Name: The Model Context Protocol (MCP)
Uploaded: 2025-06-26T21:27:10.000Z
Duration: 19 min 35 s
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Around the time, in September, we had an internal hackathon.

Everyone was free to build basically whatever they wanted to build.

But it turns out everyone just built on MCP.

Everyone's ideas were, oh, but what if we made this an MCP server?

I lead Cloud Relations here at Anthropic.

Member of technical staff at Anthropic and one of the co-creators of MCP.

Today we're going to be talking about the model context protocol and diving in deep into what it is and what's next.

But first, there's a lot of talk about MCP and not a lot of maybe real deep understanding of what it is.

Can we dive into how you view MCP and what it really means to be using MCP or building on it?

MCP is just a way for putting my workflow into an AI application in a very simple way.

I think that's how I really wanted it to be initially, or that's how we wanted it to be.

But it's just a way to give context to an application that uses another lab.

And it can be tools, it can be just raw context, whatever you like it to be.

How is that different than you calling an API or something like that?

It's passing this information from one place into the prompt, basically, of the model.

I think the question is, what do models interact with?

And they don't interact directly with APIs, they interact with prompts and tools and whatever you're giving the model to ingest.

And so MCP standardizes how you take that data from, whether it's an API or some internal data source or whatever it is, how you take that data and then actually give it to the model.

What are the main aspects of this protocol that has to follow?

The main part is that it's a protocol between the AI application that uses another lab.

And it exposes basically three main things.

It's tools, it's a thing called resources, which is just raw data that you could ingest into a RAG pipeline or whatever you wanted to do, and there's prompts.

And that's the three main things that a server can expose for now.

So tools are actions that the model can take out in the world.

Files, data, whatever kind of context you want to give the model.

What a user wants to put into the context window by themselves and is triggered by the user and just put into the context window and then they can edit it as they want to.

That's really what prompts are for, prompt templates in the end of the day.

We typically see that being implemented as a slash command.

If you're in the AI application of your choice, you would do a slash command and it would pull in the prompt template and save you time from having to write that out, whatever it is.

There's definitely a lot of nuance in there.

The most basic thing is that I worked on internal developer stuff and I got very quickly frustrated about having to copy things in and out of Cloud Desktop and then copying things back and forth between my IDE.

And that's just really what I was thinking about, how can I solve copying, pasting, the things I care about the most between these two applications.

And that's really the absolute origin of where MCP started, at least in my mind.

And then from there, I explained that to Justin, who is the other co-creator, and he really took it and ran it and then we together just built it out and built it into Cloud Desktop.

And I think there was a pivotal moment that you alluded to.

We weren't really sure, is this going to work?

But around the time, in September, we had an internal hackathon and everyone was free to build basically whatever they wanted to build.

And we had everything from people doing very standard things like Slack integration or things you would think of when you think MCP, up to people who steered their 3D printer as MCP.

And I love this, when it got into the real world, when Cloud got into the real world because of an MCP server.

I remember that too, when we were doing all these hackathon projects and there was no mandate to force people to use MCP.

Why did people gravitate towards MCP for all their projects?

I think it really was that standardization layer.

It just made it so much easier to add context to the application because the moment that Cloud is now integrated against MCP, that means as the server builder, you can build 1, 2, 10, 20, however many servers you want and you know that it will automatically work with that application.

I think that just gives you the ability to only think about one side and not have to think about the other side.

I think there's a bit of a magic moment when you teach Cloud something new using an MCP server for the first time and you see it takes action about something you care about and I feel that's a little bit of a moment of magic that I think MCP captures really well, which makes people so excited because within five minutes they have something going.

I've seen it myself and even experienced it where it almost feels like you take Cloud out of the box, so to speak.

All of a sudden, instead of just being this thing that is just right there outputting text, it's doing other things.

It's calling other applications, fetching other data, or even operating a 3D printer, which is a really crazy thing.

That does feel really special and I guess MCP allows that pretty seamlessly to some degree.

This was back in the end of summer, early fall, as we were doing this Hack Week and these other things.

When did we launch MCP and what did that look like?

I think everyone's response, as you can imagine, well, some people still have this response, is what's MCP, right?

We definitely could have named it better.

But you still get the MPC instead of MCP and then it makes me think NPC.

You had a lot of people asking, what is MCP?

Not just externally, but I also think internally because it was such a bottoms-up movement.

Initially people were like, oh, what is this thing?

What does it mean to give the model context?

Then as people started playing around with it and seeing it for themselves, I think that's where it actually slowly caught steam.

The turning point was when more and more clients started adopting.

I think the IDEs were the first to adopt.

That's created a lot of waves in the market to incentivize a lot more server providers to actually build servers.

You see so many times on social media, what is MCP?

A month later, a few days later, they're going to be like, this is the best thing ever.

It's now become, I think it's fair to say, industry standard of integration protocol.

There's nothing else in my mind that rivals it.

Going back to the launch, a key decision here was to actually make this open source.

That was pretty different in comparison to maybe previous efforts in this area that had been launched.

Can you explain the reasoning behind that decision and why did we open source it?

If you have a closed ecosystem for integrations and for a context to be provided to AI applications, then it isn't clear to the server builders or the integration builders, is that AI application going to be around forever?

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The Model Context Protocol (MCP)

context

awesome

basically

intelligence