All right. Well, um, welcome everyone to

hopefully a live demo of the GitHub MCP

server. I'm Sam Marorrow. I'm the lead

developer building GitHub's MCP server.

Worked at GitHub for about four years.

And as you can see, I love snow sports.

Um and

>> hi, I'm Toby Padilla. I am a principal

product manager at GitHub, and I'm in

charge of our MCP initiatives, which

means the GitHub MCP server, the GitHub

MCP registry. I'm also a member of the

MCP steering committee working on the

open source MCP registry.

So, uh, firstly, I wanted to do a little

hands up. So, uh, hands up if you know

what an MCP server is. And now, keep

your hands up if you've ever used one.

a lot of hands and keep your hands up if

you've ever built one.

Oh, really? Awesome. You'll have to come

and talk to us after. I'd love to know

what you're building. Uh, so yeah,

firstly, Toby's just going to give a

little introduction of MCP and

particularly the GitHub MCP and what it

does. And then I'm going to go into a

demo and I'll show you kind of

installing from our registry into VS

Code. And then um I'll be working with

some GitHub issues and coding agent and

showing some fun stuff you can do. And

Toby's going to go through a few use

cases afterwards because 15 minutes is

not enough time to show everything we

can do. And uh we're not going to have

any time I'm afraid for Q&A. But if

anyone's got burning questions, we'll be

around after the talk. So please do come

up and let us know what your questions

are. Um, so Toby,

>> so what is MCP? And I think a lot of you

said that you're familiar with it, but

just briefly, MCP is a way for AI to

interact with the outside world. And

that primarily means two things. One is

that it can fetch context that the model

wasn't trained on. So this might be

information that was published after the

model's cut off training date, or it

could be information that was like

private if you had like internal

information that wasn't accessible to

the public web and the LLM got trained

on it. And secondly, it's a way for it

to interact with the outside world and

create side effects. So it could create

a file system or a file on your local

file system or a GitHub repo or a Figma

design. Um MCP is about 11 months old,

so it's not even a year old. Um

Anthropic created it last November. And

I like to say it was more of a social

innovation than a technical innovation

because there was pre-existing function

calling APIs, but they weren't getting a

lot of traction. So what Anthropic did

was create a standard around those APIs

um and then bootstrap the community with

30 high-quality reference servers

including the original GitHub MCP

server.

And this is a look at our MCP server. So

we've got a bunch of tools. Like I said,

Anthropic created the original one and

they told us it was the most popular of

the original set of MCP servers. So we

worked with them to take ownership of

the MCP server and we ended up rewriting

it and go because it was in Typescript

and we've launched it and it was quite

popular. We'll just talk through what it

does and and how it engaged with the

community.

Um, the week that we launched it, it was

actually the most popular open source

project on all of GitHub. So, it was

exciting to see that there was sort of

this pent-up demand to drive uh GitHub's

platform with agents.

And now I'll hand it over Sam so he can

show you what it does.

>> All right. So, somebody told me there's

some outages and things going on. So,

fingers crossed everything works. Uh so

firstly this is our MCP registry web

page and um I'm going to just install it

and

hopefully everything will work. You can

see that we have install for VS Code and

VS Code insiders. Um VS Code is actually

the most comprehensive MCP host that

exists. So it has the complete coverage

of the latest version of the spec. It

has more support for the MCP protocol

than any of anthropics hosts. And if you

use insiders, it's sort of the nightly

build for VS Code. And this is where

they're launching a lot of super

innovative MCP stuff. So if you're doing

MCP work, I would encourage you to get

VS Code insiders and test your MCP

servers in there.

Okay. So I should be assuming the

authentication completes successfully.

Yeah, I think I'm logged in now.

It's going slow. So I think the outage

is affecting stuff slightly but not

enough to actually stop me. So

uh firstly let's actually just give

stuff a go and see what happens.

So um uh some of you are probably

already familiar but um this is the

agent mode tab and I'll make it full

screen so it's easier to see. And

firstly I'll just try a super simple

prompt. can you list some issues from a

repo in my um account?

