Mastering MCP: Beginner to Pro Across the AI Ecosystem

LangChain, LlamaIndex, OpenAI SDK, Google ADK, Claude, Azure OpenAI & Gemini, Build your Own MCP Server, Docker , Gradio

What you'll learn
What is Model Context Protocol ?
What is a Model / Context / Protocol ?
History & Transition to MCP
Key Features & Benefits of MCP
A Quick intro to Anthropic & Claude
Why the mention of Anthropic for MCP
How is it trending on Github Stars
Core Architecture of MCP
Key Components- Deep Dive
What is JSON-RPC 2.0 ?
MCP Transport Layer
Transport Mechanism - STDIO vs SSE
What is Claude Desktop
Demo using Claude Desktop
Enable Filesystem MCP Server in Claude
Enable Sqlite MCP Server in Claude
Working on the Pre-reqs for Future Lab Exercises
Langchain-Gemini-Sqlite MCP Server
LlamaIndex-Azure OpenAI-Filesystem MCP
OpenAI SDK & Azure MCP Server
Google ADK & Paypal MCP Server
Build your own MCP Server

Requirements
Basic Understanding of Generative AI
Basic Understanding of Python
Basic Understanding of Large Language Model (LLM)
How to use LLMs with Python
Understanding of Azure / Open AI / Google Gemini
Basic Understanding of AI Agents

Description
Description:Dive into the evolving world of Model Context Protocol (MCP) and learn how to harness its full potential across today's leading AI frameworks and platforms.This course is designed for learners of all levels — whether you're a no-code enthusiast or a Python developer — and takes you on a practical journey from foundational concepts to advanced integrations.You'll explore how MCP is implemented and extended across top-tier AI platforms, gaining real-world experience with tools used by industry leaders.What You'll Learn:The Foundations of Model Context Protocol (MCP): Understand its role in building contextual, memory-aware AI systems.No-Code to Pro-Code: Start with intuitive interfaces and transition into Python-based development.LangChain: Build complex agent workflows and memory chains with MCP integration.LlamaIndex: Implement Retrieval-Augmented Generation (RAG) pipelines for smarter context handling.OpenAI Agents SDK: Use MCP in GPT-powered apps with advanced prompt engineering and session management.Google ADK: Integrate Google’s tools with MCP-based workflows.Claude (Anthropic): Work with high-context, safety-first AI models.Azure OpenAI: Deploy MCP-powered models in enterprise-ready environments.Gemini (Google DeepMind): Explore the cutting edge of multimodal MCP applications.Who This Course is For:No-code builders looking to level up with PythonDevelopers and engineers interested in cross-platform AI designProduct teams building memory-aware assistants or RAG systemsAI enthusiasts exploring advanced prompting, agents, and context managementBy the end of this course, you’ll not only understand the core mechanics of MCP, but also know how to implement it across frameworks to build scalable, contextual, and intelligent AI systems.

Who this course is for:
Beginner to Model Context Protocol - MCP, Understand No-Code & Python Code for each Use Case, No-Code Enthusiasts & Citizen Developers, Machine Learning Engineers & AI Practitioners, Product Managers & Tech Leads