MCP Fundamentals for Building AI Agents

MCP is widely discussed, but practical guidance for production-oriented implementations is still relatively limited. The protocol provides a clear foundation for connecting AI applications with tools and context, but the documentation often assumes familiarity with underlying concepts and implementation patterns, and the ecosystem is still evolving. A common challenge is knowing how to move from the protocol specification to a working implementation. This course is designed to address that gap. Drawing on experience researching and teaching AI systems, from neural networks and adaptive systems to production-oriented generative AI architectures, I wanted to create a resource that provides the structured path I wanted when I started working with MCP: a clear, hands-on progression from core concepts to a working MCP application. You'll start with the fundamentals: why agentic systems need a protocol like MCP in the first place, and how MCP's architecture (host, server, client) solves the integration problems that have plagued AI tooling. From there, you'll build. You'll code an MCP server, wire up a client, add prompt and resource capabilities, and see your agent dynamically discover and use tools through the protocol. Everything runs in-browser. No local setup required. This course is designed for engineers who want to move beyond surface-level understanding and actually build with MCP. By the end, you’ll know how to implement the protocol, debug it, and understand where it fits in real GenAI architectures. If you’re serious about working with agentic systems and want a practical, grounded entry point into MCP, this course gives you a clear place to start and the confidence to go further.