Recommended Short Course: MCP: Build Rich-Context AI Apps with Anthropic (DeepLearning.AI)

Much of my recent work on agentic AI involves the Model Context Protocol (MCP), the open protocol that standardises how AI applications connect to tools, data and prompts. When colleagues ask how to get hands-on with MCP quickly, I point them to this short course from DeepLearning.AI.

The resource

• Title: MCP: Build Rich-Context AI Apps with Anthropic
• Provider: DeepLearning.AI, created in partnership with Anthropic
• Instructor: Elie Schoppik
• Link: MCP: Build Rich-Context AI Apps with Anthropic on DeepLearning.AI

What it covers

The course explains why MCP exists and how its client-server architecture works, then moves quickly into code:

• Building a chatbot with custom tools and making it MCP-compatible
• Building a local MCP server with FastMCP that exposes tools, resources and prompt templates, and testing it with MCP Inspector
• Creating an MCP client inside the chatbot and connecting it to reference servers such as filesystem and fetch
• Configuring Claude Desktop to use your server
• Deploying an MCP server remotely

It closes with a look at where MCP is heading, including authorisation, server discovery and multi-agent architectures.

Who it’s for

Developers and data professionals who know some Python and have a basic understanding of prompting and LLM application development. You don’t need prior MCP experience.

My take

MCP clicked for me once I saw that the hard part isn’t the protocol. It’s designing good tools. This course gets you past the plumbing quickly, so you can focus on that. After finishing it, try wrapping something from your own data platform, such as a read-only query over a gold table or a lookup against your data catalogue, as an MCP server. Then think carefully about permissions before you let an agent call it. The same ideas carry over to agents built with Microsoft Foundry and other platforms.

A practical point: MCP is moving fast, so read the current specification and SDK documentation alongside the course. The core ideas of clients, servers, tools, resources and prompts are stable, and those are what the course teaches well.

This is an independent recommendation. I’m not affiliated with DeepLearning.AI or Anthropic.