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🔴 A(P)I: Unified connectivity for AI applications using MCP

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Summary

Core Theme

This session introduces the Model Context Protocol (MCP), an open protocol designed to standardize how AI applications connect to external data sources and tools, complementing Retrieval Augmented Generation (RAG) by addressing interface fragmentation and enabling more sophisticated agentic AI systems.

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Hola SAP developers, welcome to this

session part of the the 12S 2025

integration trend. Before we start,

please feel free to drop us your

questions on the chat window as we'll be

monitoring it and we will answer some of

your questions there, some of your

questions live. If not, then we will

have a a session section at the end for

Q&A. Uh today we have Vadim who you

might know already uh from previous Leto

Fest. He's been a speaker at uh Lto Fest

before for the two times that uh there's

been an integration track before and

he's here with us today uh delivering

the last session part of the integration

track this year. Uh we're we're

definitely leaving the best for last. Uh

and well that's it. Without any further

delay or vadim over to you.

>> Thank you Antonio and hello to

everybody. Um

uh my name is Vim Kimov and uh I was a

speaker at pasttoberfest uh sessions and

also I was uh I was attendant listener

of all the sessions uh all years for the

fest. So literally I was with the fest

from from its introduction. Uh thank you

for having me here. Um I uh specialize

in SAP technologies in SAP integration

technologies in particular. So currently

I work on PTP uh platform for customer.

Uh but my heart is uh with integration

technologies and uh this is where I

would like to introduce the session uh

for your attention and talk about uh AI

integrations. So we are going to talk

about how AI applications can be

connected in a unified way uh with the

different uh external data sources and

tools. So this is all about uh MCP in

the session

and before we dive into the topic I

would like to introduce the agenda. So

we will start from the problem

definition. So why at all we need

something new uh in here. Then we will

talk about what MCP

um stands for, how it works, um some

some basic and common things that will

be required for us to progress and we

will go into some demo. We will see how

it works. And last but not least, we

will look into challenges

uh so that we have a more complete

picture about what MCP can introduce and

where we can benefit from it and where

And we start from introduction, we start

from the problem. So as it's very common

for le language models, um they are

nowadays very powerful but the knowledge

is still limited. So even though the

training data sets get extended and

advanced still there is certain cut off

for the knowledge that LM knows and

that's the date beyond which LLM itself

will not know what what happened. uh there.

there.

So LLMs are useful but there is this

restriction of the knowledge cutff and

to address that as a result as a result

we have some outdated answers that uh AI

applications can generate and also uh

those applications are commonly not able

uh to extract information uh which is

very domain specific or which resides in

corporate internal systems or relates to

some private uh data because commonly

that's not part of uh LN um models um

data sets.

So we need to solve it somehow and some

time ago the retrieval augmented

generation or rock for short was

introduced which allows retrieval and

pre-processing of data from the variety

of data sources and then this uh data

can be augmented into the LLM prompt and

then even though LLM itself wasn't

trained on that data it becomes aware of

that data which is of great help and now

we have quite a lot of rug based

solutions out uh

but then we come to the problem of

interface fragmentation. So now we have

the mechanism how we can reach uh uh

current data uh realtime data and

internal data private data but we have

to reach it in variety of formats and uh

communication protocols. So uh this adds

the complexity when we want to scale it

up and uh when we have the heterogeneous

landscape of the variety of data sources

and the interface fragmentation this is

where the integration u colleagues may

get excited because now we need to solve

the problem of how we can unify that.

So firstly we need to figure out how we

can stand standardize the interface that

AI applications uh can use to connect to

external data sources and also as a next

step we need to have something uh that

can be used as the foundation for

agentic systems because agentic AI

systems although they don't mandate that

but they really can advance and progress

and scale when we have this kind of

mechanisms. Um this is not to confront

retrieval augmented generation. It's

more to complement it. So it's not the

competitor to it's the

technology that is yet to help. And uh

moreover uh one of them can exist

without another. So we can easily have

something uh to connect to real time uh

to external data sources and that will

not be rug based or also rug based

systems uh not necessarily requiring

some unified mechanisms to connect to

external data sources. So now we have

this question and as you might have

guessed um we now have the answer or we

have the mechanism how we can address uh

that question and that is called model

context protocol or MCT for short.

We will look now uh at MCP at a glance.

So MCP is the open protocol. So it was

introduced by entropic in November last

year. So very soon it will be one year

old. It's quite recent technology

and the intention of it is to

standardize the the way how LLM

applications can connect to external

data sources and tools and it was

inspired by language server protocol by

the protocol uh which is used by modern

ids when they want when they need to

have language specific uh features

access to language specific features

it's client server protocol it's

agnostic from languages and from frameworks

frameworks

and it's entirely based on JSON RPC. So

the message format uh is not something

new. It's based on quite battle tested

format that that we may know from along

case and we have out of the box two

transport um communication mechanisms.

So the first one is for local it's based

on standard input output and the other

one is for remote invocations. That one

is based on streamable HTTP.

