WEBVTT

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If you are using CPS or want to learn how to use them, this new MCP toolkit developed by Docker will

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save you a lot of time and make it much easier.

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It's really impressive.

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And in this video I'm going to show you exactly how this works and how to use it.

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MCP stands for Model Context Protocol.

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It's an open standard created by anthropic that helps AI models talk to real world tools like calendars,

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CRM, emails, or your local files.

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So basically, instead of building custom connectors, every time you want to give you a access to something,

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you can use MCP as a shared language between models and tools.

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So MCP solves that by acting like a bridge.

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Once you set up your model, can read emails, write code, update CRM, or fetch live data all securely,

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and you can do all those things from one app.

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Now, while MCP is a powerful concept and you can already use it for a lot of useful things and use

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cases.

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It's still early days, so the ecosystem is kind of messy right now.

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There are a lot of apps and tools to choose from, but not much guidance.

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Many MCP servers are open source, so you can find them on GitHub, but they require manual setup.

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So normally you would need to go to GitHub.

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Find the right repository for the MCP you want to use, and then set everything yourself.

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And that includes downloading the code, managing dependencies, and in some cases even hosting the

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service on your own machine and connecting llms to tools like Cloud Desktop App or Visual Studio Code,

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or even a simple API takes a lot of manual setup.

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So you need to know how to set up environment variables, credentials, permissions, and so on.

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And because of that, if you are just starting out and you don't have any programming background, It's

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tough to know which MCP servers you can trust.

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A lot of people end up relying on random blog posts, GitHub threads, or community lists and hope the

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setup will work and that nothing shady is inside the repo.

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Yes, so the setup process is usually pretty manual, so you often need to clone the code, install

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dependencies, and figure out how to run these services locally.

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And when you are running tools that can access your machine, that can become a problem at some point

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because security is one of the biggest concerns here.

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So many of these tools have wide access to your system, and credentials are sometimes passed in plain

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text.

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So that means it's possible for sensitive information like environment variables, API keys to be exposed.

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And if you are working in a team, it's even harder to use because most setups don't come with basic

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protections like permissions controls.

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And that's a big reason why adoption has been slow, especially on the enterprise level.

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Also, every time you try to install one of these MSPs and it fails, it often still sends API requests.

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So at the end, you might get a lot of errors and spend money for each API call.

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So yes, it can be really frustrating.

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And that's where Docker comes in.

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Just to give you a bit of context, Docker is a really useful tool that lets developers run apps in

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isolated environments called containers.

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So it's widely used in programming and DevOps.

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And honestly, I could make a whole separate course just on Docker itself.

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So instead of manually setting up your own server and figuring out how to set up all this technical

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stuff, you can now launch verified MCP servers in separate containers with just a couple of clicks

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from Docker Desktop.

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Docker desktop helps you run these MCP Servers safely without messing with your system setup.

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So what that means is you can access all these MCP tools created by developers.

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And Docker released this toolkit where you can see a whole list of MCP servers.

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And these are all secured verified servers that you can access.

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And you can connect these MCC to apps like cloud desktop app cursor, Visual Studio Code.

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So to the MCP clients and automate a lot of tasks.

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So I will show you exactly how to set this up and how it works step by step.

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I will go through how to get started with Docker MCP toolkit, how to install it and launch your first

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server, and how to use some of the tools on real examples.

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So let's get started.

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Obviously to start you will need Docker Desktop.

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So go to docker.com and you should land on this page.

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So simply download Docker Desktop on your operating system and install it.

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Now, once you're done, please go ahead and open it.

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Now, before just a few days ago, you had to open extensions in the left hand menu and search for MCP

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toolkit.

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As you can see, it has been deprecated and you have to use the integrated Docker MCP toolkit in Docker

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Desktop 3.42 or later.

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So Docker MCP toolkit is a Docker desktop extension allowing allowing to connect Dockerized MCP servers

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to MCP clients.

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Yes.

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So a few days ago, the team integrated MCP toolkit into the user interface, and now it has a dedicated

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tab, as you can see here.

