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⇱ Model Context Protocol: Advanced Topics

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About this course

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. You'll explore sophisticated MCP capabilities including sampling for AI model integration, notification systems, file system access control, and the technical details of different transport protocols.

What you'll learn

  • Sampling implementation - Understand how MCP servers can request language model calls through connected clients, including the architecture that shifts AI costs and complexity from server to client
  • Progress and logging notifications - Learn to implement real-time feedback systems using context objects, logging callbacks, and progress reporting for long-running operations
  • Roots-based file access - Explore permission systems that grant MCP servers access to specific directories while providing security boundaries and enabling user-friendly file discovery
  • JSON message architecture - Examine the complete MCP message specification, distinguishing between request-result pairs and notification messages, and understanding bidirectional communication patterns
  • Stdio transport mechanisms - Understand how MCP clients and servers communicate through standard input/output streams, including the required initialization handshake sequence
  • StreamableHTTP transport implementation - Learn how Server-Sent Events (SSE) enable server-to-client communication over HTTP, including session management and dual-connection architectures
  • HTTP transport limitations - Discover how configuration flags affect functionality, particularly regarding server-initiated requests and streaming capabilities
  • Production scaling considerations - Understand when to use stateless HTTP for horizontal scaling with load balancers and the trade-offs between stateful and stateless server configurations
  • Transport selection criteria - Learn to choose appropriate transport methods based on deployment requirements, functionality needs, and scaling constraints

Prerequisites

  • Experience with Python development and async programming patterns
  • Familiarity with JSON message formats and HTTP protocols
  • Basic knowledge of Server-Sent Events (SSE)

Who this course is for

  • Developers working with Model Context Protocol implementations
  • Engineers building MCP servers and clients

Curriculum

  • Introduction
  • Let's get started!
  • Core MCP features
  • Sampling
  • Sampling walkthrough
  • Log and progress notifications
  • Notifications walkthrough
  • Roots
  • Roots walkthrough
  • Survey
  • Transports and communication
  • JSON message types
  • The STDIO transport
  • The StreamableHTTP transport
  • StreamableHTTP in depth
  • State and the StreamableHTTP transport
  • Assessment and next steps
  • Assessment on MCP concepts
  • Wrapping up

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. You'll explore sophisticated MCP capabilities including sampling for AI model integration, notification systems, file system access control, and the technical details of different transport protocols.

What you'll learn

  • Sampling implementation - Understand how MCP servers can request language model calls through connected clients, including the architecture that shifts AI costs and complexity from server to client
  • Progress and logging notifications - Learn to implement real-time feedback systems using context objects, logging callbacks, and progress reporting for long-running operations
  • Roots-based file access - Explore permission systems that grant MCP servers access to specific directories while providing security boundaries and enabling user-friendly file discovery
  • JSON message architecture - Examine the complete MCP message specification, distinguishing between request-result pairs and notification messages, and understanding bidirectional communication patterns
  • Stdio transport mechanisms - Understand how MCP clients and servers communicate through standard input/output streams, including the required initialization handshake sequence
  • StreamableHTTP transport implementation - Learn how Server-Sent Events (SSE) enable server-to-client communication over HTTP, including session management and dual-connection architectures
  • HTTP transport limitations - Discover how configuration flags affect functionality, particularly regarding server-initiated requests and streaming capabilities
  • Production scaling considerations - Understand when to use stateless HTTP for horizontal scaling with load balancers and the trade-offs between stateful and stateless server configurations
  • Transport selection criteria - Learn to choose appropriate transport methods based on deployment requirements, functionality needs, and scaling constraints

Prerequisites

  • Experience with Python development and async programming patterns
  • Familiarity with JSON message formats and HTTP protocols
  • Basic knowledge of Server-Sent Events (SSE)

Who this course is for

  • Developers working with Model Context Protocol implementations
  • Engineers building MCP servers and clients
  • Introduction
  • Let's get started!
  • Core MCP features
  • Sampling
  • Sampling walkthrough
  • Log and progress notifications
  • Notifications walkthrough
  • Roots
  • Roots walkthrough
  • Survey
  • Transports and communication
  • JSON message types
  • The STDIO transport
  • The StreamableHTTP transport
  • StreamableHTTP in depth
  • State and the StreamableHTTP transport
  • Assessment and next steps
  • Assessment on MCP concepts
  • Wrapping up

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Skilljar collects basic learning analytics such as course progress, lesson completion status, quiz scores, and time spent on materials. This data helps us understand how you're progressing through the course and allows us to provide you with completion certificates. All data collection is focused on improving your learning experience, and is subject to Skilljar's Privacy Policy.

Skilljar only tracks your learning progress within this course platform, while your Anthropic account manages your access to the Anthropic Console and/or Claude AI services.

Yes, Skilljar employs industry-standard security measures including data encryption, secure hosting, and regular security audits. Your learning data is stored on secure servers with appropriate access controls. Skilljar is SOC 2 compliant and follows best practices for data protection to ensure your information remains safe and private.

To request deletion of your learning data or account, email academy-support@anthropic.com. Your request will be processed in accordance with applicable privacy laws and our data retention policies. Note that some data may need to be retained for legitimate business purposes, such as compliance or security, but we'll delete all personal information where legally permissible.

No, you don't need an Anthropic account to access this learning content. The course is hosted on Skilljar and only requires a Skilljar account for access. However, if you want to use Claude AI services after completing the course, you would need to create a separate Anthropic account at claude.ai.

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