensed under the MIT License - Feel free to fork and build upon this innovation! 🚀

📞 CONTACT & NETWORKING 📞

💼 Professional Networks

[](https://www.linkedin.com/in/ratneshkumar1998/) [](https://github.com/Ratnesh-181998) [](https://x.com/RatneshS16497) [](https://share.streamlit.io/user/ratnesh-181998) [](mailto:rattudacsit2021gate@gmail.com) [](https://medium.com/@rattudacsit2021gate) [](https://stackoverflow.com/users/32068937/ratnesh-kumar)

🚀 AI/ML & Data Science AI/ML 1620+ Problem Solved

[](https://share.streamlit.io/user/ratnesh-181998) [](https://huggingface.co/RattuDa98) [](https://www.kaggle.com/rattuda)

💻 Competitive Programming Including all coding plateform's 5000+ Problems/Questions solved

[](https://leetcode.com/u/Ratnesh1998/) [](https://www.hackerrank.com/profile/rattudacsit20211) [](https://www.codechef.com/users/ratnesh181998) [](https://codeforces.com/profile/Ratnesh181998) [](https://www.geeksforgeeks.org/profile/ratnesh1998) [](https://www.hackerearth.com/@ratnesh138/) [](https://www.interviewbit.com/profile/rattudacsit2021gated9a25bc44230/)

📊 GitHub Stats & Metrics 📊

Source & license

This open-source MCP server is cataloged on AgentStack and links to its original source — we do not rehost the code.

Install and usage instructions live in the source repository linked above.

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Versions

  • v0.1.0 Imported from the upstream source.

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