No need to wait! Andrew Ng’s MCP course is here!

Written by

Caleb Hayes

Updated on:June-21st-2025

Why is MCP so popular?

MCP is an open technical protocol that aims to standardize how large language models (LLMs) interact with external tools and services. You can think of MCP as a universal translator for the AI world, allowing AI models to "talk" to a variety of external tools.

Before the emergence of MCP, AI tool calls faced two major pain points :

The first is interface fragmentation: each LLM uses a different instruction format, and each tool API also has a unique data structure. Developers need to write customized connection code for each combination;

The second is development inefficiency: this "one-to-one translation" model is costly and difficult to scale, just like hiring a dedicated translator for each foreign customer.

MCP uses a common language format (JSON - RPC) that can be learned once to communicate with all tools that support this protocol. A universal translator can be used to use tools/data regardless of LLM.

Professor Andrew Ng and Anthropic jointly launch MCP course

DeepLearning.AI by Andrew Ng, a famous artificial intelligence scholar and professor at Stanford University, has partnered with Anthropic to launch a new short course called "MCP: Building Context-Rich AI Applications" !

Through this course, you will be able to build context-rich AI applications that can be easily connected to the growing MCP server ecosystem with significantly reduced integration effort, which is critical to improving development efficiency and promoting innovation in AI applications.

The course is taught by Elie Schoppik, Anthropic's Head of Technical Education.

Course Outline

Deep understanding of MCP:

Explore how MCP standardizes tool and data access, its underlying architecture, and how it simplifies integration of new tools and external systems (such as GitHub, Google Docs, local files)

Hands-on practice, from zero to one:

* Learn to build and deploy an MCP server that provides tools, resources, and prompts, and configure it into AI applications (such as Claude Desktop) to extend its capabilities.
* Build an MCP-compatible application that can host multiple MCP clients, each maintaining a one-to-one connection with an MCP server.
* Converted a custom chatbot tool for searching academic papers into an MCP-compatible application.
* Use FastMCP to build a local MCP server and test it with MCP Inspector.
* Create an MCP client in your chatbot to dynamically connect to your server.
* Connect your chatbot to reference servers built by the Anthropic MCP team, such as `filesystem` for implementing file system operations and `fetch` for fetching content from web pages.
* Configure Claude Desktop to connect to your server and other servers, and experience how it abstracts the underlying logic of the MCP client.
* Deploy your MCP server remotely and test it with the Inspector or other MCP compatible applications.