Building AI Applications with MCP Server and Box Integration

Connecting large language models to your organization's private data is one of the most powerful ways to leverage AI, but managing secure file access can be challenging. The Model Context Protocol (MCP) provides a standardized, secure way to bridge the gap between LLMs and external data sources like Box. In this text-based course, you will learn how to build AI applications that securely discover, read, and extract text from Box files using an MCP server. You will start with the fundamental concepts of MCP and tool-calling, progress to implementing a Box MCP server, and ultimately transform your setup into a cooperative multi-agent system that communicates seamlessly to process complex documents. What you'll learn: - Understand the core architecture of the Model Context Protocol (MCP) and how LLMs utilize external tools. - Configure an MCP server to establish a secure connection between an LLM and Box storage. - Implement search and discovery workflows to find specific Box files programmatically. - Extract and process text from various document formats using AI-driven tools. - Design a multi-agent system where specialized agents cooperate and communicate using Agent-to-Agent (A2A) patterns. - Apply security best practices for managing API credentials and file permissions in AI integrations. This written guide begins with foundational terminology and MCP basics before guiding you through step-by-step implementation patterns and architectural designs for multi-agent workflows. You will learn through clear, text-based explanations and practical code examples designed to be read and applied directly. This course is designed for software developers and AI enthusiasts who want to learn how to connect LLMs to cloud storage solutions; no prior experience with MCP is required. Start reading today to unlock the potential of context-aware AI agents in your workflow.