Building AI Integrations with Model Context Protocol (MCP)

About this course

The Model Context Protocol (MCP) is emerging as a core standard for how AI systems connect to tools, data, and external capabilities. This course provides a practical, end-to-end understanding of MCP—from first principles to real integrations—so you can design AI agents that scale cleanly across tools, models, and environments.

You’ll start by learning why MCP exists: the M × N integration problem that plagues modern AI systems, and how MCP reframes it into a composable, extensible architecture. From there, you’ll explore MCP’s core components, capabilities, and communication flow—building a clear mental model before touching any implementation.

Once the foundations are solid, the course moves into hands-on development. You’ll build MCP servers and clients from scratch, understand the JSON-RPC 2.0 message protocol that powers MCP, and work with streamable HTTP transport for real-world usage. Finally, you’ll integrate MCP servers with modern AI tooling like Claude Code and Cursor, showing how MCP fits naturally into today’s AI-powered development workflows.

Throughout the course, the focus stays on conceptual clarity, architectural correctness, and real-world applicability—not just getting something working, but understanding why it works and how it scales.

What You’ll Learn

What MCP is, why it exists, and how it solves the AI integration problem

How MCP defines and manages “context” through tools, resources, prompts, and sampling

The roles and responsibilities of MCP hosts, clients, and servers

How MCP communication flows from user request to server execution and back

How to build and test MCP servers and clients using FastMCPHow MCP uses JSON-RPC 2.0 and streamable HTTP transport

How to integrate MCP with modern AI devel

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