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Uh, it's still in beta, so you don't need to install this extension.

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You can go straight to the MCP toolkit.

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Now when you click on it.

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It gives you a whole set of tools to easily install and manage verified MCP servers.

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They have a catalog of nearly 120 MCP that are already set up, but I'm sure they will expand it soon.

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All of these run in isolated Docker containers, so you don't have to worry about them accessing anything

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on your system directly.

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So as the first step is dedicated for servers.

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The second one for MCP.

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And the last one for clients.

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So here you can connect all these MCP to apps like cloud desktop app, cursor or even some internal

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custom apps.

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All right.

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So now let's connect it to a client.

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So we are going to use Cloud Desktop app as our client as our MCP client.

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So you just simply need to click here to connect and don't need to edit configuration file like we did

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to connect the cloud desktop app to N810.

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And as I said, this works with other tools like cursor or even your own local setup.

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So you can browse and connect Dockerized MCP servers to your favorite MCP clients.

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So as you can see, you have access to some of the very popular apps like GitHub, like notion, like

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AWS, who can also give your agents access to MCP server like Azure.

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You also got 11 apps.

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You can use fire, which is really great to scrape the internet and Google Maps.

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And when you scroll down, I'm sure you will be familiar with some of the apps.

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We also have Shopify.

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Slack.

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Wolfram.

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Wikipedia.

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Webflow.

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Now when you click on the specific MCP server.

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Let's use Google Maps as an example.

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Here we have an overview.

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So this tool is for interacting with the Google Maps API.

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And here you have more details such as links to repository.

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And this is very important.

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Available tools.

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So here we can check what tools we can use for this specific MCP server.

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So in Google Maps we've got maps directions.

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To get directions between two points you have maps distance matrix to calculate travel distance and

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time for multiple origins and destinations.

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We also have maps, search places to search for, places using Google Places API.

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And when you scroll down.

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You have more details about the tools, which is really useful because by visiting this page.

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An overview.

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You can figure out what tools you would need to use for your specific use case.

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Next, when you go to tools tab, here you have a short descriptions of the tools the SMTP server offers.

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And then also very important configuration.

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So here in that case we just only need to provide API keys.

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Because to be able to use this server you have to provide API keys.

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And I'm going to show you how to find API keys for some of the services like Google Maps, perplexity,

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pickle and so on.

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If you want, you can go to repository to check the GitHub.

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And here you can read through the documentation to learn more about this particular Amqp server.

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So if you are interested, you can learn in more detail how it works and how to configure it manually.

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As you can see, this specific repo contains a collection of MCP servers.

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So for example, let's open Google Maps.

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And here we have some additional information.

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Google maps MCP server.

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For the Google Maps API we have tools.

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Again you have setup if you'd like to learn how to set up this manually.

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So you have JSON snippets.

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And on the left hand side we have all the files.

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So the code packages dependencies instructions and so on.

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Yes.

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So this MCP toolkit by Docker makes things so easy because the only thing you need to do is to for example

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go to stripe.

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Added to your MCP servers.

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But in order to do that, you have to provide credentials.

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So configure this MCP server.

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But this is really simple.

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You need to find API keys and that's it.

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So go to configuration here.

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Paste your API keys.

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And it's all set up for you.

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So you don't need to mess up with code and do everything manually.

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So this tool is really great because it can save you a lot of time and money.

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All right.

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Now let's use Cloud Desktop App as our MCP client.

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So once we install it go to Docker Desktop.

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Launch it go to settings.

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If you have never used Cloud Desktop app before you need to enable developer mode.

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There should be an option to switch it on, then go to developer.

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And here you should see all the integrations.

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For now I've got just an A10.

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So my cloud desktop app is connected to an A10.

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And now when you go to Docker Desktop.

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In the MCP toolkit, click on Clients Connect.

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Now your MCP toolkit should be connected to Cloud Desktop app.

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But before you will be able to see the connection inside Cloud Desktop app, you need to restart the

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Cloud desktop app completely.

