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A smarter way to learn with interactive, real-time conversations that help you test your knowledge, challenge assumptions, and deepen your understanding as you progress through the course. Modern AI systems rely on structured communication between models, tools, and applications. In this course, you will learn how the Model Context Protocol (MCP) enables seamless interaction between large language models, tools, and external systems. You’ll gain a clear understanding of MCP architecture, server-client interactions, and how MCP solves key limitations in AI integrations. By exploring both theory and real-world implementations, you will build the skills needed to design scalable AI tool integrations and develop powerful AI-enabled applications. The course begins with a foundational overview of MCP, explaining how the protocol works, the problems it solves for LLMs, and why it is considered a universal adapter for AI applications. You’ll explore the core architecture, server-client-host relationships, and communication transports such as STDIO, Server-Sent Events (SSE), and Streamable HTTP. These lessons provide the conceptual framework required to understand how MCP systems operate within modern AI ecosystems. As the course progresses, you will move into hands-on development. You’ll build MCP servers from scratch, integrate tools, create resources and prompts, and test your implementations using development environments like Claude Desktop and VS Code. Through practical projects—such as building SQL-based MCP servers and real-world data integrations—you will learn how to develop functional MCP services and expand their capabilities. In the final sections, you will build a complete end-to-end MCP server project, integrate prompts and resources, and deploy your server remotely to production environments. This course is ideal for developers, AI engineers, and technical professionals interested in AI tooling and LLM integration. A basic understanding of programming, APIs, and software development concepts is recommended. The difficulty level is intermediate. By the end of the course, you will be able to design MCP architectures, build and test MCP servers, integrate tools and resources for AI applications, and deploy scalable MCP services that extend the capabilities of modern AI systems.