A well-written prompt only gets an agent so far if it can't actually reach your data and tools. The Model Context Protocol (MCP) is the open standard, released by Anthropic in November 2024, that solves that problem — a single, common way for an AI assistant to connect to the systems where your data and tools actually live, instead of a custom integration for every model and every source.
This module starts with the fundamentals: what MCP is, why it exists, and how it fits alongside prompting and tool use — first straight from the team at Anthropic who built it, then from a focused explainer that lays out the core ideas in one sitting. It then moves to a practical demo that shows an agent using MCP to complete a real task end to end, before turning hands-on with building your own MCP server and client in Python. It closes with a full, project-based course that puts everything together against a real MCP deployment.
Watch the lessons in order, then continue on to Agent Memory.
Straight from the team at Anthropic: Theo Chu, David Soria Parra, and Alex Albert on why MCP was built, how it was launched and open-sourced, and what its core components actually are.
A focused walkthrough of what MCP actually standardizes — hosts, clients, and servers, and the tools, resources, and prompts a server can expose — without getting lost in implementation detail.
A live, practical demo showing an AI agent using MCP to search for and book a flight — a concrete look at what "an agent calling a tool through MCP" actually looks like in a real conversation.
Hands-on build of an MCP server from scratch with the Python SDK and FastMCP — setting up the environment, defining a tool, and connecting it to both Claude Desktop and Claude Code, with debugging along the way.
The full freeCodeCamp.org course: what MCP is and why it matters, then three hands-on coding scenarios building calculator and API-backed MCP servers, testing them with GitHub Copilot, and deploying to FastMCP Cloud.