In this course, you'll discover how to build powerful AI applications using the Model Context Protocol, a revolutionary approach that eliminates tedious integration code when connecting Claude to external services and data sources. This course takes you from understanding MCP's core architecture to building fully functional MCP servers and clients that can interact with real-world systems. You'll learn the three essential MCP primitives - tools, resources, and prompts - and understand when to use each one. Through hands-on implementation, you'll create your own MCP server with the Python SDK, defining tools that Claude can call autonomously, resources that provide data to your applications, and prompts that deliver professionally crafted workflows to end users. By building both client and server components, you'll gain complete insight into how MCP enables seamless communication between AI models and external services. You'll work with the MCP Inspector to test your implementations, understand transport protocols, and see exactly how messages flow through the entire system. Whether you're building GitHub integrations, document management systems, or custom AI workflows, this course gives you the practical skills to leverage MCP's standardized approach and dramatically reduce your development burden.
HD
This course structure draws from established courses and provides a balanced, insightful path from theory to building real AI applications.
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assumes you will pay for an anthropic API key, this intro should be generic without the need to pay another company money. This should provide a student API key or not need one.
I genuinely enjoyed this course. As a developer who has been working more with AI tools and workflows recently, I kept hearing about MCP but didn’t have a clear understanding of what was actually happening behind the scenes. This course helped me connect the dots. The explanation of MCP clients, servers, tools, and how they allow AI models to interact with external systems made the whole concept much easier to understand. What I liked most is that it didn’t feel like I was just memorizing another AI technology. I could actually see how I can use MCP in the applications I’m building and even start creating my own MCP server. A simple but genuinely useful course for developers who want to understand where AI + software integrations are heading.
Clear, practical, and very useful introduction to MCP. The course helped me understand resources, tools, prompts, and MCP Inspector, and gave me enough foundation to build a small working MCP server project. Thank you to Stephen Grider and Anthropic.
Stephen is a great instructor! The course is well structured, concepts are clearly articulated and introduced in a way that's easy to follow.
This course structure draws from established courses and provides a balanced, insightful path from theory to building real AI applications.
good.
Insgesamt gut. Bei KI Aufgaben schwierig genau die Worte zu finden, die die KI erwartet
Some things are missing , like access to the code , the code is presented but is not provided in any form to the student. . I did not like the AI performing the evaluation grade assignments, there are missing instructions in the questions and until grading and how as result of the evaluation. Some concepts are not clearly explained.
Very basic explanation. Does not discuss about the actual protocol or their methods yet one is asked to code it in the client session.