Connect AI to Anything with MCP

The USB-C of AI

Every AI app needs to reach real information and real systems: your calendar, your database, your documents, your booking system. Before MCP, every app had to build every connection itself. The Model Context Protocol (MCP) is an open standard that fixes this: write a connection once, as an MCP server, and any MCP app can use it — chat apps like Claude, coding tools like VS Code, and the agents you build yourself.

In this course you'll build Corner Café's MCP server: tools to check tables and request bookings, the staff-handbook search from the RAG course, the menu and opening hours as resources, and ready-made prompts. Then you'll use it from your own Python client, a LangGraph agent, desktop AI apps, and over the web — with a token, and with safety in mind.

What you'll learn

• What MCP is: hosts, clients and servers, and tools, resources and prompts
• Building a server in Python with the official SDK, and testing it in the MCP Inspector
• Designing tools: names, descriptions, input schemas, annotations and errors
• Writing your own MCP client
• Giving a LangGraph agent tools from several MCP servers — including ready-made ones
• Connecting desktop AI apps, and running servers over HTTP with a token
• Testing MCP servers, and the security risks to know about

Before you start

You should have done AI Agents with LangGraph (tools and agents) — and ideally RAG in Depth, whose document search becomes one of the server's tools. You'll need Python, Ollama and Node.js (for the MCP Inspector), on a computer with at least 8 GB of memory. Everything is free.

How it works

• 6 modules, 18 lessons, about 7 hours including quizzes
• Every command, tool call, agent run and screenshot in the lessons was recorded for real
• The finished code for every step, a quiz after each module, a cheat sheet and a certificate

Free forever — like every self-paced course on Avro Academy: all lessons, quizzes, downloads and your certificate. Our promise