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Hands-On Introduction to Model Context Protocol (MCP)
Highest Rated
Rating: 4.7 out of 5(16 ratings)
259 students

Hands-On Introduction to Model Context Protocol (MCP)

Hands-on guide to building MCP servers with tools, resources and prompts
Created byLukasz Kallas
Last updated 8/2026
English
English [Auto],

What you'll learn

  • Understand what Model Context Protocol (MCP) is and why it's important for AI integrations
  • Build MCP servers that expose tools, resources, and prompts
  • Test MCP servers
  • Work with different transport mechanisms

Course content

3 sections • 17 lectures • 2h 53m total length
  • Welcome!6:35
  • Join Our Community0:04
  • What is MCP & Why to Use it7:54
  • Setup17:05

Requirements

  • Basic programming knowledge
  • Familiarity with using a terminal
  • Understanding of APIs and HTTP requests helpful but not required
  • Cursor or another code editor such as VS Code installed

Description

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.

Who this course is for:

  • Developers wanting to integrate AI models with external tools and data sources
  • Backend engineers building infrastructure for AI applications
  • Developers curious about the protocol side of AI tooling
  • Anyone interested in learning the Model Context Protocol standard