Mastering Model Context Protocol (Mcp): A Practical Guide

Design robust AI backends with MCP: context-rich, secure, and ready for deployment.

What you'll learn

Understand MCP architecture and JSON-RPC basics.

Spin up and configure a FastMCP server.

Build MCP clients over SSE, streamable-http, and stdio.

Leverage MCP Tools, Resources, Prompts, Roots, Discovery, Sampling.

Secure MCP endpoints with OAuth 2.1 via Auth0.

Apply FastAPI integration, composition, proxy, and Docker patterns.

Requirements

Solid intermediate-level Python skills

Hands-on experience with Large Language Models (LLMs), especially tool calling

Fundamental software-engineering knowledge

Basic understanding of HTTP or similar client-server protocols

Description

Mastering Model Context Protocol (MCP) is your practical guide to building robust, secure, and production-ready AI backends using the FastMCP ecosystem.This course walks you through every step—from spinning up a minimal MCP server to deploying a full-stack application that integrates LangGraph, FastAPI, and OAuth 2.1 security.You’ll learn how to design modular, extensible systems that provide high-quality context to LLMs through modern protocols and best practices. With a strong focus on hands-on development, this course prepares you to build scalable MCP-powered applications that are ready for real-world use.Course HighlightsMCP FundamentalsSet up a basic FastMCP server and client. Understand the JSON-RPC request/response cycle and handle errors effectively.Transport MethodsWork with SSE, streamable-http (stateless & stateful), and stdio. Learn how to switch between transports and apply them in different scenarios.Advanced MCP FeaturesImplement key features like Tools, Resources, Prompts, Discovery, Roots, and Sampling to create dynamic and adaptive context pipelines.LangGraph IntegrationBuild a LangGraph client that interacts with your MCP server and generates intelligent, human-like responses using stateful logic.Security with OAuth 2.1Secure your endpoints using Auth0 and OAuth 2.1. Apply scopes, token management, and best practices for safe deployments.FastAPI & Proxy PatternsEmbed MCP into FastAPI, compose services for modularity, and create proxy bridges to support legacy systems or alternate transports.Full-Stack Deployment (Capstone)Combine all components—frontend, API, MCP server, and LLM backend—into a Dockerized, production-ready solution.By the end of this course, you’ll not only understand the theory behind MCP but also have the skills to build, secure, and deploy it in modern AI workflows.Whether you're a developer exploring LLM infrastructure or an engineer building context-aware systems, this course gives you the practical tools to take your AI applications to the next level.Let’s build the next generation of intelligent, context-driven systems :-)

Overview

Section 1: Before you enroll…

Lecture 1 Prerequisites

Lecture 2 My Teaching style

Section 2: Introduction

Lecture 3 A Brief History of AI Data Integration

Section 3: Understanding JSON-RPC – A High-Level Overview

Lecture 4 JSON-RPC High-Level Overview

Section 4: Project Setup

Lecture 5 Project Resources & Quick Setup

Lecture 6 Setting Up Your Project: Git, venv & Dependency Install - Full Walkthrough

Section 5: OPTIONAL: Tool / Function Calling Recap

Lecture 7 Tool Calling Theory & Practice

Section 6: Getting Started with MCP in practice

Lecture 8 Setting Up Your First MCP Server

Lecture 9 Low-Level Shell Interaction with the MCP Server

Lecture 10 Connecting to the MCP Server from a Python Client

Section 7: Transports - Model Context Protocol

Lecture 11 Comparing MCP Transports: stdio vs. SSE vs. streamable-http

Lecture 12 Building a Server & Client with stdio

Lecture 13 Building a Server & Client with SSE

Lecture 14 Building a Server & Client with streamable-http

Section 8: MCP Capabilities - Beyond Tools

Lecture 15 Difference between Tools, Resources, Prompts

Lecture 16 MCP Server with Resources & Prompts

Lecture 17 MCP Client with Resources & Prompts

Section 9: From MCP SDK to FastMCP v2

Lecture 18 From MCP SDK to FastMCP v2

Section 10: FastMCP - The Context Object

Lecture 19 Why the Context Object matters (Theory)

Lecture 20 Stateful Server with Notifications & Log Messages

Lecture 21 Using the Context Object in the MCP Client

Section 11: Discovery

Lecture 22 Static Discovery vs. Dynamic Discovery

Lecture 23 Dynamic Discovery: Server & Client

Section 12: Roots

Lecture 24 Roots Theory & Usecases

Lecture 25 Roots: Server & Client

Section 13: Sampling

Lecture 26 What is sampling and when you might need it

Lecture 27 Sampling Server

Lecture 28 Sampling Client

Section 14: Integrate MCP with a modern GenAI framework

Lecture 29 Connecting LangGraph Client to an MCP Server

Section 15: OAuth 2.1 Authorization

Lecture 30 OAuth Flow – Theory

Lecture 31 Auth0 vs Identity Provider – Setting up the Authorization Server

Lecture 32 Managing Sensitive Information with Environment File

Lecture 33 Building a Secure MCP Server

Lecture 34 Accessing an MCP Server with an Authorized Client

Section 16: FastAPI Integration

Lecture 35 Why FastAPI and FastMCP are a Perfect Match

Lecture 36 Mounting an MCP Server in a FastAPI App

Lecture 37 Turning a FastAPI App into a MCP Server

Section 17: Composition

Lecture 38 Composing Multiple MCP Servers with FastMCP

Section 18: Capstone Project

Lecture 39 Application Demo & Key Technologies

Lecture 40 MCP Server – Code Walkthrough

Lecture 41 Agent Class – Code Walkthrough

Lecture 42 FastAPI Server – Code Walkthrough

Section 19: Thank you!

Lecture 43 Congrations for finishing this course :-)

Junior to intermediate Python developers with hands-on AI/LLM experience who want to dive deep into the Model Context Protocol (MCP).