Model Context Protocol: Advanced Topics by Anthropic

This course examines advanced features and implementation patterns for Model Context Protocol (MCP) development, focusing on server-client communication, transport mechanisms, and production deployment considerations. Explore sophisticated MCP capabilities including sampling for AI model integration, notification systems, file system access control, and the technical details of different transport protocols. An ideal fit for developers and engineers invested in MCP implementation and server deployment, by the end of this course, you’ll be equipped with in-demand skills to advance MCP capabilities and scale server infrastructure efficiently, applying best practices in infrastructure setup and protocol usage.

Note: This course requires basic experience with Python development and async programming patterns, familiarity with JSON message formats and HTTP protocols, and basic knowledge of Server-Sent Events (SSE).

Learning objectives
Define how MCP servers can request language model calls through connected clients, including the architecture that shifts AI costs and complexity from server to client.
Implement real-time feedback systems using context objects, logging callbacks, and progress reporting for long-running operations.
Grant MCP servers access to specific directories while providing security boundaries and enabling user-friendly file discovery.
Examine the complete MCP message specification, distinguishing between request-result pairs and notification messages, and understanding bidirectional communication patterns.
Define how MCP clients and servers communicate through standard input/output streams, including the required initialization handshake sequence.
Implement Server-Sent Events (SSE) to enable server-to-client communication over HTTP, including session management and dual-connection architectures.
Control functionality using configuration flags, particularly regarding server-initiated requests and streaming capabilities.
Use stateless HTTP for horizontal scaling with load balancers and the trade-offs between stateful and stateless server configurations.
Choose appropriate transport methods based on deployment requirements, functionality needs, and scaling constraints.