And you can see it immediately ran the

list issues from our MCP server. And

because I didn't ask it to do anything

with that information, it's just listing

them out. Um and um it showed me the

closed ones, too. That's cool. Uh MCP

also has a really interesting feature

called prompts which is like a pre-baked

prompts and um

uh if you want to enable a more complex

agentic kind of workflow from a single

command, they're like slash commands. So

um this one assigned coding agent. It

asks me for the repository I wanted to

work on and then it generates a prompt.

And this is our MCP server providing

that. And now it's just going to go

through a list of issues from the

repository

and it's going to decide uh like which

ones it thinks an agent can work on and

then it's just going to assign coding

agents. So, I'm going to allow it uh for

this session

and um

we'll have to give it a moment, but uh

it should hopefully assign a few issues

and um

yeah, like it it decided that some of

the issues aren't worth assigning and

that's good. That's that's kind of what

the prompt was intended to do. So

ideally lowquality issues and things

that needs further specification don't

get triggered. So anyway, uh that's the

MCP prompt and I do think that's pretty

cool. The um

for a slightly more fun one, let's try

aggregating

information from the GitHub MCP server

repo. So for this prompt, we're going to

get it to pull down recent pull requests

and then generate an issue celebrating

the work that was done in them. Um, so

as you can see, it immediately listed

the pull requests from the correct repo.

And now it's just processing the

information. And in a moment, it'll ask

me uh to let it uh open an issue for me.

Um,

I spend most of my life obviously

waiting for agent modes to run. Um, but

um,

all right. Uh, and now it's going to

summarize it, but it's already created

the issue. It's already done that. So,

if I skip into my web browser, I should

be able to

have a quick look and blah blah blah.

So, um, yeah, I'd asked it to summarize

the pull request and to generate a bunch

of mermaid charts. And, um, I don't

know. I like every time I see this, it

blows my mind. It's kind of,

uh,

from very basic prompts, it's able to

generate some really advanced things. So

if you're interested in um like

improving your issue bodies and things

like that and doing more summary work,

especially for managers who think why

would I touch this technology? I think

like you know this is just a oneshot

short prompt and you can see the kind of

really interesting things it's come out

with. Um and if you were to iterate on

it and kind of refine things, there's

all sorts of things you could create.

So, um, yeah. Anyway, that amazes me

every single time.

>> I think it shows off GitHub's ability to

render mermaid charts as well. Like, I

don't think I knew that it did that

until I saw the MCP server go ahead and

do that. I was like, "Oh, wow. There's

this rich capability in our platform

that the MCP server helps you find just

because it would be way too much work as

an individual to do it. But when you let

the LLM do that hard work for you, it

kind of scales your ability."

>> Yeah. And so, um, I'll make it slightly

bigger because I think it's a little

small. Um,

so now I'm also just going to ask it to

pull down a file for me. And so another

thing is it has access to resources. So

any file that you have access to on

GitHub, like in any revision of it or

branch or whatever, you can send off the

agent to just go and get a copy of it.

And so it already pulled down the index

html it asked for. And I also have the

preview plugin installed. So I can

actually just look at the this was a

silly vibe coding project I did for uh

an like a fun example website. Um

and um

uh the coding agent prompt I did earlier

to assign coding agent actually was uh

sent it off to work on different

revisions of this uh website. So, um, if

I go back into my browser and I have a

look at our agents HQ, I think I might

need to Oh, no, I don't even need to

refresh. So, we can see uh the latest

agent session that I triggered is

showing up and it actually also uses

GitHub's MCP server itself. So if you

reference issues in the repo it's

working on uh in your issue, it can then

go off and get them for example on uh

other things that it can do too. So uh

that's not going to finish I think

probably within 15 minutes. So what I

will do is just show you that um uh

previous time I told it to redesign that

web page.

Uh, let me see if I can find one that's

got a nice

a nice output. Yeah.

