MCP for Practical Builders
Learn Model Context Protocol from scratch — how it works, how to build servers in Python, and how to give AI assistants safe access to your files, databases, and APIs. Ends with a complete hands-on project.
Course modules
01The Problem MCP Solves
Start here, even if you've never written code. What 'Model Context Protocol' actually means, in plain English — and the simple problem it was built to solve.
How MCP Works
The three-part architecture, three primitives, and the JSON-RPC communication model that makes MCP tick.
Setting Up Your Environment
Install the Python MCP SDK, get Claude Desktop ready, and verify your setup with a minimal working server.
Your First MCP Server
Build a real multi-tool server, connect it to Claude Desktop, and understand the complete request-to-response flow.
File System Tools
Build a safe file server with read, write, list, and delete tools — and learn why safe_path() is the most important function you will write.
Database Tools
Build a SQLite MCP server with safe query and insert tools — and learn why parameterized queries are non-negotiable.
Web and API Tools
Build async tools that fetch webpage content and call REST APIs — with URL validation, timeouts, and content size limits built in.
Resources and Prompts
Expose structured data as MCP resources with URI patterns, and define reusable prompt templates that give Claude consistent starting points.
Security Patterns
Six rules every MCP server must follow — from path validation and command injection prevention to rate limiting, input sanitization, and audit logging.
Hands-on Project: Personal Content Assistant
Build a complete MCP server that combines file, web, and database tools into a single personal content assistant — and see the whole system working together.
Where to go next
AI Agents — From Basics to Multi-Agent Systems
Build AI agents from scratch — single-agent patterns, multi-agent pipelines, memory, observability with Langfuse, and a complete hands-on project. Covers LangGraph, CrewAI, AutoGen, and Agno with real code examples.
AI Ops
The difference between a demo and a product that survives real users: monitoring, cost control, security, guardrails, and rollback. The operational layer that keeps AI systems safe, affordable, and trustworthy in production.