How long does it take to complete the course?

The course takes approximately 4 hours to complete. It is structured into multiple sections that combine conceptual explanations with hands-on demonstrations and practical projects, allowing learners to progress from foundational knowledge to building and deploying real MCP servers within a relatively short timeframe.

Can I preview a course before enrolling?

Yes, you can preview the first video and view the syllabus before you enroll. You must purchase the course to access content not included in the preview.

When will I have access to the lectures and assignments?

If you decide to enroll in the course before the session start date, you will have access to all of the lecture videos and readings for the course. You’ll be able to submit assignments once the session starts.

What will I get when I enroll?

Once you enroll and your session begins, you will have access to all videos and other resources, including reading items and the course discussion forum. You’ll be able to view and submit practice assessments, and complete required graded assignments to earn a grade and a Course Certificate.

When will I receive my Course Certificate?

If you complete the course successfully, your electronic Course Certificate will be added to your Accomplishments page - from there, you can print your Course Certificate or add it to your LinkedIn profile.

Why can’t I audit this course?

This course is currently available only to learners who have paid or received financial aid, when available.

Is financial aid available?

Yes. In select learning programs, you can apply for financial aid or a scholarship if you can’t afford the enrollment fee. If fin aid or scholarship is available for your learning program selection, you’ll find a link to apply on the description page.

Model Context Protocol – Fundamentals to Advanced Use

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Model Context Protocol – Fundamentals to Advanced Use

Instructor: Packt - Course Instructors

Included with

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8 modules

Gain insight into a topic and learn the fundamentals.

Intermediate level

Recommended experience

8 hours to complete

Flexible schedule

Learn at your own pace

8 modules

Gain insight into a topic and learn the fundamentals.

Intermediate level

Recommended experience

8 hours to complete

Flexible schedule

Learn at your own pace

What you'll learn

Understand the architecture of the Model Context Protocol and how servers, clients, and hosts communicate to enable scalable AI tool integrations.
Build and test MCP servers from scratch, integrating tools, resources, and prompts to extend the functionality of AI applications.
Implement MCP communication transports including STDIO, Server-Sent Events, and Streamable HTTP for reliable system interactions.
Deploy production-ready MCP servers remotely and integrate them with development environments and AI assistants for real-world use cases.

Skills you'll gain

Tools you'll learn

Details to know

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There are 8 modules in this course

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.

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.

What's included

3 videos1 reading

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.

What's included

6 videos1 assignment

6 videosTotal 30 minutes

MCP Breakdown3 minutes
Server and Clients - How They Work - Full Overview7 minutes
MCP - The Universal Adapter for AI Applications - Deep Dive8 minutes
The Problems MCP Solves for LLMs2 minutes
Advantages of MCP for LLMs and AI Agents5 minutes
Common Questions - MCP Server and Tool Calling and APIs3 minutes

1 assignmentTotal 15 minutes

MCP Quick Overview - Assessment15 minutes

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.

What's included

8 videos1 assignment

8 videosTotal 47 minutes

MCP Architecture and MCP Server Core Architecture Overview4 minutes
How Does it All Work - MCP Servers, Clients, and Host5 minutes
MCP Transports Deep Dive7 minutes
The Three MCP Transports - STDIO Pros and Cons5 minutes
SSE - How it Works and Pros and Cons5 minutes
Streamable HTTP - How it Works and Pros and Cons5 minutes
Communication Lifecycle - STDIO and Streamable MCP Overview for Remote Servers7 minutes
MCP Summary - The Architecture of a Collaborative Ecosystem7 minutes

1 assignmentTotal 15 minutes

MCP Deep Dive - Main Concepts - Assessment15 minutes

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.

What's included

10 videos1 assignment

10 videosTotal 100 minutes

Build MCP Servers - Dev Tools You Need5 minutes
Hands-on: Running our First Community MCP Server in Claude Desktop11 minutes
Building our First MCP Server and Testing Using MCP Inspector - Local STDIO13 minutes
Adding Another Tool to Our MCP Server and Testing3 minutes
Testing Our MCP Server in Claude Desktop5 minutes
Building a Sqlite MCP Server - Community Chatter MCP Server14 minutes
SQL MCP Server - Getting Countries14 minutes
Adding More Tools to Our MCP Server - World Countries MCP Server13 minutes
Installing the World SQL MCP Server in Claude Desktop and Testing11 minutes
Running our MCP Server in VS Code with Copilot12 minutes

1 assignmentTotal 15 minutes

MCP Hands-on - Build Simple MCPs and Testing - Assessment15 minutes

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.

What's included

5 videos1 assignment

5 videosTotal 44 minutes

MCP Primitives Overview4 minutes
Creating a Prompt MCP Server and Testing9 minutes
MCP Resources - Get Book Inventory Resources7 minutes
MCP Server - Resources Adding More Resources and Testing in Claude Desktop12 minutes
Using Streamable HTTP Transport Type to Run an MCP Server - Deep Dive12 minutes

1 assignmentTotal 15 minutes

MCP Hands-on: MCP Resources and Prompts - Assessment15 minutes

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.

What's included

5 videos1 assignment

5 videosTotal 36 minutes

Building the Recipe MCP Server - Setup and Saving Recipes11 minutes
Adding More Tools to the Recipe MCP Server10 minutes
Adding Resources to MCP Server3 minutes
Adding Prompts to the MCP Server4 minutes
Testing our Recipe MCP Server in Claude Desktop and Debugging8 minutes

1 assignmentTotal 15 minutes

MCP Hands-on: Build an End-to-End - E2E - MCP Server - Assessment15 minutes

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.

What's included

3 videos1 assignment

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.

What's included

1 video2 assignments

Instructor

Packt - Course Instructors

Packt

1,725 Courses482,219 learners

Offered by

Packt

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Frequently asked questions

Model Context Protocol (MCP) is a framework that allows AI applications and large language models (LLMs) to connect with external tools, data sources, and services in a structured and scalable way. It acts as a universal adapter that enables AI systems to interact with servers, APIs, and resources more effectively. As AI agents and LLM-powered applications become more common, MCP is increasingly relevant because it standardizes how these systems access tools, exchange context, and perform complex tasks across different environments.

This course provides a comprehensive introduction to the Model Context Protocol, starting with fundamental concepts and progressing to advanced implementation techniques. It covers the architecture of MCP, how servers and clients interact, the role of transports such as STDIO, SSE, and Streamable HTTP, and the problems MCP solves for LLM-based systems. The course also includes extensive hands-on practice where learners build MCP servers, integrate tools and resources, create prompts, test implementations in environments like Claude Desktop and VS Code, and deploy MCP servers remotely.

After completing this course, you will understand how MCP works and how it enables AI applications to interact with external tools and services. You will be able to design and build MCP servers, add tools and resources to them, implement prompts, test servers locally, and deploy them remotely. You will also gain practical experience integrating MCP with development tools and AI platforms, allowing you to build more capable AI agents and applications that leverage external data and functionality.

Learners should have basic familiarity with programming concepts and general software development workflows. Some understanding of APIs, client-server architecture, and AI or large language models will be helpful for understanding the concepts more quickly. However, the course introduces MCP concepts from the ground up, making it accessible to developers who are new to the protocol but interested in building AI-integrated applications.

This course is designed for developers, AI engineers, and technical professionals who want to learn how to integrate tools and external systems with AI applications using MCP. It is also suitable for individuals building AI agents, working with LLM platforms, or exploring ways to extend the capabilities of AI systems through standardized tool integration and server-based architectures.