WEBVTT

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But as I say, it's quite interesting that that happened.

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It gave us an opportunity to understand more about remote servers and their stability and the like.

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Okay.

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So now the final one I want to show you is completely optional.

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I have done this one for myself and I've enjoyed it.

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You do not need to for one moment.

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So please take this as an optional thing.

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There is a service called, uh, that's that's, uh, it's called Financial Datasets AI.

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And it is a provider of real time financial information.

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But that does come at a price.

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So they charge about $0.01 for every lookup.

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And they are they're a good company.

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They are a, uh they're also part of the recommended MCP servers from, from anthropic.

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So they direct you to this and it's used by, uh, lots of data scientists.

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So it's active and the data science community, uh, and, um, they are they're yeah.

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As I say, they're perfectly, uh, decent, but they are a little bit pricey because $0.01 per lookup

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doesn't sound like it's going to break the bank, but if you're going to do a lot of looking up, then

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it could do.

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And also they have a minimum $20 that you have to put there to then draw down against.

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So I put in my $20.

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I haven't yet got very far through it, but I've been enjoying having that $20.

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You should only do this if you're comfortable with that.

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And we're going to be building something that we'll be looking and trying to simulate trading financial

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markets.

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And for me, it was well worth 20 bucks to be able to to play with this.

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But it's a preference thing.

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You can stick with a free service which is down right now.

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When it's running, you will get free stock prices, but you'll only get I think it's 25 a day.

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It's something like that.

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Uh, and then you'd have to make another key if you wanted to, which you can keep doing, but it's

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a bit of a bore.

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Uh, so, uh, yeah, there's the free option.

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Should you should you be okay to do that?

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But if you don't mind forking out 20 bucks or your local equivalent, then this is a great service.

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And by the way, you should go and explore on Macpaw or one of the others, uh, glamour AI and see

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if you can find a better provider of financial data that you prefer at a price point that you like,

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but I've gone ahead with the $20.

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Now, the other thing about this one is that there's a slightly more tricky process for setting it up,

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but that is good because it gives us a chance to experiment with with another way that this is sometimes

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done.

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So let me take you through that.

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And even if you decide you don't want to pay the 20 bucks, it's still worth you seeing this process

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because it's a it is a fairly common kind as well.

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In the past, we've used commands like UVF and Mpcs which allow you to run.

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UVF lets you run like a tool, um, a package that's, uh, that's Pip installed, but it's not actually

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installed.

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It's able to run it without needing to install it.

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But this, this tool works differently.

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The financial data service, they need you if we if we go to their their entry on MCP.

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So let me show it to you.

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So this is the financial data sets MCP server that connects to their service and what they need you

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to do.

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These are these are the tools that they provide.

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Get income statements, get balance sheets, cash flow current price prices and news.

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So they've got a lot of stuff here that's like really great.

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And it's real time.

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Um, so you have to, uh, install it properly as a project as a, as a Python project.

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So first you'll go in and type git clone and the, the, the, um, the folder, uh, and do this in

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your projects folder.

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So in your home directory you have a projects uh folder.

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And you will clone this in there.

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Um, we, it says if you don't have you've installed install it.

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But luckily we do have, you've installed of course.

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And then you just follow their instructions, do exactly what they say.

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Uh, then they need you to create a local file in their, in their project in this MCP server project.

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And you populate that with the financial data sets API key that we are about to set up.

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And then that's all you don't need to do that last step.

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That is all that you need to do.

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And by the way, you'll be able to give Claude access to this as well, should you wish.

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Claude desktop so you could be talking with Claude about the stock market.

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That's like an added boost.

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Of course, you could do that to all of the MCP server tools that we've been talking about.

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All right.

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So if I come back here, if I just bring up the terminal, uh, I just want to show you that this is

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if I go up a directory and look in my projects directory, you will see that there is indeed an MCP

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server directory there because I cloned in there.

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And this is it.

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And there is if I look at hidden files as well, you'll see that there is a dot env file that I've created

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and that I've populated.

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So this is now set up and running for me, installed locally as another project alongside the agents

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project.

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And so if you do wish to splash out and be part of this real time market data, then the next step is

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to go to financial datasets where you can sign up for an account, which I've done.

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And when you come in, you get a dashboard after you've signed up like this and you can see I made a

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bunch of requests as I've been trying different things.

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And when you go to billing here and to credits.

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This is where you manage putting a bit of a top up on your account.

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It takes a $20 minimum and you can see here the costs of API's.

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It's free to get company facts and earnings releases.

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A stock price is $0.01 per request.

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Crypto prices is $0.01 per request.

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And then there's some other things down here as well.

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But some of them I don't think are exposed through the MCP server.

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So that is that is what you can can look to set up and create an API key should you wish to.

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And then once you've done that, then as usual, you put that API key into the EMV file.

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In Financial Data sets API key.

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You actually have to put that into EMV files.

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In are EMV file.

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That's right here, because we'll use it directly in our accounts code, but also in the env file of

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the project where the MCP server is running.

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Okay.

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And with this it is time to run this.

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So the way that we actually specify the MCP tool to run is a bit different now.

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We're still using the second technique here.

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So we're still running a local MCP server that's going to call remotely, but we're running it based

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on a project that we have downloaded using git clone.

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So the way we have to do this is we specify the directory where this project is.

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It's right here for me in AD projects MCP server for you it will be wherever you've put it.

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And if you're on a PC there'll be backslashes and uh, but it should probably be.

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It will be called MCP server if you cloned it that way.

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We need to pass in the key like this.

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Now, it's interesting because we did set a env file in this project, but it turns out that you need

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to do this for it to work.

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So it may be that it doesn't use the env file, but.

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But I followed its instructions.

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I put it there too.

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So we'll specify it this way.

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And then we, we specify uh, the parameters to run this.

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And the way we run it, it isn't us this time because we're just running their Python code.

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So it's UV minus minus directory and then the directory where it is run server.py.

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And if you look in this directory you'll see that there is a Python script Server.py.

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That launches their server and we'll pass in our environment variable.

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So we will run this.

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And then the code the MCP server code is identical to before.

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So simple.

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Let's see what tools we get access to.

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We get tools to get income statements, balance sheets, cash flow, historic prices, and a bunch of

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crypto stuff too, if you're interested.

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And then we will quite simply call our MCP server the same way as always, we will pass in our requests

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to get Amazon's share price.

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This time, we're going to be spending a cent for the privilege of learning Amazon's share price.

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But it does come through.

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We get the share price.

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It's $170 apparently, and it's dropped today 2.45% drop.

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So we get a bunch of extra information to boot.

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And that concludes our last of our MCP servers for today.

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But the exercises for you, of course, is to explore more.

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You now know all of the techniques for hooking up to MCP servers.

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Which means that, yeah, the world is open to you.

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Do some browsing.

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Look on the marketplace, find some interesting looking MCP servers, and then put them to good use.

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Uh, build an agent, ask them some questions and explore and enjoy it.

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And put some examples in community contributions, or post them on on LinkedIn so I can weigh in.

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I would love to see what you're doing.

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The the joy of this is that it opens up so many capabilities that you can easily integrate with your

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own agent.

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So go do it.

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And so there you have it.

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We've done one, two and three.

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And for number two we did a couple of different ways of, of uh, of, of using, of calling the code

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one where we did a git clone and ran it the whole thing locally.

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And that wraps up day three of week six.

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We've only got two days left.

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We're coming onto the very, very home stretch.

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And I want to make sure that we that we bring it home with a brilliant, brilliant final project.

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Great fun.

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I can't wait to tell you about it next time.
