MCP: Build Rich-Context AI Apps with Anthropic
by DeepLearning.AI
Build, deploy and connect real MCP servers with the Anthropic team that wrote the protocol.
Overview
Taught by Elie Schoppik, Head of Technical Education at Anthropic, this DeepLearning.AI short course is built directly with the team that authored the Model Context Protocol, and it runs about two hours across eleven video lessons and six runnable code examples. The sequence moves from motivation to deployment: Why MCP, MCP Architecture, a chatbot example, Creating an MCP Server, Creating an MCP Client, Connecting the MCP Chatbot to Reference Servers, Adding Prompt and Resource Features, Configuring Servers for Claude Desktop, and Creating and Deploying Remote Servers. The running project is an academic paper search assistant: you start with a plain Claude-powered chatbot calling a hand-wired search tool, then refactor that tool behind a local MCP server built with the FastMCP Python SDK, exposing all three MCP primitives — tools, resources and prompt templates — rather than tools alone. You then write your own MCP client, point it at multiple servers at once including the official open-source reference servers, wire the finished server into Claude Desktop through its config file, and finally deploy a remote server so the same integration works over HTTP instead of stdio. Because MCP is a vendor-neutral open standard now supported by ChatGPT, Visual Studio Code and Cursor as well as Claude, the server you build here is reusable well beyond Anthropic's own products.
At a Glance
- Topic
- MCP
- Level
- Intermediate
- Format
- Course
- Cost
- Free
- Duration
- ~2 hours (1h 58m), 11 video lessons, self-paced
- Provider
- DeepLearning.AI
- Hands-on
- Yes — code/exercises
- Certificate
- Available
What You’ll Learn
- ✓Explain how MCP standardizes tool and data access across LLM clients
- ✓Build a local MCP server using the FastMCP Python SDK
- ✓Expose all three MCP primitives: tools, resources and prompt templates
- ✓Write an MCP client that connects to multiple servers simultaneously
- ✓Connect an MCP chatbot to the official open-source MCP reference servers
- ✓Configure a custom MCP server inside Claude Desktop's configuration file
- ✓Deploy a remote MCP server so clients can connect over HTTP
- ✓Refactor a hand-wired chatbot tool into a reusable MCP integration
Highlights
- •Taught by Elie Schoppik, Head of Technical Education at Anthropic — the organisation that authored MCP
- •Covers resources and prompt templates, not just tools, which most MCP tutorials skip entirely
- •Ends with remote deployment over HTTP rather than stopping at a local stdio demo
- •Six runnable code examples build one coherent project instead of disconnected snippets
- •Free to audit on DeepLearning.AI; only the certificate sits behind PRO membership
Who It’s For
Best For
- ✓Python developers wiring LLM apps to internal tools and data
- ✓Engineers replacing bespoke function-calling glue with a standard protocol
- ✓Teams planning to expose company data to Claude, ChatGPT or Cursor
- ✓AI engineers evaluating MCP before committing to it in production
Prerequisites
- •Comfortable writing Python
- •Basic understanding of LLM prompting and building LLM applications
- •No prior knowledge of MCP is assumed
FAQ
What is MCP: Build Rich-Context AI Apps with Anthropic?
A two-hour, hands-on introduction to the Model Context Protocol taught by Anthropic's Head of Technical Education. You build a working MCP server with FastMCP, expose tools, resources and prompt templates, write your own MCP client that talks to several servers at once, connect the result to Claude Desktop, and deploy a remote server. Afterwards you can replace ad-hoc tool wiring in your own LLM apps with a standard integration other clients can reuse.
Is MCP: Build Rich-Context AI Apps with Anthropic free?
MCP: Build Rich-Context AI Apps with Anthropic is free to access.
What level is MCP: Build Rich-Context AI Apps with Anthropic for?
MCP: Build Rich-Context AI Apps with Anthropic is aimed at a intermediate audience. Recommended background: Comfortable writing Python, Basic understanding of LLM prompting and building LLM applications, No prior knowledge of MCP is assumed.
How long does MCP: Build Rich-Context AI Apps with Anthropic take?
Expect roughly ~2 hours (1h 58m), 11 video lessons, self-paced. Most learners work through it at their own pace.
What will I learn from MCP: Build Rich-Context AI Apps with Anthropic?
You'll learn: Explain how MCP standardizes tool and data access across LLM clients; Build a local MCP server using the FastMCP Python SDK; Expose all three MCP primitives: tools, resources and prompt templates; Write an MCP client that connects to multiple servers simultaneously; Connect an MCP chatbot to the official open-source MCP reference servers; Configure a custom MCP server inside Claude Desktop's configuration file; Deploy a remote MCP server so clients can connect over HTTP; Refactor a hand-wired chatbot tool into a reusable MCP integration.
Topics
Sources
This page was written from 2 sources, 1 on domains other than deeplearning.ai.