Course Overview

Modern AI is evolving fast. Today’s large language models (LLMs) are the engines behind digital agents expected to retrieve live data, automate workflows, and act on a user’s behalf. While the potential is vast, building these agentic systems has proven challenging: connecting LLMs to the world of tools and data has resulted in fragile, bespoke integrations for each use case.

This course provides a deep dive into Model Context Protocol (MCP), a new open standard transforming how LLM-powered agents interact with external systems. You’ll discover why MCP is a game changer for scalable, secure, and future-ready AI applications through a blend of theoretical insights and hands-on implementation. You’ll learn the motivation behind the agentic AI, unpack MCP’s architecture, and develop practical skills for building, deploying, and integrating your own MCP servers and clients—increasingly essential skills in the AI industry.

What will you learn?

Throughout this course, you’ll begin by exploring the fundamental limitations of standalone LLMs, e.g., why even the best models struggle without context and external tool usage. You’ll trace the industry’s journey from simple chatbots to fully agentic systems, discovering how introducing universal protocols like MCP has propelled the field to a tipping point.

You will comprehensively understand MCP’s definition, role within the AI technology stack, and why it is rapidly becoming an industry standard. The course will guide you through the architecture of agentic systems built with MCP, introducing the concepts of host, client, and server, and breaking down their roles through practical, real-world examples.

This hands-on course begins with building your own MCP servers. You will define resources, tools, and prompts, and connect them to AI agents. You will work with real SDKs, experiment with integration patterns, and learn best practices for security, extensibility, and real-world deployments.

By the end, you will have the confidence to design, implement, and extend modular AI agent systems, equipped with skills that are in high demand as MCP adoption accelerates across the industry.

Intended audience

This course is designed for junior to mid-level developers who wish to advance beyond traditional application logic and start building the next generation of AI-powered assistants and automation systems. Whether you are an AI/ML engineer, a backend or platform developer, or simply someone interested in the future of agentic systems, this course will provide theoretical grounding and practical skills to make you effective with MCP. No prior experience with MCP or advanced AI frameworks is necessary; curiosity and a basic comfort with coding are the only prerequisites.

Prerequisites

To get the most from this course, you should possess a working knowledge of, ideally, Python, as most hands-on examples use this language. Familiarity with basic web protocols and APIs will be beneficial, but the course is designed to be accessible even if you are new to agentic AI and cloud integrations.

Course structure

The journey begins with three lessons introducing the rationale for MCP: the limitations of LLMs, the transition to agentic systems, and the problem MCP was designed to solve. The subsequent four practical lessons focus on building MCP servers and clients and connecting agents to real-world tools, data, and platforms.

By the end of the course, you will understand how to design and implement MCP-enabled agentic AI systems and why such architectures are foundational for the next decade of AI innovation. You’ll be prepared to extend existing tools, integrate with industry-standard agent platforms, and contribute to an ecosystem moving quickly toward open, modular, and intelligent automation. As MCP continues to gain traction, you’ll be ready to lead and innovate in the evolving world of AI-powered agents.