Mastering MCP: Building Advanced Agentic Applications
The advanced MCP course teaches you to build agentic apps, integrate LlamaIndex, ensure observability, deploy multi-server systems, and create an “Image Research Assistant.”
4.5
19 Lessons
7h
Updated 3 weeks ago
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This course teaches you how to use the Model Context Protocol (MCP) to build real-world AI applications. You’ll explore the evolution of agentic AI, why LLMs need supporting systems, and how MCP works, from its architecture and life cycle to its communication protocols.
You’ll build both single- and multi-server setups through hands-on projects like a weather assistant, learning to structure prompts and connect resources for context-aware systems. You’ll also extend the MCP application to integrate external frameworks like LlamaIndex and implement RAG for advanced agent behavior.
The course covers observability essentials, including MCP authorization, authentication, logging, and debugging, to prepare your systems for production. It concludes with a capstone project where you’ll design and build a complete “Image Research Assistant,” a multimodal application that combines vision and research capabilities through a fully interactive web interface.
This course teaches you how to use the Model Context Protocol (MCP) to build real-world AI applications. You’ll explore the evol...Show More
WHAT YOU'LL LEARN
An understanding of the evolution from standalone LLMs to agentic AI and the need for Model Context Protocol
Comprehensive knowledge of MCP architecture, life cycle, and communication protocols
The ability to design and implement single-server MCP architectures, including prompt and resource integration, for context-aware AI
Proficiency in building and configuring modular multi-server MCP architectures for enhanced AI capabilities
Hands-on experience extending the MCP agent capabilities through RAG server implementation and integration with LlamaIndex
Practical knowledge of implementing authorization, authentication, logging, and debugging within MCP for robust AI systems
The skills to design, develop, and deploy a complete multimodal AI application, such as an “Image Research Assistant,” using MCP
An understanding of the evolution from standalone LLMs to agentic AI and the need for Model Context Protocol
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