Building AI Agents with Model Context Protocol and TypeScript

AI agents often struggle to act effectively because they lack secure, standardized access to real-time data and local tools. By establishing a unified communication layer, you can build systems that reliably solve complex tasks with precision. This text-based course guides you through the fundamentals of the Model Context Protocol (MCP) using TypeScript, showing you how to build robust, production-ready agentic workflows. What you'll learn: - Understand the foundational architecture of the Model Context Protocol, including servers, clients, and transports. - Configure MCP servers in TypeScript to safely expose local databases and file systems to language models. - Implement secure tool-calling patterns that allow AI agents to safely execute external actions. - Apply modern TypeScript features like strong type safety and asynchronous patterns to write resilient agent code. - Design robust error-handling and fallback strategies for reliable agent performance. You will start with core concepts and protocol definitions before reading through step-by-step integration patterns and analyzing clean, structured code snippets. This course is designed for developers who want to learn how to connect AI models to external tools, requiring only basic programming knowledge and no prior experience with AI engineering. Start reading today to unlock the potential of standard-driven AI integrations.