This course teaches you to build Model Context Protocol (MCP) servers and clients with production-ready features. You'll learn to implement sampling—a technique that shifts AI model costs and complexity from servers to clients—and add real-time logging and progress notifications to improve user experience during long-running operations. The course covers roots, MCP's permission system that enables file discovery while maintaining security boundaries.

You'll understand the bidirectional communication patterns that make MCP work, including the JSON message types that flow between clients and servers. The course compares two transport methods: STDIO for local development and StreamableHTTP for remote deployments. You'll learn how StreamableHTTP uses Server-Sent Events to work around HTTP's limitations, and when to use stateless configurations for horizontal scaling at the cost of certain features. By completing this course, you'll be able to choose appropriate transport methods based on your deployment needs, implement servers that integrate with language models without direct API access, and understand the architectural trade-offs between different MCP configurations. The hands-on walkthroughs demonstrate each concept with practical implementation examples.