Overview

In this course, you'll become an AI architect by learning the Model Context Protocol (MCP). You'll build sophisticated, agentic systems where multiple AI tools, databases, and servers work together as one. You will write your own Python-based MCP clients (the "brain") that use LLMs to plan and execute tasks, and your own MCP servers (the "tools") that perform specific jobs. Your final project is to build "PriceScout," an autonomous bot that can understand a request, scrape websites, query a database, and deliver a complete analysis. Master the skills to build the next generation of interconnected AI.

Syllabus

Introduction to Model Context Protocol (MCP)

Discover what Model Context Protocol (MCP) is, why it matters, and how it standardizes AI app interactions—learn to build and use MCP servers and clients for interoperable workflows.

MCP Fundamentals

Understand MCP's role in connecting AI apps to external tools, MCP core components, real-world use cases, and complete a hands-on job search assistant walkthrough.

MCP Workflow Analysis

Analyze AI workflows, identify inefficiencies caused by manual tool handoffs, and envision improvements using the Model Context Protocol (MCP) for seamless integration.

MCP Architecture & Roles

Explore the MCP architecture, roles of host, client, and server, communication via JSON-RPC, transport mechanisms, and lifecycle phases of an MCP workflow.

Designing MCP Architecture

Design a smart home system using MCP by mapping roles, tracing JSON-RPC communication, categorizing capabilities, and choosing suitable transport mechanisms.

MCP Server Features

Explore the three core MCP server features—tools, resources, and prompts—that enable LLMs to act, access data, and use guided templates for effective user interactions.

MCP Client Features

Explore key MCP client features: roots (directory restrictions), sampling (LLM requests), and elicitation (user info requests) for secure, flexible workflows.

Building your First MCP Application

Learn to build your first MCP server app using the Python SDK, implement tools, resources, and prompts, and debug/test with Claude Desktop and MCP Inspector.

Building MCP Servers

Learn to build an MCP server that provides calculator tools, mathematical constants as resources, and calculation prompts for AI applications, using FastMCP and best development practices.

Building MCP Clients

Learn how to build, run, and test MCP clients using both Standard IO and Streamable HTTP, with step-by-step implementation, debugging, and a client-server exercise.

Building Chatbots and Agents with MCP

Learn to build chatbots and autonomous agents with MCP by integrating multiple servers, configuring clients, leveraging LLMs, and managing tool execution and agent memory.

Course Review

Review your MCP course journey, recap key concepts and skills, and get ready to apply your knowledge in the final project and beyond.

PriceScout: Agentic Analyst

Build the "PriceScout" AI agent. Write a client that uses an LLM to command custom scraper and database servers to analyze competitor pricing automatically.