Course Overview

Modern AI applications are moving beyond conversation to action. Powered by large language models (LLMs), AI agents can retrieve live data, automate workflows, and perform tasks on a user’s behalf. But turning these capabilities into reliable applications requires more than a capable model: agents need access to external tools and data. Developers often build custom integrations for each system, creating connections that are difficult to maintain, reuse, and scale.

This course introduces the Model Context Protocol (MCP), an open protocol for connecting AI applications to external tools and data. Through conceptual explanations and hands-on Python examples, you’ll learn why standardized connectivity matters, how MCP’s architecture works, and how to build and integrate MCP servers and clients.

What will you learn?

You’ll begin by exploring the limitations of standalone LLMs and why AI applications need relevant context and access to external tools. You’ll then examine the transition from chatbots to AI agents and the integration problems MCP is designed to address.

You’ll learn what MCP is, where it fits in an AI application, and how its hosts, clients, and servers work together. Practical examples will illustrate each component’s role.

In the hands-on lessons, you’ll build an MCP server and client using the Python MCP SDK, then extend the application with prompts and resources. You’ll learn how to expose tools, provide contextual data, and make reusable prompt templates available to an AI application.

You’ll also explore how to extend a single-server application into a broader agentic system. Additional lessons introduce MCP elicitation and server security.

Intended audience

This course is designed for junior to mid-level developers who want to build AI-powered assistants and automation systems. AI/ML engineers, backend developers, and platform developers will gain both conceptual grounding and practical experience with MCP. No prior experience with MCP or advanced AI frameworks is required.

Prerequisites

You should have a working knowledge of Python, which is used in the hands-on examples. Familiarity with basic web protocols and APIs is helpful. No prior experience with agentic AI or cloud integrations is required.

Course structure

The course begins with an overview and a lesson on the connectivity challenges that can prevent agentic systems from scaling. Three introductory lessons then cover the transition from LLMs to AI agents, MCP’s purpose, and its architecture. Four practical lessons guide you through creating an MCP server and client, then adding prompts and resources. A closing lesson explores the transition from a single server to an agentic ecosystem. Two additional lessons cover MCP elicitation and MCP server security.

By the end of the course, you’ll be able to build and extend an MCP-based AI application and explain how its components work together.