This hands-on course introduces the Model Context Protocol (MCP) and demonstrates how secure AI agent workflows can connect large language models(LLMs) with external tools, systems, and datasources. Through practical labs, you’ll learn howMCP enables structured, context-aware interactionswhile maintaining security, transparency, and user control.
You’ll create MCP servers with FastMCP, configure prompts and resources, and develop MCP clients that communicate through STDIO and Streamable HTTP. The course also explores how MCP supports retrieval-augmented generation (RAG), multi-agent orchestration, and structured AI reasoning workflows.
In addition, you’ll work with security-focused concepts including permission-based execution, JSON-schema elicitation, auditing strategies, and approval workflows. You’ll examine real-world scenarios to understand how safe execution patterns reduce operational risks and improve trust in AI systems.
By completing the course, you’ll gain practical experience building and testing secure MCP-driven AI workflows that integrate usability, automation, and responsible AI design principles.
