Introduction to MCP and Its Role in the Future of AI Agents

Understanding MCP’s Architecture

Diving Into MCP Servers and a Workflow Example

Building Your First MCP Server – Let’s Get Coding!

Connecting Your MCP Server to Claude for Desktop

Closing Thoughts and Next Steps

Introduction

( the primaryDescriptionMarkdown is long - several lines, include images, as long as you want )

In this course we <headline of what we do>. <why this technology is good> but <here's how learning it is problem>. 

In <the length of these lessons> we cover <three salient points about what we cover. Your BEST points>. Its <however long it is> and comes with <these bonus features, and reasons to buy. code, etc.>

It's taught by <author name> who <credibility, reason you should listen to them>. <social proof, if you have it yet>

<then you can list other supporting stuff you want to add, anything you want here>

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Example:
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We created a free course to create your first GraphQL server (from scratch) using Node and TypeScript. TypeScript and GraphQL are a powerful combo, but there's a lot of confusing edges if you're getting it setup the first time.

In this course we cover the basics of getting TypeScript setup with Node, how GraphQL works, and how to set up a GraphQL server using Apollo server. It's two hours long, it comes with complete code examples, and we walk through the code step-by-step.

It's taught by my friend, Shopify-engineer Hassan Djirdeh and he's integrated what he's learned using GraphQL in production as an engineer at Shopify. We've already had over 1,300 students enroll and it's been getting great reviews so far.

// ... and so on

How Model Context Protocol (MCP) will Change the Way we Build Al Apps and Why you Need to Learn It

In this course we’ll walk you through everything you need to know about MCP—from its core components and main concepts to practical implementations. We will focus specifically on building an MCP server, as it is likely the most useful and frequently used part of the MCP architecture that you will want to implement.

MCP in Practice: The Future of AI Agents

Hey everyone, welcome to the first lesson of our mini-course on AI and the Model Context Protocol, or MCP for short. In this lesson, we will cover the fundamentals: what MCP is, why it represents a significant advancement for developing AI applications, and the reasons to consider adopting it promptly. Let’s get started!

Before we even touch MCP, let’s make sure we’re on the same page about AI agents. These are basically AI applications designed to handle tasks autonomously. That’s the key word here. You give them a task, maybe some instructions, and that’s it—your job’s done. From there, the AI takes over. It searches for info, processes it, maybe tweaks it a bit, and delivers the result, all without you holding its hand. Pretty cool, right?

Why does this matter? Because MCP shines brightest when you’re building these kinds of AI agents, especially the complex ones. So, having at least a basic grip on what AI agents are sets us up nicely to understand why MCP is such a big deal.

Now, let’s examine MCP—the Model Context Protocol. I firmly believe it will shape the future of AI agents, and here’s why. At its core, MCP is a protocol, essentially a standardized framework, that simplifies how AI systems connect to external data and tools. It’s not some random idea a couple of buddies built up over a weekend. It was built by Anthropic, a big player in the AI world, and it’s open-source. That means it’s got serious backing and it’s here to stay.

Consider the traditional method of building AI applications: you manage multiple APIs, writing specific code for each, maintaining them, and updating them as needed. This approach functions, but it lacks flexibility. Adding a new service or replacing an existing one can be a cumbersome process. MCP addresses this challenge effectively. Think of it as a universal connector for AI—akin to a standardized plug—enabling seamless integration without the need for bespoke code for every connection.

To elaborate, most AI agents today, from basic to advanced, rely on direct API calls. Developers write the code, oversee the connections, and navigate the resulting complexity. This can feel disorganized, with each project following its own rules, making maintenance difficult. 

MCP introduces a different approach. It operates as a unified protocol, similar to how the Model-View-Controller (MVC) pattern structures backend applications. With MCP, there’s a collective agreement on how to link external resources—be it databases, APIs, or other tools—to AI applications. This standardization ensures predictability and ease of use. The architecture includes an MCP client (the application leveraging the AI) and MCP servers (the resources or tools), communicating via the protocol in a streamlined manner.

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Hey everyone, welcome to the first lesson of our mini-course on AI and the Model Context Protocol, or MCP for short. In this lesson, we will cover the fundamentals: what MCP is, why it represents a significant advancement for developing AI applications, and the reasons to consider adopting it promptly. Let’s get started!

## AI Agents: A Brief Overview

Before we even touch MCP, let’s make sure we’re on the same page about AI agents. These are basically AI applications designed to handle tasks autonomously. That’s the key word here. You give them a task, maybe some instructions, and that’s it—your job’s done. From there, the AI takes over. It searches for info, processes it, maybe tweaks it a bit, and delivers the result, all without you holding its hand. Pretty cool, right?