In the past we had the option to use

serverside events and we still can use

it technically but uh that was uh

superseded by streamable HTTP and now it

was duplicated from the standard plus if

we are not satisfied with these two

options we can develop custom u

transport mechanisms

maybe it's not that common but still

it's technically possible

speaking about adoption as I said it was

introduced quite recently in November

last year but since Then we saw quite

intense adoption of of this protocol by

the ecosystem by PKA companies, big tech

companies. Uh we could evidence that a

lot of SDKs were released for different

languages and I just mentioned few of

them here. Uh those which are official,

some of them were developed by entropic,

some of them were developed by other big

companies which you can see here. And

the ecosystem keeps growing.

when I tell uh when I talk about ecosystem

ecosystem

um there is no one single u number for

MCP clients and MCP servers that are out

there available but there are some

researchers which suggest that in

February this year so literally a couple

of months after the release of MCP

specification there were more than

thousand u MCP servers released and in

August this year there were already more

than 10,000 of MCP service released and

uh the number keeps growing. So a lot of

big companies, smaller companies,

clients uh they create MCP servers on

top of uh assets which they have already.

So we will have a quick look into the

architecture. So when we talk about MCP,

we commonly will have a local host where

we have some AI application. this AI

application it can be AI assistant think

of like cloud desktop or uh development

development copilots or digital assistants

assistants

uh which talks to LLM.

Now we have some data sources and those

can be either local running on the same

host or maybe it's a file on the file

system or um some other process that

runs on the local host or those can be

remote services. So maybe this is an API

exposed by the external service or maybe

it's database which resides elsewhere

or file storage that resides outside um

of the local host. Now we need to find

the mechanism how we can plug them in

the AI application and for that as a

part of MCP specification. Uh it

suggests that we can uh develop or we

can use if it has been already developed

and released uh the MCP server which

acts as a middleware and acts as a proxy

in front of the data source being a

local data source or remote data source.

This MCP server the way how it

communicates with the data source it's

uh specific to the data source. So it

can be a file system call operating

system call it can be HTTP call uh to

some API it can be database call or any

way that the MCP server can use to

programmatically access data and

resources which the data source exposes.

So this is hidden and obstructed from

the AI application. It's the

implementation details of the MCP serve

can be some open industry standard

technologies. It can be something very proprietary.

proprietary.

What MCP serve exposes? This is what uh

is agnostic technology wise agnostic

from the data source. And this is where

the unification comes in.

And in the EI application we have MCP

client. MCP client establishes the

connection with the MCP server using um

the model contact protocol the unified

protocol in this way AI application

doesn't need to know how to reach data

source so it only needs to know how to

reach the MCP server in the unified way

and then all the rest is implemented by

now we come to the life cycle so as I

mentioned it's a client server uh

technology so we have MCT client which

is a part uh which is a part of the

application and we have the MCT server

that can be run locally or remotely and

exported as a service. Uh the initiation

of the connection always happens by the client

client

um and after the connection handshake um

has been uh completed and was successful

uh the MCP server and MCP client

negotiate of which capabilities are

available. So what capabilities they can

um use we will come to this in a moment

and after that we come to the operation.

So operations part is the major part of

the that is used uh by the AI

application. This is where MCP client

can access uh tools resources. It can

access prompts on the MCP server and

also uh it can get notified about uh

changes which happen. For example, MCP

serve can notify MCP client about the

data object that was retrieved by MCP

client but it which was updated after that

that

and after uh the work is done uh if we

would like to terminate or to shut down

the connection the shutdown can happen

from can get initiated by either sites.

So it can be the server who shuts down

the connection it can also be the client

who shuts down the connection. For

example, we uh quit clog desktop or or

another client. Uh and this is transport

specific. So it can be termination of

the process if it's local or it can be

uh HTTP uh transport shutdown if it's uh remote.

We just briefly talked about a few

primitives and these are components

which uh primitives that lie at the head

of MCP server client server

communication. uh those are resources,

tools and prompts. Very commonly we will

hear about tools because tools this is

the most the largely used primitive

amongst uh these three uh uh the tool is

the function. We can think of it as the

function that is exposed by the MCP

server and that can be called by the MCP

client. The function can be to read

something to fetch some data or the

function can be to create or update or

maybe the function exposes some logic

which is exposes the access to some

logic execution maybe some calculation

or so. Uh so for the function it's model

controlled. So uh LLM model that is used

by the AI application makes the decision

based on tools which were advertised by

the MCP server and which has been

discovered by uh the LM through the MCP client.

client.

Uh LM makes decision of which tool to

call and with which arguments parameters

depending on user or workflow input for

example based on the prompt that the

The other primitive is resource.

Resource we can think of it as text or

binary data. It's not function. It's not

logic that is implemented there. It's

data which is exposed and usually it's

read only access. So we cannot create or

update it. For example, a good example

for that can be uh we have some entity

for example employee record or material

record, product record or sales order

record and we would like to retrieve uh

it just to fetch data about it. So

that's example of uh resource to some

extent we can map it to a rest architecture

architecture

where we have resources and those

resources are always uniquely

addressable by the URI. uh this makes

them quite uh nicely resonating to rest

uh architecture and those are

application controlled. So the AI

application determines uh which resource

it wants to retrieve data about not LLM

anymore. And the last one is prompts.

prompts or prompt templates. They are

predefined messages or struct structured

messages which allow the client

uh to better understand how it can

interact with MCP server. So that's

something that is user controlled and

that's something that we can commonly

see in uh for example coding assistance

where we have uh uh slash comments.

uh so that's something that helps to

drive communication and uh to make the

the use of most most commonly tools more

uh precise. So for example it can help

us drive which arguments must be filled

uh for the specific invocation of the

tool based on those which are exposed by

or acceptable by the tool.

So these are primitives but this is not

the only functionality that MCP exposes.

