WEBVTT

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So, look, I know I often start these things by saying today is a big day.

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But here's the thing today is a big day.

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It's a really big day.

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So I am absolutely entitled to say welcome.

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Welcome to week three, day three.

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Today is a big day.

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Today is of course MCP day and unless you've been living under a rock, you have heard the excitement,

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the buzz, potentially the overhype around MCP that some people think is the next, next best thing

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after sliced bread and some people think is overhyped and is on the way out.

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Whatever your viewpoint, or even if you haven't ever heard of it before, by the end of today you're

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going to know all about it.

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And particularly you know about how to use it within an A-10.

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Before we dive in, I want to give you the big picture here about MCP.

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And it starts with me saying saying a few principles, which I'd like you to keep in mind during this.

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The first of them is that there is a major, major innovation that sits behind a genetic AI.

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And look, it's not MCP.

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MCP is not the big deal about genetic AI.

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The big deal is tools.

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The big deal is this thing that I.

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That I keep telling you about, about the way that you can get an LLM to generate tokens and you can

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you can tell it that that by the way, as you generate these tokens, keep in mind that you could you

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could tell me that you want me to run a few tools.

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And this is what the tools do.

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And you put that information in the system prompt that sets the scene for the LLM.

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And then when it's generating tokens, it decides to call the tools.

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So it's this clever, clever trick for making sure that you can get an LLM to make decisions about running

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tools just from the tokens that it generates, and that that's the big thing about Agentic AI.

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There's masses of excitement about MCP, but and a lot of it is very much warranted.

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There's a lot of reason to be excited about MCP as we will cover, but some of it has gotten a bit confused

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that people should be excited about tools.

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That's the big thing, and they're getting it kind of conflated with MCP, which, as you'll discover,

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is a great way to connect LMS to tools.

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It's about being able to to reuse and share tools, but the real hype should be about tools rather than

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MCP, which is just a great standard.

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So I just want to make sure that that you keep that in the back of your mind.

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Differentiate between the use of tools, which is the really big deal, and MCP, which is an exciting

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standard which makes sharing tools even more possible.

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Okay, with that in mind, let's dig in.

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And of course, we should start with what?

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What is MCP that I imagine I'm guessing actually probably like maybe 50%, 40% of, you know, already.

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MCP stands for the Model Context Protocol from anthropic.

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And it's something that was released towards the end of 2024.

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And to start with, it wasn't all that much of a big deal.

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It had some traction, but not much.

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But then, wow, it really took off around March April 2025.

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It became a really big deal indeed.

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And anthropic themselves describe it as the USB-C port for AI applications.

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And that's why there's the picture of a USB-C port.

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People that are my Agentic AI course may remember the point when I got an image generated and it was

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USB-A port, which was not really the point, but this you'll see.

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This time I have improved.

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This is indeed a USB-C port, which is how anthropic describe the MCP protocol.

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And the other way they describe it is it is a standard.

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Is it an it is an open source standard for how you connect AI applications to external systems.

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And really by that they mean tools.

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You can use it for a few other things too.

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But tools is the thing that's really taken off.

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So really, you should think of this as it's a way to connect AI applications to tools, to tools that

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other people have written.

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Not to your own tools, but to to tools all over the world.

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That's what MCP is all about.

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And so let's let's go a little bit more concrete.

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What does this mean.

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It actually is.

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And what is it not.

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So it is an easy way to use tools that somebody else wrote.

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If somebody else built a tool and you didn't build it and you don't have it and you want to use it.

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Then MCP gives you a really easy way just to stick it into your AI application.

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And you may know that that programs like Claude, uh, you can just just connect it to MCP and it will

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just use those tools.

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So it's an easy way to use somebody else's tools.

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It's also the flip side of that.

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If you've written a tool yourself, if you're in the business of writing, uh, different APIs and you've

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got something that could be a useful tool for an LLM, you can turn it into something that anyone else

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can instantly reuse using MCP.

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And so generally speaking, it's just an easy way to hook up llms to tools, even if you didn't write

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the tools so you don't quite know what they do.

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It's like something which describes it perfectly to an LLM and the game changer.

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The reason it's such a huge deal isn't because there's anything massively innovative about it, and

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anthropic is very clear about that.

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They didn't invent anything that's hugely game changing.

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The thing that's made it really take off is that it's been so widely adopted, everyone has taken it

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as the standard, it's open source, so anyone can implement it and use it, and it's just really taken

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off.

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And it's one of these sort of ecosystem things that once lots of people are using it, it becomes a

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kind of victim of its own success.

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It's like suddenly there's so many people sharing tools using MCP that there's just masses to choose

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from, and it's really convenient.

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So the big the big thing about MCP is the massive number of tools that allows you to connect with really

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easily and to cover some of the things that MCP is not, or some of the misconceptions.

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The first point to make is that it's not the tools themselves.

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MCP is, of course, the glue between your LM and the tools, or between something like N810 and the

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tools.

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It's the, the, the interface or it's a standard way to have that interface.

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It's a way to take a tool and to to describe it with language, with, with just, just English and

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then with a description of the parameters in English or in any natural language, in a way that an LM

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can read that in and understand it and then use that tool well, all packaged together in one nice package.

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That's all it is.

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And the technology underlying it is nothing particularly new.

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As I say, anthropic is very clear on this point.

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It's using just common sense technologies.

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It's really all about the adoption.

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It's that it's it's taken off and everyone has embraced this way of doing it.

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And look, it needs to be said.

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MCP is all the rage at the moment, but it's still entirely possible that MCP might be more of a sort

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of transitory fad.

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It might be something that gets replaced by something else, but it's very, very hot at the moment.

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But there are a number of other evolving approaches for integrating llms with tools.

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There's skills also by anthropic that's more oriented around products like Claude.

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But you could certainly people that use use a Claude skills now, also OpenAI skills, the same thing.

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You certainly can see the parallels.

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And then code mode is another new exciting idea that anthropic trying to package in with MCP, with

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an idea that you can use it with MCP, but I'm sort of seeing that it might well end up being an alternative

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to MCP.

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So it's perfectly possible that MCP itself won't won't persist for a very long time.

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Right now, of course, it's incredibly useful.

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But but regardless, even if MCP in its current form doesn't last forever, then I think whatever replaces

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it will have many of the same ideas, so it's still great to learn all about it as we will today.

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So look, if you ever worked for a management consultant, one of the cliched things you sometimes hear

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is that when you're making a convincing, compelling argument, it should come in threes.

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There should be three powerful bullets, three slides, that kind of thing.

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Well, I've now got three slides, each with three sections on them, three things to make, and they're

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all a little bit technical.

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So so the next video is going to get a little bit more into the sort of nitty gritty of how MCP works.

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And just enough only the stuff that you kind of need to know for it all to fall into place when we use

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it.

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It's somewhat optional that you could listen to it quickly.

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If you're not all that into it.

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You don't need to know this, but I do think it's helpful context.

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It's really good grounding for you.

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So.

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So put up with me if you wouldn't mind.

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Sit through the next video, get a sense of the technology because then when we build it, hopefully

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a lot of it will make sense.

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Let's do this.