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Just make sure to quit it.

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And this is really important.

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If you don't do that, it will not work.

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Now open it again.

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You can see that MCP Docker connection appeared.

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So let's open it.

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And just to confirm it's running now.

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So this means that our MCP toolkit has been successfully connected to cloud desktop app.

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Now, if for any reason your connection failed, just close the cloud desktop app, go to Docker Desktop.

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Press on this button again and then restart the cloud desktop app.

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And that's why Docker Desktop is so powerful, because you don't need to configure each MCP and each

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tool individually.

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You can simply connect all of them CP servers and the tools via Docker desktop and manage all of them

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From this up.

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All right.

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Now, once our MCP toolkit is connected to the cloud desktop app, let's start configuring MCP.

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So let's search for Google Maps.

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Configuration.

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And here we need to provide Google Maps API keys first before we start generating API keys and connecting

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applications to the MCP toolkit.

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Let's actually plan what we are going to do in the next part of this tutorial.

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So let's add a few MCP from a catalog.

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As our MCP servers.

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Because you can combine multiple MCP to complete a single task to get much better results.

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And that's why this is such a game changer.

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So this is our task.

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Let's say you are based in London and your remote team is mostly around Manchester.

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You are planning a weekend meet up somewhere close to Manchester where you can all work together in

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person.

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We'll use Google Maps to find towns within 35 minutes driving distance from Manchester.

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So when we search for Google Maps, MCP, the catalogue and go to tools.

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We'll use two tools offered by the MCP server.

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Let's use map search tool to find towns or areas nearby with coworking spaces or venues.

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And also let's use maps distance matrix to calculate actual driving times from Manchester, including

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traffic conditions.

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You can also use other tools, for example maps directions to get directions between two points or maps.

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Place details to get detailed information about a specific place.

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But for this, we are going to use other MCP perplexity to make this our server and use its tools.

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So then we can use perplexity MCP to research the pros and cons of each town, because we want to know

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if it's affordable, is it to get to, and if it's a good place for entertainment or to relax after

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work.

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Specifically, we are going to use Perplexity research tool to perform deep research using perplexity

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API and get a comprehensive research response.

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So yes, you can do all these things in one conversation inside the cloud desktop app without switching

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tabs, uh, or between websites.

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So you just install servers you need connect them to cloud and then I can start using them.

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So now I'm going to show you how to generate this API keys.

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First we need to create a project on Google Cloud.

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So search for console.

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Cloud Google.

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Open the first link.

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Then login to your Google account.

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Go to your console.

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Now here you can create a new project.

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So press on.

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Create new project.

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Give it a name.

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Create.

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Now just wait a moment, then select the project you just created.

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And make sure you are in the right project.

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Then what we need to do we need to enable APIs we want to use.

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So in that case we want to use.

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Let's check.

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Two tools maps search places and maps distance matrix.

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So let's search for it.

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So I'm going to type maps search.

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And this is the first result.

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So places API.

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So click on that.

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Then we need to enable API.

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So click enable.

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So you'll be able to get detailed information about 100 million places.

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Now let's copy the API key.

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Let's go to Google Maps platform.

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Here you can also restrict key.

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I'm going to select maybe later.

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Now when I go to APIs and services.

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I can see all the tools I can use from Google Maps platform.

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And when I scroll down.

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I can see that places API is enabled because I can disable it right here.

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Now we also want to Activate the Distance Matrix API to click on enable it.

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If you want to calculate the travel time and distance for multiple destinations.

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Alright, perfect.

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Now let's go back to the Docker desktop.

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And under configuration I'm going to remove this API key and creating one.

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Let's paste the API keys we copied.

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Confirm.

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All right.

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Perfect.

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And now we should have access to this MCP in our cloud desktop app.

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But we need to restart the app.

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So let's do it.

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And now every large language model offered by anthropic.

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Should have access to this MCP.

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So here you can switch between different large language models.

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You have access to cloud sonnet for cloud opus for animal models.

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And now when you press on this button you should see that you have access to MCP Docker.

