What you'll learn:
- Build FastMCP servers and clients from scratch
- Create production-ready AI tools with structured inputs and outputs
- Design tools using schemas, validation, and error handling
- Work with resources, prompts, context, and state effectively
- Implement built-in and custom middleware for AI systems
- Add logging, caching, rate limiting, and error handling
- Use sampling, tool calling, and structured output
- Integrate FastMCP with MCP clients like Claude Desktop
This course is a complete, hands-on guide to mastering FastMCP and building production-ready AI systems with the Model Context Protocol.
FastMCP is quickly becoming one of the most important frameworks in modern AI development. It allows you to build AI tools, servers, and agents that work seamlessly with models like Claude and other LLM clients, with structured outputs, middleware, sampling, authentication, and full control over context and state.
In this course, you’ll go from zero to advanced FastMCP step by step.
You won’t just learn concepts, you’ll build real systems. Every section is designed to be practical, incremental, and reusable in real projects. By the end of the course, you’ll understand not just how FastMCP works, but why it’s designed the way it is, and how to use it confidently in production.
What you’ll learn
• Install and run FastMCP on Mac and Windows
• Build FastMCP servers and clients from scratch
• Work with Claude Desktop and real MCP clients
• Create powerful tools with decorators, schemas, and structured outputs
• Handle errors, validation, and client-side behavior correctly
• Build and manage resources (static, dynamic, templates)
• Design reusable prompts with typed arguments and return values
• Master context, state, elicitation, logging, and progress reporting
• Use built-in middleware (logging, caching, rate limiting, timing, injection, error handling)
• Build custom middleware with hooks, request/response modification, filtering, and metadata
• Implement sampling, tool use, structured output, multi-turn conversations, and fallbacks
• Work with background tasks, dependencies, lifespan, pagination, and storage backends
• Understand and implement handlers for logging, sampling, elicitation, and background tasks
• Use and build providers (local, filesystem, proxy, skills, and custom providers)
• Apply transforms to convert resources and prompts into tools
• Secure your servers with authentication, token auth, authorization, and OAuth
How this course is different
• Build things incrementally instead of jumping between disconnected examples
• Learn how FastMCP pieces fit together as a system
• See real-world patterns for production AI backends
• Avoid common mistakes around context, state, and middleware
• Gain skills that transfer directly to AI agent platforms and tooling ecosystems
By the end of this course, you’ll be confident building robust, secure, and extensible AI systems using FastMCP, from simple tools to advanced, production-ready architectures.