How's this? So, we can see the pull

request that gets generated. And, uh, I

think it should hopefully finish with a

before and after screenshot of of the

work it did. So, you can just kind of

when you think of ideas, just say them,

tell it to go off and do it, and when

you come back, you've got a pull

request. hopefully. And if you don't

like it, you can be like, um, you know,

uh, I wanted it to be even more extreme.

And then, uh, you can steer the sessions

as well. So like uh really I guess what

I'm trying to show is like once you get

addicted to using like having the MCP

and the various agentic tools that you

can connect with it, you just walk

around like I use GitHub's mobile app to

edit websites like personal websites

when I'm walking in the park and things

and uh you know by the time you're home

it's like there's a real version of it

you can hold in your hands and you could

pull it down maybe do some more coding

on it like tweak it

>> and this is actually demonstrating two

MCP servers. So the copilot coding agent

by default has the GitHub MCP server

installed but also has the Playright MCP

server installed and so this is what

drives web browsers. It's sort of a

human computer interaction agent and

that's where it's taking the

screenshots. So, this is super useful,

especially if you're not on your

machine. Like Sam said with his phone,

you tell it to do something. If you're

especially if you're building a website,

although even with CLI, it'll kind mock

it up as a website and it'll take the

screenshot and so you can then see the

result of the work without having to

locally install whatever it did, build

it and deploy it and test it like that.

It's just a really quick round trip that

you can do with the coding agent and

then give it immediate feedback.

>> And very like lastly for the live demo,

um I've just asked it to pull down a

physical image. So hopefully, oh, it's

doing the summarizing. So I'm just going

to start a new one. I think it'll be

faster. Um,

all right. So it's pulled down an image

file and then so you can use multimedia

and stuff as well. So um, it's actually

able to read the content of the image in

the agent and provide access to it

locally. So, whatever it was I wanted to

do, whether it's a design mockup or an

image I want to put in the repo, like I

can just pull stuff from anywhere on

GitHub. Um, so, uh, Toby, I think I'll

pass it back to you for closing

comments. So, I we we browse through a

lot of fun stuff you can do with the

GitHub MCP server. Um, but you can also

use it to scale. So, for instance, the

GitHub MCP server, like we said, is a

super popular open source project. It's

got over a thousand forks, which means

we get a lot of issues and pull requests

given to us like on a daily basis. So,

as a product manager, it's very helpful

for me to come in and say like summarize

the last 30 days or the last week of

issues and classify them by priority or

cluster them by topic. And so, it allows

me to sort of sort and scale this really

high volume of information that would be

quite difficult to do otherwise. So, I

think we've got a bunch of different

tool sets. So, to you can break up the

tools by use case. We've got repos,

search or whatever. um you have def

dedicated endpoints and ways to

configure the MTP server to just have

the tools that you want. I think all of

this is documented in the repos. So I'd

encourage you to kind of dig through our

our open source repo. Start star the

repo while you're there. Um and if you

look at the docs, you have a lot of

options there, including like putting it

into readonly mode. Um defining just the

tool sets you want. Uh we've got secret

scanning in there so it just the agent

doesn't push like your tokens up into a

public repo. Um we in we integrate with

the enterprise products. So if you're uh

GHC, you can use the the MCP server with

that. And like I said, it's in C-pilot

coding agent as one of the default MCP

servers.

Um and finally, you should go to our

registry. So this is something that we

just launched, and it's right now just a

list of highquality partner servers,

including the GitHub MCP server, but

we're going to be hooking into the OSS

MCP registry and allowing

self-publication. So this is going to

grow very quickly to thousands of

servers as soon as we do that. So, so

watch this space, but check out

github.com/mcp and find a bunch of other

cool MCP servers.

>> Yeah. And the last thing I wanted to

leave you with is just please,

if you can do one thing after this, if

you haven't, like load up agent mode, go

to github.com/mcp,

just install the MCP server and just

play with it. Like ask it to summarize

issues, try and vibe code, like just see

for yourself because it's awesome.

Thank you very much. Thank you.