MCP Across AI Frameworks & Platforms: LangChain, LlamaIndex, OpenAI SDK, Google ADK, Claude, Azure OpenAI & Gemini
Description:
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.
Course Content
- MCP Introduction –> 12 lectures • 49min.
- MCP Architecture and Components –> 7 lectures • 28min.
- Understand MCP using Claude Desktop –> 8 lectures • 30min.
- Working on the Pre-reqs for Future Lab Exercises –> 13 lectures • 25min.
- Langchain & MCP –> 9 lectures • 42min.
- LlamaIndex & MCP –> 10 lectures • 43min.
- OpenAI Agents SDK & MCP –> 9 lectures • 30min.
- Google ADK & Paypal MCP Server –> 8 lectures • 24min.
- New Content –> 1 lecture • 1min.
Requirements
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 Python
- Developers and engineers interested in cross-platform AI design
- Product teams building memory-aware assistants or RAG systems
- AI enthusiasts exploring advanced prompting, agents, and context management
By 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.