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And when you expand it.

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These are all the tools that I connected in Docker desktop.

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So all MCP servers with their tools are connected to cloud desktop app.

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So here I have a file called Google Maps and Perplexity.

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And I can easily switch on and off each tool separately.

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So here we have our maps distance matrix And map search places where I.

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Perfect.

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Now let's actually give it a test.

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So I'm going to open the cloud desktop app again.

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And I'm going to ask it to use Google Maps tool to find out what cities near Manchester can I reach

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within 35 minutes driving during evening rush hour traffic?

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Let's send it.

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And let's see what happens.

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So I will help you find.

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Okay, so now cloud is asking about permission to use this MCP server.

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So as you can see maps search places MCP Docker.

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So I'm going to allow once.

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Let me search for nearby places and then check driving times.

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Okay.

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Now it's going to use this MCP server again.

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So this time I'm going to allow always.

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So you don't need to give it permission every time.

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Now cloud is asking for the permission to use our second tool.

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So maps distance matrix from this MCP server.

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Okay I'm going to allow always.

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Okay.

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Now let's wait a moment.

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Perfect.

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Now we got results.

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Here you can see the entire process of performing this search.

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So I will help you find cities in Manchester that are reachable within 35 minutes during rush hour.

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Let me search for nearby places and then check driving times during typical rush hour conditions.

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And here you can see that cloud was using Maps Places tool from the Google Maps.

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And you can also expand it to see the request.

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You can see the response.

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Right.

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Great.

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Then let me search for more specific cities and towns around Manchester.

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It was using map search places tool three times.

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Okay, great.

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And then now let me check driving times from Manchester to these nearby cities during evening rush hour.

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I will use the distance matrix to get multiple destinations at once.

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And here it was using the second tool.

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So maps distance matrix.

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Let's let's expand it more Of driving college in Manchester, UK.

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Destinations.

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Okay, so it's found quite a few cities based on my search using Google Maps data.

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Here are the cities and towns in Manchester that you can reach within 35 minutes of driving during typical

25:03.460 --> 25:04.540
traffic conditions.

25:05.300 --> 25:07.420
And here you have the list of the cities.

25:09.340 --> 25:10.020
All right.

25:10.180 --> 25:12.300
Just outside the 35 minute range.

25:12.900 --> 25:18.220
Please note that these times are based on current traffic data and may vary during actual evening rush

25:18.220 --> 25:18.420
hour.

25:18.460 --> 25:23.500
Typically 5 to 7 p.m. during heavy rush hour traffic.

25:23.500 --> 25:29.260
Some of these journeys could take longer, particularly those involving major motorways like right.

25:29.260 --> 25:34.140
For the most accurate real time estimates during your specific travel time, I would recommend checking

25:34.140 --> 25:36.740
Google Maps or similar navigation apps just before your journey.

25:37.380 --> 25:45.980
As you can see, cloud was using current real time traffic data and the output is pretty detailed and

25:46.220 --> 25:49.660
accurate in the second part of this task.

25:51.060 --> 25:53.980
Now let's ask perplexity.

25:54.660 --> 25:55.860
So use perplexity.

26:00.420 --> 26:01.500
Let me just check.

26:03.780 --> 26:04.580
Okay.

26:06.900 --> 26:09.460
To research the pros and cons of each town.

26:12.540 --> 26:12.980
Okay.

26:13.020 --> 26:14.140
Let's just copy it.

26:18.460 --> 26:19.460
And paste it here.

26:24.460 --> 26:25.260
Now let's see.

26:26.380 --> 26:29.100
I will use perplexity to research the pros and cons of each town.

26:30.300 --> 26:32.180
Now it's gonna ask for permission.

26:32.540 --> 26:33.260
Going to error.

26:33.300 --> 26:33.900
Always.

26:37.980 --> 26:43.660
I think it will take maximum two minutes, because perplexity is going to do a deep research.

26:43.660 --> 26:43.660
Deepresearch.

26:49.020 --> 26:50.780
Let's give it some time to process it.

