Build Agentic AI And Gen AI Agents With MCP

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10 Python AI/ML libraries

• 🔢 NumPy 👉 🌐Official Website 📘 Documentation

• 🐼 Pandas 👉 🌐Official Website 📘 Documentation

• 📊 Scikit-Learn 👉 🌐Official Website 📘 Documentation

• 🚀 XGBoost 👉 🌐Official Website 📘 Documentation

• ⚡ LightGBM 👉 🌐Official Website 📘 Documentation

• 🧠 TensorFlow 👉 🌐Official Website 📘 Documentation

• 🎯 Keras 👉 🌐Official Website 📘 Documentation

• 🔥 PyTorch 👉 🌐Official Website 📘 Documentation

• 🤖 Transformers (Hugging Face) 👉 🌐Official Website 📘 Documentation

• 🧩 spaCy 👉 🌐Official Website 📘 Documentation

• Complete Agentic AI Course In 10 Hours- Langchain, Langgraph, RAG,Vectorless RAG, Guardrails,Evals : https://www.youtube.com/watch?v=rV3HJ4LEZ7k

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Build Agentic AI and Gen AI Agents with MCP

• Model Context Protocol (MCP) Bootcamp offers a deep dive into MCP architecture and its role in the Agentic AI ecosystem. Learn to build real-world, production-ready AI workflows using MCP with LangChain, LangGraph, and CrewAI through fully practical, project-based implementations.
• https://fastmcp.cloud/

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Table of contents - Live Demo & Topics in Details Coming Soon

Section 1 Model Context Protocol

Section 2 Getting Started With Claude Desktop And Cursor IDE

Section 3 Cursor IDE MCP Server Setup

Section 4 How to build Your Own MCP Client using Python and Google Gemini API

Section 5 How to build Docker MCP Server

Section 6 LangChain MCP Client using LangChain MCP Adapters

Section 7 MCP Client with Multiple Server Support

Section 8 MCP Server and Client using SSE

Section 9 Deploying MCP Server to AWS Cloud Platform

Section 10 Real Time Weather Agent using MCP and MCP Inspector

Section 11 Real Time Job Recommendation System

Section 12 StoryForge Agent

Section 13 Clinisight AI

Section 14 Build Agent with Google Development Kit ADK

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Model Context Protocol (MCP) – The USB-C for AI Applications

<img width="1121" height="1343" alt="image" src="https://github.com/user-attachments/assets/eae5ab87-7147-46a6-8f49-ad8be55a1c95" /> <img width="1069" height="1307" alt="image" src="https://github.com/user-attachments/assets/2f0ed634-5b3d-4c36-8ee0-4aa11834a7df" />

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Just explored an incredible book on Model Context Protocol (MCP)

— and honestly, it completely reshaped how I think about the future of Agentic AI.

• For years, AI systems have been powerful individually, but fragmented when it comes to collaboration, context-sharing, scalability, and orchestration.

• MCP changes that.

• This book explains how MCP is becoming the foundational communication layer for next-generation AI ecosystems — enabling AI agents, tools, servers, and workflows to operate with shared context, adaptive intelligence, modularity, and secure multi-agent coordination.

Why MCP matters for the future of AI:

<img width="1054" height="1048" alt="image" src="https://github.com/user-attachments/assets/f32597dd-e030-4eab-a745-810228bfb57b" /> <img width="1074" height="1006" alt="image" src="https://github.com/user-attachments/assets/f65a3426-c364-4931-9522-5b934d4c5309" />

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📜 License

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

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📞 CONTACT & NETWORKING 📞

💼 Professional Networks

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

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

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📊 GitHub Stats & Metrics 📊

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