Why does this matter? Because MCP shines brightest when you’re building these kinds of AI agents, especially the complex ones. So, having at least a basic grip on what AI agents are sets us up nicely to understand why MCP is such a big deal.

## Understanding MCP

Now, let’s examine MCP—the Model Context Protocol. I firmly believe it will shape the future of AI agents, and here’s why. At its core, MCP is a protocol, essentially a standardized framework, that simplifies how AI systems connect to external data and tools. It’s not some random idea a couple of buddies built up over a weekend. It was built by Anthropic, a big player in the AI world, and it’s open-source. That means it’s got serious backing and it’s here to stay.

Consider the traditional method of building AI applications: you manage multiple APIs, writing specific code for each, maintaining them, and updating them as needed. This approach functions, but it lacks flexibility. Adding a new service or replacing an existing one can be a cumbersome process. MCP addresses this challenge effectively. Think of it as a universal connector for AI—akin to a standardized plug—enabling seamless integration without the need for bespoke code for every connection.

## Advantages Over Traditional Methods

To elaborate, most AI agents today, from basic to advanced, rely on direct API calls. Developers write the code, oversee the connections, and navigate the resulting complexity. This can feel disorganized, with each project following its own rules, making maintenance difficult. 

MCP introduces a different approach. It operates as a unified protocol, similar to how the Model-View-Controller (MVC) pattern structures backend applications. With MCP, there’s a collective agreement on how to link external resources—be it databases, APIs, or other tools—to AI applications. This standardization ensures predictability and ease of use. The architecture includes an MCP client (the application leveraging the AI) and MCP servers (the resources or tools), communicating via the protocol in a streamlined manner.

Maksym Mitusov

Okay. Okay. So let's go. So today, we are going to discuss MCP. I believe this

is like future for the AI agents. And we will look how it will change the way.

Basically, we build an AI apps and why exactly we need to learn it and

But before we ever start with the MCP, we need to cover one small topic, not so

small, maybe, but about the AI agents. So are you familiar with the agents?

Have you worked on when we heard about this? If no, yeah, basically, that's

So just in case I would read, you're going to use a couple of details, in short

, AI agents, those are AI applications that need to perform work autonomous

lives. And what word autonomously is basically the key here, right?

Because if we give it some task and give it some instructions, this is all that

And after that, we expect that AI would search the information, would go

through information, try to change it maybe in some way and do everything from

beginning to start autonomously and would give it just a result.

And it's important to know about AI agents, at least briefly, because MCP is

really great in giants a lot specifically when you're building AI agents up and

Okay. And then how about MCP? Have you heard about MCP?

Or at least something maybe, if no, that's completely fine, but just

interesting. Oh, okay, that's great. Okay. So we're on the same page.

And I would start from the basic basics anyway, and we would go then in more

In short, we are going to call today three main topics, basically what is MCP?

And you need to implement it as soon as possible, potentially. And exactly how

We would go over the really basic MCP application.

And it would give you the most basic level of the understanding, like the core

level from which you can build up even more complex apps.

So it would be small, but really powerful and good application for learning,

Okay. But before we move forward on, like a couple of words about myself.

My name is Maxine. I'm basically from Ukraine.

That's my name might sound a little bit strange to you.

And I actually have started initially as the full-stack developer.

My initial language used to be a JavaScript.

But then as actually AI started to emerge, I started actually learn more about

And started to learn and build more location.

And it's basically my second nature by now.

I'm starting for the deep-seak and finishing this MCP, for example, and builds

including candidate evaluation, content generation,

see optimization, different chat apps, so on, basically.

So we'll be with no one I'm talking about.

And before we even start to explain MCP, just in case I also want to address

between the classical implementation that working right now with, right?

And and like then we moved to a towards the MCP right now, all the agents from

the more basic one to more most complex one are actually built.

And this is like brings, first of all, like issues in terms of the supporting

And also how easily we can connect or disconnect the different services to our

So it's just kind of rigid way of building apps.

But it's not really that much flexible and actually easy to implement and easy

So NCP once again, model context protocol in like first and the foremost.

And you can think of it as first of all, this protocol is built by anthropic.

So on Tropic, it's pretty a large company.

So it's not something that's couple of guys decided to go out on the weekends

It's actually picked up by the big company.

Not only that, since it's a big company, it will be supported for a long time

and the more and more like popular different

applications and companies start to actually implement it inside.

And I really hope that it will soon become the form for a lot of applications.

Then also is worth mentioning that it's still emerging and new technology.

So first of all, whatever we are going to call here is all the basics will be

OK, there might be a single exchange changes in the future.

It might be like small documentation changes, but the core principles and idea

After we go through everything, even the docs will change, you would understand

But else of since this is new technology.

Right now, it might be sometimes not really stable.