About Course
Connecting AI agents and LLMs to real-world data sources, internal APIs, and software tools often means writing complex, fragile integration code for every new tool. Model Context Protocol (MCP) — often called the “USB-C port for AI applications” — solves this by providing a standardized, secure architecture for AI data exchange.
This practical course guides you through building MCP servers and clients, exposing custom tools and resources, managing context flows, and establishing secure, production-ready AI workflows.
What Will You Learn?
- Overview: A step-by-step guide to understanding MCP architecture, building MCP servers and clients, and integrating AI agents with external systems seamlessly.
- Key Topics: MCP Architecture & Primitives (Tools, Resources, Prompts), Building MCP Servers & Clients, Python SDK usage, Security & Permissions, Transport Protocols, and MCP Inspector for testing & debugging.
- Who it's for: Software engineers, AI developers, system architects, and tech leads looking to build scalable, tool-connected AI applications without vendor lock-in.







