Quick Guide & Tips

đŸ’»   Accessing Utils File and Helper Functions

In each notebook on the top menu:

1:   Click on "File"

2:   Then, click on "Open"

You will be able to see all the notebook files for the lesson, including any helper functions used in the notebook on the left sidebar. See the following image for the steps above.

đŸ’»   Downloading Notebooks

In each notebook on the top menu:

1:   Click on "File"

2:   Then, click on "Download as"

3:   Then, click on "Notebook (.ipynb)"

đŸ’»   Uploading Your Files

After following the steps shown in the previous section ("File" => "Open"), then click on "Upload" button to upload your files.

📗   See Your Progress

Once you enroll in this course—or any other short course on the DeepLearning.AI platform—and open it, you can click on 'My Learning' at the top right corner of the desktop view. There, you will be able to see all the short courses you have enrolled in and your progress in each one.

Additionally, your progress in each short course is displayed at the bottom-left corner of the learning page for each course (desktop view).

đŸ“±   Features to Use

🎞   Adjust Video Speed: Click on the gear icon (⚙) on the video and then from the Speed option, choose your desired video speed.

🗣   Captions (English and Spanish): Click on the gear icon (⚙) on the video and then from the Captions option, choose to see the captions either in English or Spanish.

🔅   Video Quality: If you do not have access to high-speed internet, click on the gear icon (⚙) on the video and then from Quality, choose the quality that works the best for your Internet speed.

đŸ–„   Picture in Picture (PiP): This feature allows you to continue watching the video when you switch to another browser tab or window. Click on the small rectangle shape on the video to go to PiP mode.

√   Hide and Unhide Lesson Navigation Menu: If you do not have a large screen, you may click on the small hamburger icon beside the title of the course to hide the left-side navigation menu. You can then unhide it by clicking on the same icon again.

🧑   Efficient Learning Tips

The following tips can help you have an efficient learning experience with this short course and other courses.

🧑   Create a Dedicated Study Space: Establish a quiet, organized workspace free from distractions. A dedicated learning environment can significantly improve concentration and overall learning efficiency.

📅   Develop a Consistent Learning Schedule: Consistency is key to learning. Set out specific times in your day for study and make it a routine. Consistent study times help build a habit and improve information retention.

Tip: Set a recurring event and reminder in your calendar, with clear action items, to get regular notifications about your study plans and goals.

☕   Take Regular Breaks: Include short breaks in your study sessions. The Pomodoro Technique, which involves studying for 25 minutes followed by a 5-minute break, can be particularly effective.

💬   Engage with the Community: Participate in forums, discussions, and group activities. Engaging with peers can provide additional insights, create a sense of community, and make learning more enjoyable.

✍   Practice Active Learning: Don't just read or run notebooks or watch the material. Engage actively by taking notes, summarizing what you learn, teaching the concept to someone else, or applying the knowledge in your practical projects.

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Keep learning by enrolling in other short courses. We add new short courses regularly. Visit DeepLearning.AI Short Courses page to see our latest courses and begin learning new topics. 👇

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Your feedback helps us know what you liked and didn't like about the course. We read all your feedback and use them to improve this course and future courses. Please submit your feedback by clicking on "Course Feedback" option at the bottom of the lessons list menu (desktop view).

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Welcome back!

Hi ,

We'd like to know you better so we can create more relevant courses. What do you do for work?

DeepLearning.AI
Short CourseIntermediate1 hour 38 mins

MCP: Build Rich-Context AI Apps with Anthropic

Instructor: Elie Schoppik

Anthropic logo
  • Intermediate
  • 1 hour 38 mins
  • 11 Video Lessons
  • 7 Code Examples
  • Instructor: Elie Schoppik
    • Anthropic
    Anthropic

What you'll learn

  • Explore how MCP standardizes access to tools and data for AI applications, its underlying architecture, and how it simplifies the integration of new tools and connections to external systems (e.g., GitHub repos, Google Docs, local files).

  • Build and deploy an MCP server that provides tools, resources, and prompts, and add it to the configuration of AI applications, such as Claude Desktop, to extend them.

  • Build an MCP-compatible application that hosts multiple MCP clients, each maintaining 1-to-1 connection to an MCP server.

About this course

Join MCP: Build Rich-Context AI Apps with Anthropic, a short course created in partnership with Anthropic and taught by Elie Schoppik.

Connecting AI applications to external systems to bring rich context to LLMs has often meant writing custom integrations for each use case. This has fragmented AI development between teams within a company and across the industry.

The Model Context Protocol (MCP) is an open protocol that standardizes how LLMs access tools, data, and prompts from external sources, simplifying how new context is integrated into AI applications. MCP, developed by Anthropic, is based on a client-server architecture. It defines the communication details between an MCP client, hosted inside the AI application, and an MCP server that exposes tools, resources, and prompt templates. The server can be a subprocess launched by the client and running locally, or an independent process running remotely.

In this hands-on course, you’ll learn the core concepts of MCP and how to implement it in your AI Application. You’ll make a chatbot MCP-compatible, build and deploy an MCP server, and connect the chatbot to your MCP server and other open-source servers.

Here’s what you’ll do:

  • Understand why MCP makes AI development less fragmented and how it standardizes connections between AI applications and external data sources.
  • Learn the core components of the client-server architecture of MCP and the underlying communication mechanism.
  • Build a chatbot with custom tools for searching academic papers, and transform it into an MCP-compatible application.
  • Build a local MCP server that exposes tools, resources, and prompt templates using FastMCP, and test it using MCP Inspector.
  • Create an MCP client inside your chatbot to dynamically connect to your server.
  • Connect your chatbot to reference servers built by Anthropic’s MCP team such as filesystem, which implements filesystem operations, and fetch, which extracts contents from the web as markdown.
  • Configure Claude Desktop to connect to your server and others, and explore how it abstracts away the low-level logic of MCP clients.
  • Deploy your MCP server remotely and test it with the Inspector or other MCP-compatible applications.
  • Learn about the roadmap for future MCP development such as multi-agent architecture, MCP registry API, server discovery, authorization, and authentication.

By the end of the course, you’ll be able to build rich-context AI applications that can connect to a growing ecosystem of MCP servers, with minimal integration work.

Who should join?

It’s helpful to be familiar with Python and have a basic understanding of LLM prompting and LLM application development.

Course Outline

11 Lessons・7 Code Examples

  • Introduction

    Video・3 mins

  • Why MCP

    Video・7 mins

  • MCP Architecture

    Video・14 mins

  • Chatbot Example

    Video with Code Example・7 mins

  • Creating an MCP Server

    Video with Code Example・8 mins

  • Creating an MCP Client

    Video with Code Example・9 mins

  • Connecting the MCP Chatbot to Reference Servers

    Video with Code Example・12 mins

  • Adding Prompt and Resource Features

    Video with Code Example・11 mins

  • Configuring Servers for Claude Desktop

    Video・6 mins

  • Creating and Deploying Remote Servers

    Video with Code Example・7 mins

  • Conclusion

    Video・9 mins

  • Quiz

    Graded・Quiz

    ・10 mins

  • Appendix – Tips and Help

    Code Example・10 mins

Instructor

Elie Schoppik

Elie Schoppik

Course access is free for a limited time during the DeepLearning.AI learning platform beta!

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