There are some others. I will not go

into details for this but just for your

awareness uh we have some features which

can be implemented by the MCP client and

we have some others which can be

implemented by the MCP server and there

are some others which are common. Those

features are intended to help uh MCP

client and MCP server communicate more

accurately. So for example uh

subscription to notifications

or uh the client may decide to expose

parts of its file system so that MCP

server can have access uh can have

access to it or the MCP server may

request sampling so that it better knows

how how to uh to run the tool.

pagenation from performance perspective.

Uh completion so that we can help uh

complete argument values. Uh logging

which is quite common thing so that we

can log what happens on the MCP server

and return it back to the client. And

there are some common features like for

example we can have progress tracking if

there is some long-lasting operation. We

can cancel the request which can be

triggered by the client or by the server

and ping test so that we can check the

general health of the counterpart. So

MCP client can check if MCP server is

still alive and vice versa. So these

additional features in my personal

experience they are implemented to a

lesser extent in MCP servers that I

worked with but it's nevertheless worth

knowing about them so that uh we can see

if we need to tweak something or if we

need to add extra functionality then we

we know that what kind of other features

are available. Um these features and

primitives they are well described in

the MCP specification for which we had a

link a couple of slides ago. Um and uh I

really encourage you if you would like

to read about them uh in more details to

refer to the specification because specification

specification

um it offers not only more detailed view

on that but also they provides exact

examples in uh for JSON in JSON PC

format about how this communication may

look like between MCP server and MCP

client. And good news uh SDKs for MCP

they commonly would already uh implement

that and will offer uh APIs um as an

abstraction layer to make the developer

life easier if they would like to

implement it. Um

we are done with features discussions

and now I would like to show you in

action how it how it works. So what I

will do is

I will have the CPI integration with

tenant with CPI enabled. There we can

see that there are some packages some of

them are uh standard content from SAP

and some others were custom developed

and let's take the example where we

would like to have the digital assistant

I will use clot desktop in here and we

would like to to interact with it uh and

fetch data from that CPI tenant. So at

the moment uh CPI offers public APIs

which can be used and which are used by

quite a number of tools to extract data

and to upload data into the CPI tenant.

But if we ask if we are in the space of

working with the EI tool and we ask the

EI tool about something related to the

CPI tenant I will put it in here. it

will not know anything because the LLM

doesn't know what is inside the CPI

tenants that I have and I use trial

account for that. So what it does is it

provides some very generic statements

about how we can access it but no

further details.

Uh before we progress I just want to say

that in these demos I will use cloth

uh other digital systems as well. So

those which uh support uh MCP which

means that those which have MCP client

implemented in it and quite a number of

um digital assistance and coding

assistance they already do support MCP

quite broadly. So it's the disclaimer is

that features which I will describe here

they are not cloud specific quite a lot

of them uh we can uh uh see and we can

use in other tools. So the the reason I

use clot is mostly because I use it as

my primary tool in uh development

activities uh when I need to have some

AI uh capabilities but I could have used

something different in here. So we see

that the answer is quite generic. It

doesn't really expose anything from the

CPA tenant. Uh what we will do now is uh

we have the digital assistant uh cloud

desktop. It has MCP uh client

functionality in there but it doesn't

yet know how to talk to the MCP server

and firstly we also need to uh to get

MCP server for that. So MCP server I

implemented for uh for this um in go so

I have the project which has been

published to GitHub. So it's open source

and it's publicly accessible. If you

want you can have a look into it and you

can use it and I will be really happy if

you provide your comments about it. So

uh we have the project uh which

implements the MCP server for some

artifact types u uh in CPI and entirely

uses public APIs which also means that

we cannot fetch some data for example

about the internals of the artifact like

for example message processing steps um

but we can uh fetch information about

integration packages integration flows

script collections value mappings um and

so on and so on. So this MCP server it's

implemented it's compiled in the binary

but I also wrap it in the docker image

and I will run it as a docker container

and we will come a reason why I do so

when we talk about challenges uh

technically we can run it as a binary as

well and uh there are a lot of MCP

servers written out there in different

languages in Typescript JavaScript in

Python in Go you can develop it in Rust

if you want or in few other languages.

So what we need to do is in the first

part of the demo I will start the MTP

server locally. So I have cloud desktop

running on my machine. I will also start

uh the server on on this machine. For

this one I firstly need to instruct

cloud desktop how to start the MCP

server or which parameters have to be

passed and how to interact with that. So

for that one I will go to the cloud config.

config.

Let me just pull the file in here.

It's something that you can access

from cloud settings as well from here

in developer. If you use some different

uh digital assistant

just please check the documentation uh

either which file it has to be added or

in which menu uh path you can access it.

And there are also some registries from

where you can pull uh those MCP servers

and they they can offer uh quick install

uh functionality depending on which

agent you use or which uh desktop agent

use or which uh ID use. For example, if

you use VS code or uh something different.

different.

So what I will do now is I go to the

config file. Now MCP service is empty.

I added configuration for the MCP server

for CPI. So it runs in the docker. It

could have been any other comment that

the local machine can start. So it can

be invocation of the binary file or

NodeJS runtime. We can pass some

arguments in here. I say uh docker for

which im container for which image has

to be started and configuration

parameters for this uh MCP server uh

which are the way how uh which instruct

to which CPI tenant uh the MCP server

shall be connected uh they are provided

um in the environment file. So this no

secret I will open it in here.