26:54.180 --> 26:58.580
All right, now let's check the response based on my research using perplexity.

26:58.620 --> 27:02.460
Here are comprehensive analysis of the pros and cons of each town in Manchester.

27:03.060 --> 27:05.300
Salford 70 minutes from Manchester.

27:05.300 --> 27:12.780
Pros excellent transport links with frequent trams every ten minutes and direct Metrolink connections.

27:12.820 --> 27:23.660
Cons most expensive for solo living utilities limited local options okay residence okay Stockport.

27:23.700 --> 27:24.780
Good transport.

27:27.060 --> 27:27.780
All right.

27:30.100 --> 27:30.660
Outcome.

27:30.700 --> 27:31.620
Very affordable.

27:38.300 --> 27:39.700
Works and transport.

27:40.500 --> 27:40.900
Limited.

27:40.900 --> 27:42.300
Evening entertainment locally.

27:49.020 --> 27:51.380
Chi Minh infrastructure.

27:52.580 --> 27:54.020
Best local entertainment.

27:56.860 --> 27:57.220
All right.

27:57.220 --> 27:57.660
Summary.

27:57.700 --> 28:00.780
Recommendations for affordability.

28:00.820 --> 28:04.500
Bolton or Oldham offer the best value for quick commute.

28:06.020 --> 28:15.580
33 minutes or 30 minutes, or fastest for entertainment or income has the best local scene Bolton for

28:15.580 --> 28:15.980
students.

28:16.020 --> 28:22.660
Nightlife nightlife best overall balance value offers good transport, moderate costs and reasonable

28:22.900 --> 28:23.660
amenities.

28:24.100 --> 28:30.260
Avoid budget conscious Salford and income are significantly more expensive.

28:30.820 --> 28:37.500
The choice depends on your priorities, whether you value short commutes, local entertainment or keeping

28:37.500 --> 28:38.180
costs low.

28:39.180 --> 28:39.740
Perfect.

28:39.740 --> 28:46.020
So we are able to get real time data from Google Maps using the Google Maps MCP.

28:48.340 --> 28:52.660
The actual driving times, not just rough estimates.

28:53.180 --> 29:00.220
Then we used one of the best AI search engines on the market to perplexity, to do deep research on

29:00.260 --> 29:06.220
each location and all of that just by asking a single question inside the cloud app.

29:10.460 --> 29:12.540
Now, this was just an example.

29:12.580 --> 29:19.260
Now imagine how much time you could save when you start combining even more MCP servers and tools,

29:19.460 --> 29:23.820
especially as Docker team the continuous verifying more of them.

29:24.580 --> 29:31.660
All right, now I'm going to show you how to generate API keys in perplexity so you can set up perplexity

29:31.660 --> 29:32.900
as your MCP server.

29:33.420 --> 29:35.740
So first go to perplexity comm.

29:36.100 --> 29:42.220
If you haven't used perplexity before, just for the context, it's one of the best AI powered search

29:42.220 --> 29:42.820
engines.

29:43.020 --> 29:49.500
Right now on the market, it takes your question, searches the web in real time, and then gives you

29:49.500 --> 29:52.020
a clear, concise answer.

29:52.460 --> 29:55.580
Usually the answer is backed by resources.

29:55.820 --> 29:57.540
You can click on verify.

29:58.260 --> 30:01.460
Also, it doesn't hallucinate as much as other llms.

30:01.740 --> 30:08.020
So the perplexity AI is an advanced AI powered web search engine designed to provide accurate, trusted

30:08.020 --> 30:14.300
and real time answers to user to user questions by combining large language models with live internet

30:14.300 --> 30:14.780
search.

30:15.260 --> 30:21.500
It uses sophisticated natural language processing to understand queries contextually, and then searches

30:21.500 --> 30:28.340
authoritative online sources to synthesize concise, human like responses, complete with citations

30:28.340 --> 30:30.380
to the original sources for verification.

