Building AI Agents with Model Context Protocol

AI agents promise autonomous action, but they often struggle when they lack access to real-time data or the right tools. The Model Context Protocol (MCP) solves this by providing a secure, open standard to connect large language models to databases, filesystems, and APIs. This text-only course guides you through the foundational concepts of MCP, explaining how to bridge the gap between AI reasoning and practical data execution. Through clear, written explanations and code examples, you will learn how to design, configure, and connect agents that reliably read and write data across different environments. You will understand how to build secure pipelines that give your models the precise context they need to perform complex tasks. What you'll learn: - Understand the core architecture of the Model Context Protocol, including client-server interactions. - Configure MCP servers to safely expose filesystems, databases, and APIs to AI models. - Implement tool-use and function-calling patterns that allow agents to execute actions reliably. - Apply security best practices to sandbox agent environments and manage data access permissions. - Design structured outputs and context-rich prompts to guide agent decision-making. The course starts with essential terminology and protocol fundamentals before moving on to practical integration patterns and real-world execution strategies. This course is designed for beginners, software developers, and tech enthusiasts curious about AI agent architecture, requiring only a basic understanding of programming concepts. Start reading today to build smarter, more capable AI systems that interact seamlessly with the digital world.