This course is designed for Software Engineers, DevOps, and AI Developers who want to build modular, reusable toolsets for LLMs. This course is for those who want to understand the protocol level without being tied to a specific framework like LangChain.

Basic proficiency in Python and familiarity with JSON-RPC or REST APIs.

Course Outline:

1. Introduction to MCP
2. The Context Problem: Why “RAG” isn’t always enough
3. What is MCP? History and the shift towards standardized interfaces.
4. Core Components: The Host-Client-Server relationship
5. Lab: Setting up the MCP inspector and environment
6. Building Standalone Servers with FastMCP
7. Setting up: using uv and Python MCP SDK
8. Understanding Transports: stdio and HTTP/SSE
9. Defining Resources: Exposing static and dynamic data (read-only)
10. Defining Tools: Creating executable functions with type-safe schemas
11. Lifecycle: Initializing, listing capabilities, and handling requests
12. Lab: Creating a “System Monitor” server that reads local CPU/RAM
13. Connecting to Native Hosts
14. Integrating with Claude: set up configuration
15. Using MCP in IDEs: Connecting your local server to VS Code
16. Standard Input/Output vs Streamable HTTP: stdio vs SSE/HTTP
17. Lab: For the above topic
18. The Three Primitives in Depth
19. Resources: Using URI template to feed context
20. Tools: Handling complex arguments and returning structured content
21. Prompts: Creating reusable “Slash Commands” & instruction templates
22. Error Handling: how to signal failures to the LLM gracefully
23. Lab: Building a sever that wraps public weather or stock API
24. Building Custom Clients & Security
25. Custom Python Script: that acts as an MCP client
26. Communication: via subprocesses and JSON-RPC
27. Security best practices: Tool permissions and “Human-in-the-loop” patterns
28. Deploying MCP: From local scripts to Dockerized remote servers
29. Lab/Demo