An agent is only as useful as what it can reach. Files, databases, calendars, code repositories, and web services all hold the context an agent needs, but wiring each one in with custom code doesn't scale. The Model Context Protocol (MCP) is an open standard that solves this: a common way for AI applications to discover and use external tools, resources, and prompts, often described as a USB-C port for AI.
The first two lessons explain the idea: the client-server architecture, why a standard helps, and how a host application like Claude Desktop or Cursor connects to MCP servers. You'll also see an MCP server built from scratch and tested with the MCP Inspector. The last lesson is a hands-on course on building your own servers in Python with FastMCP, testing them, and deploying one.
As you watch, notice how the same server can work with many different clients without changes. Also keep in mind that every server you connect extends what your agent can do, so it's worth thinking about which tools to expose and how to secure them.
A beginner-friendly explanation of what MCP is and the problem it solves, followed by where to find ready-made MCP servers and how to add one to an AI tool.
Video 16minSee how MCP turns a plain LLM tool into a shared server: build one for the LangGraph docs, test it in the MCP Inspector, and connect it to Claude Desktop, Cursor, and Windsurf.
Video 12minA hands-on course on building MCP servers in Python with FastMCP: three coding scenarios (including stdio and HTTP transports and API integration), testing with GitHub Copilot, and a bonus deployment to FastMCP Cloud.
Video 1h 12min