
Explore MCP's client-server architecture, transports, capabilities, and JSON RPC 2.0 messaging to understand tools, resources, and prompts across hosts and servers.
Explore MCP prompts, predefined templates that standardize server interactions and expand into full instructions. Learn to build argument-free and argument-taking prompts, and orchestrate multi-step workflows alongside resources and tools.
Build a sandboxed MCP filesystem server from scratch, implementing safe path resolution. Perform read, write, and list operations within a controlled workspace.
Switch from stdio to streamable HTTP in the MCP transport layer, enable startup logs and robust error handling with logging to standard error, and explore inspector-based debugging.
This hands-on course provides a practical introduction to the Model Context Protocol (MCP), the open standard that enables AI applications to securely connect to external tools, data sources, and services.
If you've ever wondered how AI assistants can read files, query databases, call APIs, or interact with external systems, this course is for you.
We focus on learning by doing. There are no theory-heavy slides or unnecessary complexity. Instead, you'll build real MCP servers from scratch and connect them to AI clients using the official Python SDK.
You'll start by understanding what MCP is, why it was created, and how it standardizes communication between AI models and external capabilities.
Then we'll move straight into practice.
You'll set up your development environment, build your first MCP server with FastMCP, and connect it to real clients for testing and development.
Throughout the course, you'll explore concepts including:
Model Context Protocol (MCP)
MCP servers and clients
FastMCP
Tools
Resources
Prompts
MCP Inspector
Cursor integration
File system server
SQLite database server
REST API integration
Transport protocols
Error handling and logging
Good practices for MCP servers
You'll build practical examples including a sandboxed file system server, a SQLite database server, and an MCP server that exposes external REST API.
Although the examples are written in Python, MCP is a language-agnostic protocol with SDKs available for Python, TypeScript, C#, Go, Java, Rust, and other languages. The concepts you'll learn transfer directly regardless of the language you use in production.
Everything is explained through practical examples designed to help you understand not only how MCP works internally, but also how to build reliable integrations that extend the capabilities of modern AI applications.
By the end of this course, you'll be able to confidently build MCP servers, expose tools and resources to AI clients, integrate external systems, and understand how MCP fits into the rapidly growing ecosystem of AI engineering.
If you want a clear, beginner-friendly, and hands-on introduction to the Model Context Protocol, this course is for you.