Model Context Protocol – Fundamentals to Advanced Use

Packt via Coursera

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Details

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Provider

Coursera
Pricing

Paid Course
Languages

English
Certificate

Certificate Available
Effort

8 hours
Sessions

Self-Paced
Level

Intermediate
Subtitles

English

Found in

Overview

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This course features Coursera Coach! A smarter way to learn with interactive, real-time conversations that help you test your knowledge, challenge assumptions, and deepen your understanding as you progress through the course. Modern AI systems rely on structured communication between models, tools, and applications. In this course, you will learn how the Model Context Protocol (MCP) enables seamless interaction between large language models, tools, and external systems. You’ll gain a clear understanding of MCP architecture, server-client interactions, and how MCP solves key limitations in AI integrations. By exploring both theory and real-world implementations, you will build the skills needed to design scalable AI tool integrations and develop powerful AI-enabled applications. The course begins with a foundational overview of MCP, explaining how the protocol works, the problems it solves for LLMs, and why it is considered a universal adapter for AI applications. You’ll explore the core architecture, server-client-host relationships, and communication transports such as STDIO, Server-Sent Events (SSE), and Streamable HTTP. These lessons provide the conceptual framework required to understand how MCP systems operate within modern AI ecosystems. As the course progresses, you will move into hands-on development. You’ll build MCP servers from scratch, integrate tools, create resources and prompts, and test your implementations using development environments like Claude Desktop and VS Code. Through practical projects—such as building SQL-based MCP servers and real-world data integrations—you will learn how to develop functional MCP services and expand their capabilities. In the final sections, you will build a complete end-to-end MCP server project, integrate prompts and resources, and deploy your server remotely to production environments. This course is ideal for developers, AI engineers, and technical professionals interested in AI tooling and LLM integration. A basic understanding of programming, APIs, and software development concepts is recommended. The difficulty level is intermediate. By the end of the course, you will be able to design MCP architectures, build and test MCP servers, integrate tools and resources for AI applications, and deploy scalable MCP services that extend the capabilities of modern AI systems.

Syllabus

Introduction

In this module, we will introduce the course, discuss the prerequisites needed to follow along successfully, and provide a quick demo that highlights what you will learn throughout the program. We will also walk through the overall structure of the course so you clearly understand the learning path from foundational MCP concepts to advanced practical implementations.

MCP Quick Overview

In this module, we will break down the MCP framework and explore its fundamental components, including servers, clients, and tool integrations. We will examine how MCP acts as a universal adapter for AI applications, understand the problems it solves for LLMs, and explore the advantages it provides for building scalable AI agents and systems.

MCP Deep Dive - Main Concepts

In this module, we will explore the internal architecture of MCP and understand how its core components—servers, clients, and hosts—work together to form a functional system. We will also dive deep into MCP transport mechanisms including STDIO, SSE, and Streamable HTTP, analyze their pros and cons, and understand the communication lifecycle used for local and remote MCP servers.

MCP Hands-on - Build Simple MCPs and Testing

In this module, we will move from theory to practical implementation by building MCP servers step-by-step. You will learn how to use development tools such as MCP Inspector, Claude Desktop, and VS Code to build, test, and debug MCP servers while also integrating SQL databases and expanding server capabilities with additional tools.

MCP Hands-on: MCP Resources and Prompts

In this module, we will explore MCP primitives and learn how resources and prompts extend the capabilities of MCP servers. You will build prompt-based and resource-based MCP servers, integrate external data sources such as book inventories, and test the functionality within Claude Desktop while also exploring Streamable HTTP as a transport mechanism.

MCP Hands-on: Build an End-to-End - E2E - MCP Server

In this module, we will build a complete end-to-end MCP server using a practical recipe management project. You will create tools for saving and managing recipes, integrate resources and prompts to enhance functionality, and test the entire system in Claude Desktop while debugging and refining the server implementation.

MCP Hands-on: Deploying an MCP Server Remotely

In this module, we will learn how to deploy MCP servers to a remote environment using platforms such as Render. You will prepare the server for deployment, test the remote server through Claude Desktop, and run the deployed MCP server within VS Code while ensuring proper communication and functionality.

Wrap up

In this module, we will review the key concepts covered throughout the course and connect the theoretical MCP foundations with the hands-on projects you built. We will also discuss next steps and opportunities for applying MCP to build more advanced AI agents, integrations, and real-world AI systems.