So this except the last one uh this

first four parameters they are mandatory

because they define uh the endpoints and

client and decline secret uh to interact

with uh CPI. The last one I will need to

change it further in the demo. So I just

provide it in here. It instructs uh

which protocol we're going to use. So do

we use transport for local or do we use

HTTP? Uh there are some other parameters

which have been documented in the GitHub

repository of this project. So that you

can control LO level, you can control uh

LO file location, you can define what is

the maximum concurrency uh that MCP

server will use for calls uh that are

sent to the CPI endpoints, what is

timeout. So those are predominantly uh

to change login behavior and also to

have some drop link between MCP server

and CPI so that MCP server doesn't um u

generate excess number of uh API calls.

Here we go. We add it in uh cloud uh in

configuration in here. So I will save it

and what I will do is I will exit desktop

desktop

and I will test again. I will just start

that so that it picks up

Here we go. So we can see that uh clot

recognized that we now have a new MCP server

server

in uh dock desktop. I can see that we

have now a new container. So that one

was started by cloud desktop at startup.

So now we have the MCP server running.

We have cloud desktop running. And now

we c we can select which tools given

that the MCP server exposes multiple

tools. We can select which tools we

would like to enable. So we can be

selective and we can dis disable those

which we don't need. And I will come to

the point why it may be useful. In

extreme case we can enable all of them

if we want. For this demo, I will only

need to search for packages and forlows.

Eyeflows which are available at design

time and those which are available at

runtime, those which have been deployed.

So I will disable tools which are

related to search for script

collections, for value mappings, for

message mappings etc. And now what we

will do is we will just repeat the

prompt that we sent previously. So that

we literally just uh send exactly the

same prompt once again. And now you see

that the answer is different. So now it

invokes um cloud desktop invokes the

tool. So it from the prompt it

recognized that I want to get

information about if flows which were

developed by uh by myself and I want to

get some information about it. So it

will fetch information. It will look for

those which have been developed by me

and it will present I asked just some

attributes so that we are not flooded

with uh information. Um and here we go.

We have a list of flows per package in

couple of test packages which I created

in advance

which is good fine we can get it from

web UI as well. So now we come to the

second point. So we found if flows now

we want based on that understand if we

have any drift between design time and

runtime. So now what we do is we ask can

we see if there are can we identify if

there are any flows which have been

deployed to runtime but for which

versions differ from the design time

and there are some good cases so for

example it's a development or it's this

one is trial environment so we can

actively work on some flow and the

version at design time may be higher

than at runtime which is fine or

acceptable for development or trial

accounts it can be another version

uh that runtime artifact has higher

version than the design time artifact

and this is more boring and let's see I

created examples for both of them let's

see if cloud desktop will be able to

pick this up so we can see that it

recognized that the prompt asks for

runtime artifacts so now at first it

looked for integration packages and

integration flows design time now it

tries to fetch information about runtime

And here we go. So there were some ifs

which exist in design time. I haven't

deployed them to runtime. There are some

ifs which were deployed and the versions

are the same and a couple of flows which

I deliberately drifted between design

time and runtime. So one option is where

one flow is where design time version is

higher than runtime which is as I said

acceptable for development environments.

The other one is boring where design

time is lower the runtime. This means

that we deployed something to the

runtime which is not yet known at design

time or we uh confused versions uh for

some reason we overritten the version at

design time with a lower version. So

this is how we quickly were able to spot

that there is something uh which uh

which can be identified as a drift and

we can think of some other prompts some

other use cases uh where we can use it.

So we can analyze the tenant, we can uh

we can compare uh uh and so on and so on

and we can build up we can enhance those

tools as far as we have the way to

programmatically extract this

information from the CPI tenant we can

enhance it. Technically we can create

tool which also will update something in

the CPI tenant.

That's the demo for the cloud desktop

and this is where we used the tool. Now

what we will use is uh I will I will

open another tool uh clo code

and using the same MCP server. So I will

that one is my great helper for

development. So I really like cloud code

uh for to help me with development

activities and what it has it also has

sub agents uh concept and to prepare for

this demo I created two agents our small

guests which are also on this session uh

which will help us to demonstrate how we

can embed the use of MCP server into the

agentic flow. So what we have is let me

just quickly show you. So we have uh two

sub agents which I created. The first

one is CPI collector. So this one uses

the MCP server which we have just seen

uh in cloud desktop connected to the

cloud desktop. So it knows how to

extract data from the CPI tenant via the

MCP server but it's not opinionated

about is this data compliant. So uh is

configuration compliant or not? So it

knows how to extract data now how not

how to do something with this data how

to assess the quality of this data and

we have CPI reviewer sub agent it

doesn't know how to extract data how to

fetch data from CPI but what it knows it

knows some best practices um standards

guidelines so it can review that content

for example integration packages and it

can provide a view about whether it's

compliant with the guidelines or whether

it's not in the real life These sub

agents could have been combined into

one. So maybe we could have had

something like CPI inspector which does

both. But for in sake of this demo I wanted

wanted

uh to split them into two so that we can

see how these agents how we can make

this agentic flow more uh interactive so

that we have couple of agents and we

talk to them. Another uh disclaimer here

is I don't use for for the sake of this

demo I don't use the orchetrator agent.

uh so that we don't have a single agent

that orchestrates invocations of other

sub agents here I will do it manually

just to show what is the actual

communication flow that we have so

technically we could have built an

orchestrator agent and then whatever

happens under the hood will be

transparent for us as a user so what we

will do is firstly uh

firstly let me ask the collector uh sub

agent to extract information about

integration packages is so what it does

under the hood is it invokes the MCP

server and here I also use local comm

connection. So what we will see is uh we

have another uh docker container which

was created one was created because of

cloud desktop using MCP server and

another one was created by cloud code.

