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Quick Guide & Tips

💻   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.


💻   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)"


💻   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).


📱   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.

🖥   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.


📚   Enroll in Other Short Courses

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. 👇

👉👉 🔗 DeepLearning.AI – All Short Courses [+]


🙂   Let Us Know What You Think

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).

Also, you are more than welcome to join our community 👉👉 🔗 DeepLearning.AI Forum


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Welcome to MCP: Build Rich-Context AI Apps with Anthropic built in partnership with Anthropic. In this course, you'll learn the core concepts of MCP and how to implement it in your AI application. The Model Context Protocol, or MCP, is an open protocol that standardizes how your LLM applications can get access to context in terms of tools and data resources based on the client-server architecture. It defines how communication takes place between an MCP client hosted inside your own LLM application, and an MCP server that exposes tools and data resources and prompt templates to your application. Since Anthropic launched MCP in November of 2024, the MCP ecosystem has been growing really rapidly. I'm delighted that the instructor for this course is Elie Schoppik, who is Head of Technical Education at Anthropic. Thanks, Andrew. I'm excited to teach this course with you. MCP originated as part of an internal project where we recognized an opportunity to extend the capabilities of Claude Desktop so that it can interact with local file systems and other external systems. We found the protocol we developed was useful in many AI applications, with similar needs. To make this available to more developers, we published the specification and opened its development to the open source community. The MCP ecosystem includes a growing number of MCP service developed by the open source community, as well as by Anthropic's MCP team. MCP is model agnostic and is designed to be easy to plug into multiple applications. Say you're building a research assistant agent, and you'd like for this agent to interact with your GitHub repos, read notes from your Google Drive documents, maybe create a summary stored in your local system. Instead of you writing your own custom LLM tools, you can connect your agent to the GitHub, Google Drive and File System service, which will provide the tool or the API call definitions and also handle the tool execution. Elie will walk you through the details of the MCP protocol. We'll first dive into the details of the MCP client-server architecture. You'll then work on a chatbot application to make it MCP compatible. You'll build and test an MCP server and connect your chatbot to it. Your MCP server will provide tools, prompt templates, and resources to your chatbot. You'll also connect your chatbot to other trusted third-party servers to extend its capabilities. You'll then re-use your MCP server and connect it to other MCP applications like Claude Desktop. Finally, you'll learn how you can deploy your MCP server remotely. I'd like to thank from DeepLearning.AI, Hawraa Salami, who had contributed to this course. MCP is a really important technology that's making it much easier for LLM application developers to connect the systems to many tools and data resources. And for teams building tools or providing data, it is also making it much easier to make what they build available to many developers. So this is a technology worth learning about. The next video goes through why connecting LLM applications to resources had been so difficult before, and how MCP addresses this. So, please go on to the next video to learn more.
course detail
Next Lesson
MCP: Build Rich-Context AI Apps with Anthropic
  • 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
  • Course Feedback
  • Forum
  • Accomplishment