H, what do I want to order today?

A barista can only make the perfect

coffee, but only if someone orders. And

that's the client's role. They ask and

the server responds. Hi, I'm Ian and I'm

a cloud advocate here at Microsoft. And

I really love this part because once you

grasp the client side, you see how

developers, not just systems, drive the

interaction. Clients are where user

needs get translated into actual

requests. And that's what makes client

server architectures so powerful.

Joining me today are Sandra and Bruno,

who are a powerful team. They're going

to be showing us how MCP clients work,

how they make the requests that bring

everything to life. Guys, take it away.

>> Thanks, Aan. Welcome. So, Bruno, last

episode we just created an MCP server.

Now, I would love to see how I can use

this MCP server as a developer using for

instance VS Code with its GitHub copilot

integration. And then afterwards, why

don't we also create an client? As a

Java developer, I would love to see this

using lang chain forj.

>> Yeah, absolutely. So, we we did

implement a server that lists uh species

of monkeys and we can we can access this

tool in different ways. We can use uh

we can use the

inspector for MCP which is this project

here model context protocol/insspector

in npm. This gives us a very easy way to

test. This is an MCP client at the end

of the day and it's good for testing an

MCP server. So I put the URL here of my

MCP server. I hit connect. I can list

the MCP tools available on this server

and I can trigger them. So here I'm

going to trigger the list monkey species

and I can run this tool. I got a list of

11 total species. I can make other calls

like get uh random species. It just

returns one and all the data. But this

is just an inspector tool. I I what I

really want is to get this MCP server

available in a in an environment that is

actually useful as part of my flow when

interacting with AI. So what we're going

to do is configure this MCP server u on

Visual Studio Code and then we're going

to implement an actual Java application

that uses this MCP client as part of an

agentic flow. If you are curious about

how to do these things, it's all part of

the repo let's learn MCP Java on on

GitHub um on the Microsoft organization.

So let's look into Visual Studio Code

first. When I when I ask Visual Studio

Code, let's use the ask mode. And we're

gonna

we're gonna use the ask mode for give me

uh uh three species

of monkeys.

And right now it doesn't have the that

MCP server configured as a as an MCP

server on on this environment. So it

came up with these options here uh resus

capu chain and how

these are probably coming from the

training model of uh son 4 which is the

model that I used for this interaction.

So, let's add the actual MCP server that

you created for species. And let's go to

here. Add a server. And we're going to

do localhost

8080 MCP SSE.

>> Yes.

>> Yes. Is that correct?

>> Yeah, that was correct.

>> All right. monkey species

MCP server

and um let's available on this workspace

only

>> and let's trust this MCP server.

All right. So now we have in this

workspace in this project here which is

the which is the MCP client project. I'm

also configuring the the server as an

MCP server for these Visual Studio Code

environment and it already found four

tools as we can see here. So we can show

the output. Let's see if the output

shows something. Uh this is the login of

the Visual Studio Code client connecting

to that server. Okay. So now let's go

here and

let's ask

so nay

MCP resource. Oh, not MCP resources.

Let's let's just ask the question again.

Give me

uh three species of monkey. Let's see if

it let's see if it will connect to the

MCP server. It did not. It did not

because it's not in aentic mode. Let's

put in aentic mode.

And let's make sure it's using

the MCP server for monkeys. And let's

click okay. Now I I did select I mean

all of the MCP servers in my environment

were selected. I I deselect all of them

and I only selected this one because I

don't want the LLM go trigger all the

other MCP servers.

Cool. So now I'll get you three monkey

species using the available tools to

provide you with accurate information.

Let's say it's a it's an MCP server with

accurate data, not just random training

models. Um, and then let's allow the

execution. So it did run uh it did run

list monkey species and it found one

species here, Spidey monkey. And let's

get the details for these species. And

let's get the details for this one and

for this one. So it got details for

three species. There you go. So, we have

the spider monkey, we have the Japanese

macac, and we have pro probosi's monkey.

Great. These are actually coming from

the MCP server that we implemented

before uh as we can see uh in the source

code, which oh, we'll skip that part.

You can go back to the video and watch

again. Now, what if I want to implement

an application that actually uh uh

integrates with the MCP server as well.

So we have this code here that we

already wrote and it's a it's a an

application using lang J forj

and it has a chat model uh it has a

system message you have this chat

interaction with with the uh um with the

application we have an open AI key but

we're going to actually use the local

large language model for this example we

have up and running to have an e-memory

chat memory store And we're going to

have tools. So for the tools, we're

going to use the MCP server that we

configured that we implemented. It's up

and running. And here's the MCP.json

file configured. We're going to use this

MCPJSON file for this Java application.

So when we run this application,

what we are doing here is combining

implementation of a chat service with a

provider and with a model. the chat

service.

Here we add in this AI services builder.

We have a bot which is a chatbot. We're

going to use a chat model which is going

to be a llama and we're going to use a

tool provider. This tool provider is the

MCP tool provider that has the MCP

server we configured. So let's just run

this code and see how it works. So let's

create a new terminal

and call java minus jar. And you see I

passed the argument here chat. So now

I'm in chat mode in this Java terminal

application. And I can say something how

like what monkey species do you know

or give me three species of monkey the

same prompt that we gave visual studio

code. Let's go with that. All right. So

it returned spider monkey, howler monkey

and Japanese macak. So three species

different than two uh in a different

order than the one give me fake tissues

species.

And these ones are fake coming from the

server. Volcanic amber monkey. Uh give

me all species

you have.

Let's see if it will list all of them.

There you go. 11 v instases as we saw in

the beginning. All of them coming from

the MCP serve. So it's not using its

large language model training data set

behind the scenes. It's just using the

information coming from the MCP server.

So this client we have Visual Studio

Code as a client and we have a Java

application as a client as an MCP client

for that MCP server. And you can

configure other tools like cloud desktop

or GitHub copilot CLI that just got

released um and um um cloud codecs all

these agent AI CLIs can now can also

connect to MCP servers as long as you

have this configuration. So go have fun.

Sandra is that is I I know monkey

species is not the best example but I

mean it

>> that's the best example with you can

totally run it locally but when you also

want pictures because I would love to

see pictures here we can just switch to

Azure OpenAI and with Lchain forj it's a

quick win. It's going to be the same

code just you as Bono pointed out you

just give it the secrets and the key and

then it will work.

>> Awesome. So, thank you Sandra. Thank you

for folks watching and uh have a great

day.

>> Thank you so much Bruno and Sandra for

this amazing session.

The only thing better than one cloud

advocate is two and we had both of you

today to lead us on this amazing

journey. For those of you who followed

along or would like to learn more, you

can find resources at aka.ms/java

andai for beginners. Link is also in the

description of this video. We hope you

stick around and we'll see you in the

next episode.