So now cloud code uh via the MCP server

or sub agent of cloud code uh accesses

CPI tenant fetches the data and in a few

seconds we shall see the output. Yeah,

here we go. So it found MCP server

provided via the tool uh information

about packages which are out there. So

we have 10 packages which are

prepackaged content and we have two

custom packages. And now what we want to

do is we want to check whether they are

compliant with the naming conventions as

an example. So what we will do is we

will take we will ask the reviewers at

agent can you have a look in the

integration packages and highlight those

which do not comply with our standards.

uh when I say our standards what I did

is I uh used SAP um naming con uh those

uh naming conventions for CPI uh

integration packages which you can find

in SAP help and I created uh the agent

uh with awareness of those standards. So

both agents uh they uh the way how they

were designed is each of them is uh is

created as a markdown file and in the

markdown file we define what the agent

specializ sub aent specializes in what

kind of uh checks it has to do what kind

of actions it has to do and so on and so

on and for example for CPI collector uh

we also instructed of which tools it can

invoke in the MCP server so that for

example reviewer cannot access MCP

server It's very restricted. It doesn't

have access to uh external tools. U the

collector has reviewer only can assess

the information which is fed to it. So

now the reviewer does not make a call

because a reviewer is not allowed to

make um MCP tool calls. It acts on the

data which uh collector sub agent provided.

provided.

And here we go. This is the outcome of

the reviewer. So we see that um it

completed it. It highlighted that there

are no issues with the names for CPA

standard content for standard packages

but what it says is test package A and

test package B are not good idea for for

integration packages. Uh so really uh

this is just for demo piece but it's not

something that we would like to see in

production environment and it provided

some other recommendations about what we

shall consider um to refactor those

names and what is considered to be a

good uh practice. Again those best

practices they are referenced in the sub

agent definition and they are based uh

on SAP um recommendations.

So this is another example where we have

MCP server plugged in into the agentic

flow with segregational duties. So

different sub aents they are allowed

only to access some um some functionalities.

functionalities.

>> Vim I I want to ask a quick question

here just because I see missing company

prefix there and and you have that

identifier VKLM. Did you define that within

within

how how did it generate that VKLM? Did

you define it somehow? No. Uh this one

it was uh generated by uh the sub agent.

Uh so in the sub agent definition what

it says is that uh the naming convention

for the integration package optionally

can be prefixed with a company prefix uh

which can be of three four letters uh

length. But uh that that's a very good

question. So let me

u um B agents human

human

that's the definition of the agent and

it's very basic so it it's not

comprehensive in sense that I didn't

modularize it so I didn't externalize

naming conventions which which would

have been a good practice uh for real

life use but in here there is naming

conventions section this part uh which

is based on SAP help documentation And

this is where you can see uh what the

sub agent considers as a good naming

convention. And then it's uh it says

that optionally we can have uh uh prefix

and some others which are mandatory

elements of the um uh integration

package name. So are the examples is

something that the sub agent created and

produced in the output on top of of

instructions and conventions that it was

told to follow or it was told to review

and uh to assess

>> is I just find it so interesting that it

actually end up suggesting V KLM you

know uh like it it knows you

but it's it's so interesting I don't

know and and in the response sounds like

it was consistent everywhere where it

was uh defining that company identifier.

So yeah that that's very interesting. Sorry

Sorry

>> it's uh you know for some some of them

we can put examples like it's in here

but for some others it was either

creative or it hallucinated in the way

that uh resonated with the context very

well. So yeah,

>> I'm not sure which which of these I'm

more leaning towards, but uh you see

that part of the output was truly uh the

creativity of the sub agent and the LLM

uh that was used uh behind the scenes uh

for that. Um I will close this part. So

that's that's another example where you

can see and if I rerun it, it's probable

that it will create some other examples

or it will rephrase. So it will still

stick to the naming conventions that we

defined or constraints that we defined.

uh but given that it's nondeterministic

output then u each each next run may

produce slightly different uh output

although the overall um idea shall

remain the same and the more precise we

can define the sub aent the more

accurate it will produce the output and

this is where I will not uh drift from

our main topic but when we develop sub

aents we can really modularize them we

can have some uh corporate naming

corporate guidelines We can have

industry standard guidelines or

guidelines specific to the particular

area like for example for groovy

development or JavaScript development.

And then we can let the sub agent know

in which files to look for specific uh

or in which destinations to look for

specific parts of those uh constraints

or guidelines so that we can really uh

make it modular and reusable and we can

reuse some assets which we already have

in the organization. For example, if we

have guidelines and if we use them in

other tools, we can also reuse them in

the sub agent. So that sub sub aent will

So that was the example how we could

plug this in into the aentic flow and uh

this was still the local execution. What

we will do now is we will do the similar

thing but remotely. So another option is

we can run

uh the MCP server remotely. So what I

will do for this is firstly I will make

sure that we stopped everything and the

docker engine is clean all good and now

in the configuration

uh in here I will just change it from

local stdio to http. So now we can start

now it's not the uh cloud desktop or

cloud code that will start it for us

automatically but we have to start it

manually and for this one I will use um

since it's running in docker so I will

so now it's in http mode and we shall

see that now the new container was

started and now it's uh listening on

8080 port

which is okay. So we have the still it

runs on local machine. Hypothetically I

could have started it somewhere else

remotely in some other service. Uh now

we want to access it. So I will not

access it directly from the local

machine. I will expose it to the public

internet so that we can see that uh this

service could have been run uh

elsewhere. And for this one what I will

do is

let me just open it

in here.

