Building AI Agents with MCP and Tool Calling

Modern AI models are incredibly capable, but they truly come to life when they can interact with the external world. Mastering the Model Context Protocol (MCP) and tool calling is the key to transforming static language models into dynamic, autonomous agents that can query databases, use APIs, and solve complex tasks. In this text-only course, you will journey from the absolute basics of agentic design to deploying secure, functional AI agents. You will read clear explanations of architectural concepts, study structured JSON schemas, and analyze practical code snippets that demonstrate how to connect LLMs to external tools safely and reliably. What you'll learn: Understand the foundational architecture of the Model Context Protocol (MCP) and how agents communicate; Design precise tool definitions and JSON schemas that language models can reliably interpret; Implement secure tool calling workflows to prevent unauthorized actions and manage execution environments; Handle errors, timeouts, and unexpected model outputs with robust fallback strategies; Create stateful AI agents that can plan, execute, and self-correct using external data sources. The course begins with core definitions and architectural patterns before guiding you through step-by-step code implementations of tool-calling loops. You will progress through written exercises designed to reinforce your understanding of agent state management and schema configuration. This course is designed for software developers, technical product managers, and AI enthusiasts who want to build agentic systems. No prior experience with MCP or advanced agent frameworks is required, though a basic understanding of programming concepts is helpful. Start reading today to unlock the potential of autonomous AI agents.