30:31.180 --> 30:38.580
Founded in August 2022, perplexity AI has quickly grown into a significant player in the AI search

30:38.580 --> 30:46.260
space valued at around $14 billion as of mid 2025, with a user base processing tens of millions of

30:46.260 --> 30:47.100
queries daily.

30:47.460 --> 30:53.060
The company offers a free version and paid subscriptions, which unlock more advanced AI models and

30:53.060 --> 30:58.700
features such as multimodal inputs, image documents, follow up questions, capabilities and enhanced

30:58.700 --> 30:59.780
search depth.

31:00.100 --> 31:07.140
And the key features conversational search so allows users to ask follow up questions and maintain context

31:07.180 --> 31:10.980
within a session, making it more interactive than traditional search engines.

31:11.460 --> 31:12.620
Return data access.

31:12.620 --> 31:19.380
Unlike many AI models limited to static training data, perplexity assesses live web data enabling app

31:20.740 --> 31:28.020
enabling up to date answers including live sports scores, election results and news events to search

31:28.060 --> 31:28.780
modes.

31:29.100 --> 31:35.740
Quick search for fast, straightforward answers, plus search for deeper, more tailored responses that

31:35.740 --> 31:37.740
may involve clarifying questions.

31:38.140 --> 31:39.340
All right, how it works.

31:39.980 --> 31:43.980
It uses a blend of proprietary and third party llms, including GPT four.

31:46.540 --> 31:53.900
GPT three and its own sonar model to interpret queries, retrieve relevant information from the web,

31:53.900 --> 31:56.420
and generate coherent summaries.

31:56.660 --> 32:01.380
It continuously crawls and indexes web content to keep its knowledge base current.

32:02.380 --> 32:08.140
In use cases, perplexity AI is used for research, content summarization, language translation, data

32:08.180 --> 32:12.220
analysis, and general information retrieval across various industries.

32:12.260 --> 32:18.540
It is especially valued for providing verifiable, up to date information quickly and efficiently.

32:18.860 --> 32:25.100
In summary, perplexity is a next generation AI search engine that combines conversational AI with real

32:25.140 --> 32:31.100
time web search to deliver accurate source answers, helping users explore and verify information more

32:31.100 --> 32:35.100
efficiently than traditional search engines or standalone AI chatbots.

32:35.540 --> 32:36.860
Perplexity is very powerful.

32:36.900 --> 32:43.970
I personally prefer to use it to ask question to search the web instead of going to ChatGPT or cloth

32:43.970 --> 32:47.330
and ask the models to get an answer?

32:48.170 --> 32:48.530
Yes.

32:48.530 --> 32:54.730
So I usually go to perplexity first when I have a question, because it pulls real time info from the

32:54.730 --> 33:02.010
web so you can get more current and accurate responses than you would from ChatGPT or cloth.

33:02.050 --> 33:07.370
Even if you are using the search web features also without the extra fluff.

33:07.850 --> 33:16.570
So in order to connect perplexity to MCP toolkit in Docker Desktop, let's go to Perplexity Comm.

33:17.530 --> 33:18.610
Create your account.

33:18.770 --> 33:19.850
I'm going to sign in.

33:22.450 --> 33:24.850
Then go to your account at the bottom left corner.

33:27.570 --> 33:28.490
Click on account.

33:32.050 --> 33:32.970
API keys.

33:37.330 --> 33:42.010
But before you will be able to generate your API keys, you have to top up your account.

33:44.330 --> 33:45.330
Then create key.

33:49.170 --> 33:50.010
Copy it.

33:50.930 --> 33:53.770
Go back to Docker Desktop.

33:59.610 --> 34:00.570
Go to catalog.

34:02.170 --> 34:03.370
Search for perplexity.

34:06.450 --> 34:07.690
Go to configuration.

34:09.330 --> 34:13.530
I'm going to delete the API keys I already have and create new one.

34:14.770 --> 34:19.490
Paste your perplexity API key here and confirm.

34:20.930 --> 34:21.290
Great!

34:21.290 --> 34:24.850
And now when you go to Cloud Desktop app.

34:27.250 --> 34:28.370
And go to tools.