I will use nro for that. Uh so that I

have the tunnel just for demonstration

purposes. So that I have the tunnel that

will expose the public endpoint for the

locally run HTTP server which is our MCP

server. So I started the container with

MCP server. listens on on the port

locally but it's not yet exposed to the

internet. So what I do is

I run the tunnel. So now we shall see it

in the browser as well.

Let me refresh it.

Here we go. So the end point now this

endpoint it's publicly accessible. So

now we can call it from the internet

from the outside and we can also see it

from here. So what we will do is now we

will add it to CL desktop just rather

than add it adding it in here I will

remove the part that is about local

connection so that we go to square one

and let me start

started.

You can see that we don't have CPI tool anymore

CPMCP server and tools uh that it

exposes. They are not there anymore. So

now we will go to connectors at custom

connector. And now we will add

/ MCP. We will add the link uh where the

MCP server uh is accessible. And this

link as I said it's accessible over the

public internet. So it could have been

run elsewhere. We added

oh sorry my bad link in here and I will

put CPI as the name of the server in here.

Done. So now it's connected.

What we can do now is we go to new chat.

We see that CPI

now it's not local CPI but it's the MCP

server that runs even though it runs

remotely

like demo some problems let's see what happens

happens

uh let me activate for example get

let me see if I can succeed with

So now you see that it now accesses

again the MCP server.

Yeah. So it was successful. So now it

was the from cloud desktop perspective

it accessed the MCP server via the

public internet. So it doesn't know that

it runs locally and it was already up

and running by the time we started cloud

desktop. So cloud desktop connected to

the existing remote MCP server in here

and we see that it was successful in

fetching information about all the 12 uh

packages um in the tenant. That's

example how we can access it remotely.

That's it for this part. Let me stop it

So we saw how we could uh spin it up and

run it locally and we saw how we can

access remote MCT service which is

great. We have quite a lot of new tools

in our toolbox.

But uh now we come to the challenges and

considerations part. So MCP server is

great but it comes with its own um other

side of the coin and I will start from

those which are more governance and uh

monitoring aspects. So those which are

related to the life cycle management. I

will not go into each of them but we'll

just uh talk about them in big blobs. So

we have the discovery aspect. It works

well when we have just a few u MCP

servers. But if you think we can run

tens or hundreds of MCP servers and we

could could also have access to remote

ones, then the existence of MCP registry

becomes quite critical

uh somewhat similar to the API developer

portal. So something where we can

discover and we can have uh a registry

of those MCP servers which exist and uh

we can have some mechanisms to audit

that and we also have to have the

mechanism for active discovery for those

so that we can discover hidden gems MCP

servers that run in the infrastructure

but for some reason are not registered

in the MCP registry deployment aspect.

So we have to decide where we run, how

that infrastructure is secure, how it's

scaled, how we manage secrets and so on

and overall monitoring. So it's very

critical that especially for production

environments uh uh we need to track

which tools of which MCP service were

invoked by which agents and uh execute

uh and monitor execution of those tools.

We have to keep track of resource

consumption and anomaly detection. For

example, if there was any spike in the

call of some tool or unusual activity

related to some arguments passed to some

tool or maybe a consequence or sequence

of tools execution, some chaining of

tool executions that is not uh normal

for the use case.

The next one is performance and that's

not only about the technical performance

to a large extent that's also about the

performance of and accuracy of responses

that we can get. So the first big

challenge is that a model can be

confused if there are too many tools. So

it's uh tool overload situation

especially if tools have similar names.

So for example we have a tool called get

order in one MCP server and we have get

order in the other MCP server. just one

tool is about sales order and another

one is about uh purchase order. So if

the description and examples are not

accurate enough or absent uh that the

model may get confused about which tool

to use or which arguments to pass to

that tool. Uh that's a reason why in my

demo I was specific about that was one

of reasons uh why in the demo I selected

specific tools that I wanted to use and

not all tools uh from the MCP server.

The other one is context distraction.

That's about so-called uh destruction

syndrome. So if we bring a lot of data

in the context and effectively responses

from MCP tools, they are brought into

the context of of the model. Uh if the

context is cluttered, for example, we

have a lot of data which is irrelevant.

So we don't need it, but for some reason

it's contained in the response of the

tool, it may be confusing for the model.

And as far as we progress with our

conversation and we may invoke other

tools and they bring other information

into the context uh the context may

start drifting from the original uh uh

road and then we the response quality of

of the agent and of the model uh will degrade

degrade

and as a result it may be inconsistent

and we may see more and more hallucinations.

hallucinations.

Then the next one is probably technical.

It's about window overflow. So each

model even modern models who have large

context windows they still are limited

and depending on what kind of format we

use even what kind of language we use.

Uh we uh can have a penalty which is

called token text uh token text which

sometimes is related to some human

languages which are more talkative. So

they require more boards uh and which

require more tokens uh from the model uh

to interpret them. Uh sometimes it's

about the format. So for example, as you

know XML is more verbose format and it

will require more tokens commonly than

JSON. And this is where we can think

about which format we would like to use

when we bring it into the model. um and

truncation risk um uh so if the model

exhausts its uh context window because

we brought a lot of data into context

window for example we were interested

just in the package ID and version and

author but we inh we brought into the

context all attributes available for the

integration package and rather than

searching for specific couple of

packages we fetched all packages which

are available in CPI tenant and then

started to filter them uh in in in the

context so um the context uh can be

become exhausted and model may start

truncating uh the old content as a

result again we may have inaccurate responses

responses

and backend overload so if MCP server

doesn't implement any throttling we have

a risk that uh um the amount of

invocations of tools or the way how

tools are invoked uh may result quite

severe resource drain on the back end up

to the national service.

