So, look, I know I often start these things by saying today is a big day.
But here's the thing today is a big day.
It's a really big day.
So I am absolutely entitled to say welcome.
Welcome to week three, day three.
Today is a big day.
Today is of course MCP day and unless you've been living under a rock, you have heard the excitement,
the buzz, potentially the overhype around MCP that some people think is the next, next best thing
after sliced bread and some people think is overhyped and is on the way out.
Whatever your viewpoint, or even if you haven't ever heard of it before, by the end of today you're
going to know all about it.
And particularly you know about how to use it within an A-10.
Before we dive in, I want to give you the big picture here about MCP.
And it starts with me saying saying a few principles, which I'd like you to keep in mind during this.
The first of them is that there is a major, major innovation that sits behind a genetic AI.
And look, it's not MCP.
MCP is not the big deal about genetic AI.
The big deal is tools.
The big deal is this thing that I.
That I keep telling you about, about the way that you can get an LLM to generate tokens and you can
you can tell it that that by the way, as you generate these tokens, keep in mind that you could you
could tell me that you want me to run a few tools.
And this is what the tools do.
And you put that information in the system prompt that sets the scene for the LLM.
And then when it's generating tokens, it decides to call the tools.
So it's this clever, clever trick for making sure that you can get an LLM to make decisions about running
tools just from the tokens that it generates, and that that's the big thing about Agentic AI.
There's masses of excitement about MCP, but and a lot of it is very much warranted.
There's a lot of reason to be excited about MCP as we will cover, but some of it has gotten a bit confused
that people should be excited about tools.
That's the big thing, and they're getting it kind of conflated with MCP, which, as you'll discover,
is a great way to connect LMS to tools.
It's about being able to to reuse and share tools, but the real hype should be about tools rather than
MCP, which is just a great standard.
So I just want to make sure that that you keep that in the back of your mind.
Differentiate between the use of tools, which is the really big deal, and MCP, which is an exciting
standard which makes sharing tools even more possible.
Okay, with that in mind, let's dig in.
And of course, we should start with what?
What is MCP that I imagine I'm guessing actually probably like maybe 50%, 40% of, you know, already.
MCP stands for the Model Context Protocol from anthropic.
And it's something that was released towards the end of 2024.
And to start with, it wasn't all that much of a big deal.
It had some traction, but not much.
But then, wow, it really took off around March April 2025.
It became a really big deal indeed.
And anthropic themselves describe it as the USB-C port for AI applications.
And that's why there's the picture of a USB-C port.
People that are my Agentic AI course may remember the point when I got an image generated and it was
USB-A port, which was not really the point, but this you'll see.
This time I have improved.
This is indeed a USB-C port, which is how anthropic describe the MCP protocol.
And the other way they describe it is it is a standard.
Is it an it is an open source standard for how you connect AI applications to external systems.
And really by that they mean tools.
You can use it for a few other things too.
But tools is the thing that's really taken off.
So really, you should think of this as it's a way to connect AI applications to tools, to tools that
other people have written.
Not to your own tools, but to to tools all over the world.
That's what MCP is all about.
And so let's let's go a little bit more concrete.
What does this mean.
It actually is.
And what is it not.
So it is an easy way to use tools that somebody else wrote.
If somebody else built a tool and you didn't build it and you don't have it and you want to use it.
Then MCP gives you a really easy way just to stick it into your AI application.
And you may know that that programs like Claude, uh, you can just just connect it to MCP and it will
just use those tools.
So it's an easy way to use somebody else's tools.
It's also the flip side of that.
If you've written a tool yourself, if you're in the business of writing, uh, different APIs and you've
got something that could be a useful tool for an LLM, you can turn it into something that anyone else
can instantly reuse using MCP.
And so generally speaking, it's just an easy way to hook up llms to tools, even if you didn't write
the tools so you don't quite know what they do.
It's like something which describes it perfectly to an LLM and the game changer.
The reason it's such a huge deal isn't because there's anything massively innovative about it, and
anthropic is very clear about that.
They didn't invent anything that's hugely game changing.
The thing that's made it really take off is that it's been so widely adopted, everyone has taken it
as the standard, it's open source, so anyone can implement it and use it, and it's just really taken
off.
And it's one of these sort of ecosystem things that once lots of people are using it, it becomes a
kind of victim of its own success.
It's like suddenly there's so many people sharing tools using MCP that there's just masses to choose
from, and it's really convenient.
So the big the big thing about MCP is the massive number of tools that allows you to connect with really
easily and to cover some of the things that MCP is not, or some of the misconceptions.
The first point to make is that it's not the tools themselves.
MCP is, of course, the glue between your LM and the tools, or between something like N810 and the
tools.
It's the, the, the interface or it's a standard way to have that interface.
It's a way to take a tool and to to describe it with language, with, with just, just English and
then with a description of the parameters in English or in any natural language, in a way that an LM
can read that in and understand it and then use that tool well, all packaged together in one nice package.
That's all it is.
And the technology underlying it is nothing particularly new.
As I say, anthropic is very clear on this point.
It's using just common sense technologies.
It's really all about the adoption.
It's that it's it's taken off and everyone has embraced this way of doing it.
And look, it needs to be said.
MCP is all the rage at the moment, but it's still entirely possible that MCP might be more of a sort
of transitory fad.
It might be something that gets replaced by something else, but it's very, very hot at the moment.
But there are a number of other evolving approaches for integrating llms with tools.
There's skills also by anthropic that's more oriented around products like Claude.
But you could certainly people that use use a Claude skills now, also OpenAI skills, the same thing.
You certainly can see the parallels.
And then code mode is another new exciting idea that anthropic trying to package in with MCP, with
an idea that you can use it with MCP, but I'm sort of seeing that it might well end up being an alternative
to MCP.
So it's perfectly possible that MCP itself won't won't persist for a very long time.
Right now, of course, it's incredibly useful.
But but regardless, even if MCP in its current form doesn't last forever, then I think whatever replaces
it will have many of the same ideas, so it's still great to learn all about it as we will today.
So look, if you ever worked for a management consultant, one of the cliched things you sometimes hear
is that when you're making a convincing, compelling argument, it should come in threes.
There should be three powerful bullets, three slides, that kind of thing.
Well, I've now got three slides, each with three sections on them, three things to make, and they're
all a little bit technical.
So so the next video is going to get a little bit more into the sort of nitty gritty of how MCP works.
And just enough only the stuff that you kind of need to know for it all to fall into place when we use
it.
It's somewhat optional that you could listen to it quickly.
If you're not all that into it.
You don't need to know this, but I do think it's helpful context.
It's really good grounding for you.
So.
So put up with me if you wouldn't mind.
Sit through the next video, get a sense of the technology because then when we build it, hopefully
a lot of it will make sense.
Let's do this.