Modern AI systems are limited when they cannot interact with real-world tools, APIs, and data sources. The Model Context Protocol (MCP) solves this by providing a standardized way to connect large language models with external systems.

In this course, you will learn MCP from first principles—understanding why traditional approaches like function calling and plugins fail at scale, and how MCP introduces a structured, scalable architecture for AI integrations.

You will explore the core components of MCP, including Host, Client, and Server roles, along with its three key primitives—Tools, Resources, and Prompts. The course also dives deep into JSON-RPC communication, transport mechanisms, lifecycle management, and multi-agent orchestration patterns.

By the end, you will be able to design production-ready, MCP-based AI systems, enabling LLMs to take real-world actions while maintaining control, reliability, and scalability.