And the third section which is the most

concerning uh to to many people is

security. You see there are a lot and

lot of security threats and vectors of

attack in here that we must consider uh

before we um onboard MCP servers

especially if they are related to

production data to sensitive data and so

on. uh a huge section is about tools

integrity. This is where we have a

plethora of different ways how we can

compromise MCT tools. The most common is

tool poisoning. So we can infuse some um

bad instructions for example in the

description of the tool and so on and

then the LLM model will be confused with

that and it will try to execute actions

which it's not supposed to execute with

the normal conditions with the normal

operations of the tool. Then we have

examples of tool squing and tool

impersonation and rockpulls. These are

generally related uh to the situations

where we either introduce bad actor MCP

server bad actor MCP tools. So tools are

named similarly to u root tools but

those tools they implement some bad act

logic and those tools may be invoked by

uh the LLM. Uh if those MCP servers are

connected or rock pools when we trust

some MCP server and you could probably

see that in one of screens in cloud

desktop it asked me to explicitly allow

invocation of the pool. Quite commonly

users will click uh click allow um not

once but always allow so so that they

are not bothered about repeating the

step next time and then the tool may be

replaced uh or the MCP server may be

replaced by the bad actor with wrong uh

with a bad implementation

and then the user the system trusts that

tool and invokes the code and invokes

instructions uh which are now malicious

or cross tool contamination. That's

another vector of attack which is quite

popular where this where tool poisoning

in one tool can impact the other tool

not only in this MCP server but in other

MCP servers because when the agent

starts uh it discovers all tools

available. For example, we have five 10

MCP servers connected and we have the

11th one which is poisonous MCP server

which has uh poisonous tools with bad

actor descriptions and which can

contaminate activities that uh good MCP

servers good tools execute. So for

example uh in one of those tools the

description can say that for all tool

executions LLM needs to send data

somewhere or to execute something else.

Then we have context integrity that's

about prompt ejection and context

poisoning. So this is where not only the

tool description or tool data uh can be

brought into the context which is

malicious but also uh for example

responses of MCP server data sources

they can be poisonous. As an example the

MCP server may invoke API which brings

back some data and some of those data

records are poisoned. So they contain

hidden instructions

that LLM will interpret and will try to execute.

execute.

Um and then we have context clashes and

as a result hallucinations.

Another big thing is data leakage

because MCP servers they become quite

powerful tool but also quite a powerful

way to exfiltrate data to extract data

from data sources and potentially leak

it uh to the bad actor. We have a lot of

questions about identity and access

control. So this is where we talk about

how we can access the MCP server. So

whether the access to MCP server itself

is protected and how we manage uh

authentication and authorization between

MCP server and the back end. Do we use

principal propagation? Do we use some

impersonated uh uh tokens, API keys or

maybe client ID, client secrets? So how

safe they are and how overexposed they

are to the consumer

uh and how we manage those secrets.

Privilege escalation sandbox escape

that's one of reasons why I executed the

MCP server in the docker container not

just as a binary on my host because uh

that's another vector of attack where

the MCP server may be used to execute

something for example on the host which

is malicious. So generally it's called

sandbox escape where the application is

instructed or tries actively tries to

execute activities which is which it's

not intended uh to do network

communication insecure connections. So

my example is MCP server was started as

HTTP using HTTP on the local machine. So

it exposed HTTP endpoint which is not

secure protocol. We did wrapping into

HTTPS when we created the tunnel with

the help of MRO. So when somebody

accessed it uh for example cloud desktop

access it from the public internet it

accessed via HTTP but we can see MCP

servers that use HTTP um HTTP and may

expose HTTP to the internet combined

with the risk of being insecure from

authentication perspective. So

unauthenticated access and combined with

the access to some private data this may

be a really dangerous uh combat. And the

last one that I could think about is

resource abuse. And here I'm not talking

about the targeted denial of service. So

the bad actor, malicious actor may

intentionally try to use MCT server as

the proxy to uh to bring the back end to

the dural of service state for example

by invoking the tool too many times or

provoking the tool to make too many

calls to the back end. But also another

attack which is more financial uh angle

which is called denial of wallet. So now

a lot of backend resources live in the

cloud and deployed in the cloud. MCP

servers themselves can can run in the

cloud as well. So those are like

microservices. Um uh and

even though we may not struggle with

technical performance the cost of that

uh attack may be quite high. So we can

drain cost or we can increase spike

costs because of too intense use of MCP

server or two too intense use of the

back end when the MCP server calls it.

