Traditional tool-calling requires custom integration for every new data source, creating a fragmented ecosystem. The Model Context Protocol (MCP)provides a universal, open standard that replaces these "one-off" connectors. This course teaches you how to move beyond static APIs to a client-server-host architecture, where a single MCP client can discover and use tools across multiple servers simultaneously. You will learn to use FastMCP to provide AI models with secure, standardized access to local resources, databases, and web services without rewriting the integration layer for every new LLM.

Yes. Security is the primary bottleneck for deploying AI agents in enterprise environments, and this course addresses it through permission-based workflows. You will get hands-on experience implementing sampling, roots, and user-approval mechanisms—ensuring that your agents operate within strict filesystem boundaries and never execute unintended actions. By mastering JSON-schema-based elicitation and auditing, you’ll learn how to build "human-in-the-loop" systems where the AI must request explicit permission before accessing sensitive data or performing external writes.

To access the course materials, assignments and to earn a Certificate, you will need to purchase the Certificate experience when you enroll in a course. You can try a Free Trial instead, or apply for Financial Aid. The course may offer 'Full Course, No Certificate' instead. This option lets you see all course materials, submit required assessments, and get a final grade. This also means that you will not be able to purchase a Certificate experience.