Model Context Protocol Fundamentals

Learn the core MCP concepts, architecture, transports, and FastMCP development workflow.

Why take this course?

Unlock the future of AI integration with the Model Context Protocol. This course is your gateway to understanding how to connect LLMs to any data source efficiently. Whether you are building smart assistants or complex AI agents, mastering MCP will give you the edge in creating scalable and interoperable AI solutions. Say goodbye to custom integrations and hello to a standardized, powerful ecosystem.

Course Modules

1Module 1: Motivation & Introduction

Learning Goals

Understand why MCP was created
Identify the problems with current LLM integrations
Learn the core philosophy of "Model Context Protocol"

2Module 2: Architecture Overview

Learning Goals

Identify the key participants in the MCP architecture: Host, Client, and Server.
Distinguish between the Data Layer and the Transport Layer in the MCP stack.
Define the three core server primitives: Tools, Resources, and Prompts.
Understand the client-server interaction flow, including initialization, discovery, execution, and notifications.

3Module 3: Discovery & Capability Negotiation

Learning Goals

Understand how clients discover server capabilities
Learn the standardized /list endpoint pattern
Understand dynamic capability updates through notifications
Recognize the JSON-RPC 2.0 format used for discovery

4Module 4: Building MCP Servers

Master server primitives and build your first MCP server with FastMCP.

Learning Goals

Master the three server primitives: Tools, Resources, and Prompts
Understand who controls each primitive (Model/App/User)
Learn the difference between Direct Resources and Resource Templates
Build a functional MCP server using FastMCP and Python
Test and debug your server using the MCP Inspector tool

5Module 5: Client Capabilities

Learning Goals

Understand the three core client features servers can leverage: Elicitation, Roots, and Sampling
Learn how servers request user information through Elicitation
Understand Roots as filesystem boundary communication (advisory, not security)
Master LLM sampling through clients with human-in-the-loop control