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

DeepLearning.AI
Short CourseIntermediate36 mins

Build AI Apps with MCP Server: Working with Box Files

Instructor: Ben Kus

Box logo
  • Intermediate
  • 36 mins
  • 7 Video Lessons
  • 3 Code Examples
  • Instructor: Ben Kus
    • Box
    Box

What you'll learn

  • Build an LLM-powered app that processes invoice files, and extracts fields such as client name, total amount and purchased products, and generates a summary report for each client.

  • Connect the app to the Box MCP server to access tools such as folder and files listing, and text extraction, to avoid manual downloads and remove custom text extraction.

  • Orchestrate a three-agent system consisting of files agent, extraction agent and an orchestrator, built using Google’s ADK and communicating using the A2A (Agent2Agent) protocol.

About this course

Join Build AI Apps with MCP servers: Working with Box files, built in partnership with Box, and taught by Ben Kus, its Chief Technology Officer.

You’ll begin with an AI application that processes files manually downloaded from a Box folder and locally stored. You’ll then refactor the application to make it MCP-compliant and connect it to the Box MCP server. The server will provide the application with the required tools to process the files directly in Box. You’ll finally evolve your solution into a multi-agent system that coordinates via the A2A protocol.

MCP or Model Context Protocol standardizes how context, in terms of tools and resources, is provided to LLMs. Instead of writing custom code inside your application for file search, file downloads, and text extraction, you can offload these tasks to an MCP server. An MCP client within your application can communicate with the MCP server to discover the tools and send requests to execute a certain tool. In this course, you’ll use the Box MCP server and learn how to connect it to your application to process files from a Box folder. You’ll also build a multi-agent system using Google’s Agent Development Kit. The agents will use A2A to communicate with each other, and the Box MCP server to access file and text extraction tools.

In detail, you’ll:

  • Build an LLM-powered invoice app that processes local PDF invoices to extract fields such as client name, total amount, and purchased product, and generates per-client reports.
  • Transform your application into an MCP-compliant app and use the tools of the Box MCP server to list the invoices within a Box folder and extract texts from each invoice, without manually downloading the files.
  • Move from a single agent to a multi-agent design: use an orchestrator to coordinate the tasks among sub-agents that use the Box MCP server and are specialized in files listing, text extraction, and reporting.
  • Design the orchestrator and subagents as A2A servers, and use an A2A client to send the user’s request to the orchestrator.

By the end, you’ll know how to build an AI application and AI agents that use the MCP server to process Box content securely, without manual downloads, and that support several file types and scale better with the number of files.

Who should join?

This course is great for AI builders who work with documents and want a practical way to extract fields and build small agent-based workflows. Basic Python knowledge is recommended. No prior MCP, A2A, or Box API experience required.

Course Outline

7 Lessons・3 Code Examples

  • Introduction

    Video・2 mins

  • Simple Invoice Processing App

    Video with Code Example・3 mins

  • Introduction to Box MCP Server

    Video・3 mins

  • Processing Invoices Using Box MCP Server

    Video with Code Example・5 mins

  • From a Single-Agent to a Multi-Agent Architecture

    Video・3 mins

  • Processing Invoices Using A Multi-Agent System

    Video with Code Example・6 mins

  • Conclusion

    Video・1 min

  • Quiz

    Graded・Quiz

    ・10 mins

Instructor

Ben Kus

Ben Kus

Chief Technology Officer of Box

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

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