34:31.010 --> 34:36.610
In the MCP Docker you should be able to see three tools from perplexity.

34:36.650 --> 34:39.730
So perplexity ask perplexity, reason and perplexity research.

34:40.210 --> 34:42.130
So these are exactly the same tools.

34:45.130 --> 34:46.210
You can see here.

34:48.970 --> 34:52.530
So now you are able to use perplexity inside Cloud desktop app.

34:53.970 --> 34:59.610
Now just to let you know, using perplexity research is quite expensive because it uses deep research

35:00.130 --> 35:04.170
to find relevant information for for your questions.

35:04.890 --> 35:06.010
So just keep it in mind.

35:07.170 --> 35:11.570
Now I'm going to show you how to connect another popular tool if I recall.

35:12.130 --> 35:13.970
So it's kind of like perplexity.

35:13.970 --> 35:21.970
But instead of giving you answers, it scrapes retail websites and pulls and pulls out raw data.

35:22.610 --> 35:29.730
So perplexity gives you ready answers based on data, while five crore gives you data to work with.

35:30.650 --> 35:32.530
So let's go to File Crawl.

35:34.570 --> 35:40.490
As you can see, this is the fast, reliable web scraper for LMS, you can power your AI apps with clean

35:40.490 --> 35:42.130
data cloud from any website.

35:42.810 --> 35:45.530
It's also open source, the same as N810.

35:46.530 --> 35:49.370
You can start for free with 500 credits.

35:51.970 --> 35:53.650
It's trusted by the top companies.

36:06.170 --> 36:08.650
As you can see, you can add it to m.c.p.s.

36:16.850 --> 36:18.530
All right, so let's create an account.

36:22.050 --> 36:22.690
Sign up.

36:25.770 --> 36:28.050
Provide email and password and then login.

36:28.690 --> 36:30.850
I'm going to login because I already have an account.

36:31.970 --> 36:33.930
Then simply go to API keys.

36:36.290 --> 36:37.810
Here you can create your API key.

36:39.570 --> 36:47.410
So give it a name for example Docker desktop and create.

36:51.250 --> 36:51.890
Copy.

36:53.450 --> 36:54.810
Go to Docker desktop.

36:56.850 --> 36:58.930
Search for file crawl.

37:05.850 --> 37:07.170
Go to configuration tab.

37:08.810 --> 37:09.810
Go to secrets.

37:14.690 --> 37:25.610
I'm going to remove the current API key and create new one just to demonstrate to you and confirm save.

37:26.330 --> 37:27.130
And that's it.

37:27.490 --> 37:31.330
It should be set up and you should be able to use it in your Google Cloud desktop.

37:32.530 --> 37:37.570
Actually let's check and you should have access to it.

37:37.610 --> 37:38.050
Tools.

37:40.530 --> 37:41.890
You can read the descriptions.

37:44.250 --> 37:45.770
Go to your desktop app.

37:45.930 --> 37:48.090
Let's check tools.

37:50.290 --> 37:50.730
Mocp.

37:50.770 --> 37:51.250
Docker.

37:51.770 --> 37:55.410
And as you can see now I can use all of the tools.

37:58.130 --> 37:58.490
All right.

37:58.490 --> 37:59.090
Perfect.

38:00.130 --> 38:00.610
All right.

38:00.610 --> 38:00.890
That's it.

38:00.890 --> 38:08.330
For this tutorial you learned how to use how to combine tools like Google Maps, perplexity and through

38:08.370 --> 38:15.290
MCP toolkit in in Docker and how how you can use it in cloud desktop app and how easy it is to build

38:15.290 --> 38:16.290
something useful.

38:16.850 --> 38:23.490
I strongly recommend you using MCP Toolkit in Docker Desktop because it can save you a lot of time,

38:23.890 --> 38:32.610
and also it can open a lot of possibilities, especially as more verified MCP get added over time.

38:33.370 --> 38:34.610
I hope this was helpful.

38:34.850 --> 38:36.930
Thanks for watching and see you in the next one.