So the n of wallet is also another

growing vector of attack and MCP servers

unfortunately they are also vulnerable

may be vulnerable for that. So now when

we talked about security about the

challenges we also had some ways to

mitigate them. The first one is think of

MCP server as a lightweight layer

between your AI application and your

back end or data source. So it has to be

light. We do not we are not expected to

implement complex logic in there. The

second one is we have quite a number of

really powerful generators that can

produce MCP service tools for them on

top of existing APIs. We can take rest

API, scope API and generate MCQ server

on top of it. There are a lot of use

cases where it's valid and it's very

helpful. But also think of those

challenges we talked about like uh

context clustering and so on. Um think

what are use cases for that tool in MCP

server and what data is required from

the MCP tool. So is it really the

entirety of data that rest API response

or we need to make some truncation some

aggregation also

when we talk about rest APIs they are

mostly resourcecentric and nowadays

quite a lot of MCP servers they create

tools out of those resources but think

about resource primitive so that's more

relevant even though the challenge that

not all MCP clients support resources

and consequently it's commonly required

to create a pool not a resource primit

primitive for uh backed rest APIs and

now our focus is not only on the prompt

but also on context engineering. You

have seen from the challenges that a lot of challenges are related to how we can

of challenges are related to how we can compromise uh how we can poison context

compromise uh how we can poison context that becomes quite or how we can

that becomes quite or how we can overload context.

overload context. So that all brings us to the challenge

So that all brings us to the challenge that we need to think about how

that we need to think about how responses of MCP tools are created in

responses of MCP tools are created in terms of number of records filters uh

terms of number of records filters uh fields which are returned even formats

fields which are returned even formats which changed

which changed um and we have some optimization

um and we have some optimization techniques. So we can have map produce

techniques. So we can have map produce technique or chunking so that for

technique or chunking so that for example we can run some operations in

example we can run some operations in parallel and use multiple contexts

parallel and use multiple contexts rather than a single context and then uh

rather than a single context and then uh pre-calculate and aggregate those

pre-calculate and aggregate those results in the main context. The good

results in the main context. The good example is when we use different um sub

example is when we use different um sub aents

aents uh and next one we come to a lot of

uh and next one we come to a lot of considerations about security. So I

considerations about security. So I spent lots of time talking about

spent lots of time talking about challenges. We also have mitigation

challenges. We also have mitigation points. The first and very critical one

points. The first and very critical one is something that is called little uh

is something that is called little uh trifecta. So that's the term that was

trifecta. So that's the term that was introduced to collectal collectively

introduced to collectal collectively refer to the threat when the tool or the

refer to the threat when the tool or the agent MCP server in our case as well has

agent MCP server in our case as well has access to private data for example

access to private data for example access to CPA tenant. It's exposed to

access to CPA tenant. It's exposed to untrusted content. We don't have control

untrusted content. We don't have control over data in the data source for example

over data in the data source for example and ability to externally communicate

and ability to externally communicate for example not only we have the MCP

for example not only we have the MCP server to fetch data from the CPI tenant

server to fetch data from the CPI tenant but we also have the tool uh from

but we also have the tool uh from another MCP server or from the

another MCP server or from the capability of the AI agent to send

capability of the AI agent to send emails to invoke some operating system

emails to invoke some operating system comments or to persist data in files in

comments or to persist data in files in some external storage. So this uh uh

some external storage. So this uh uh trio becomes quite dangerous and our

trio becomes quite dangerous and our intention must be to see how we can

intention must be to see how we can break this trio. So how we can make sure

break this trio. So how we can make sure that one of these uh elements of the

that one of these uh elements of the triangle is not present and is uh or is

triangle is not present and is uh or is controlled

controlled multi- aent systems. Another way how we

multi- aent systems. Another way how we can mitigate the risk so that we don't

can mitigate the risk so that we don't bring everything to one context. So we

bring everything to one context. So we have proper segregation of duties

have proper segregation of duties between agents which agent is allowed to

between agents which agent is allowed to call which tools and uh uh operations

call which tools and uh uh operations and we have the whole uh uh element of

and we have the whole uh uh element of guardrails. So there are frameworks for

guardrails. So there are frameworks for that where we can sanitize inputs and

that where we can sanitize inputs and outputs from the model. So we can be

outputs from the model. So we can be more accurate about which data comes

more accurate about which data comes into the model as a result of calling

into the model as a result of calling MCP tool. We can have some filters and

MCP tool. We can have some filters and what is produced by the model before it

what is produced by the model before it reaches the agent

reaches the agent in here. It's also critical to think

in here. It's also critical to think about instruction hierarchy. So we can

about instruction hierarchy. So we can have some instructions in the system

have some instructions in the system prompt. Some instructions come from the

prompt. Some instructions come from the user in the user prompt. We can have

user in the user prompt. We can have instructions which are embedded in tool

instructions which are embedded in tool descriptions, tool implementations or

descriptions, tool implementations or poisonous data in the data source which

poisonous data in the data source which may also have hidden instructions. um if

may also have hidden instructions. um if they all were executed with the same

they all were executed with the same privilege it would be really a danger

privilege it would be really a danger and to address that danger um modern

and to address that danger um modern LLMs they have the concept of

LLMs they have the concept of instruction character so they have

instruction character so they have different trust levels to instructions

different trust levels to instructions coming from different layers for example

coming from different layers for example the most untrust one of the most

the most untrust one of the most untrusted layers is data that we extract

untrusted layers is data that we extract from the data source because we can have

from the data source because we can have lots of data there and potentially

lots of data there and potentially poisonous so instruction in there shall

poisonous so instruction in there shall not override for example system

not override for example system instruction of the LLM which has the

instruction of the LLM which has the highest privilege or the instruction of

highest privilege or the instruction of the tool which has lower privilege than

the tool which has lower privilege than the system instruction in the LLM but

the system instruction in the LLM but has high in privilege than uh uh the

has high in privilege than uh uh the instruction contained in the tool

instruction contained in the tool response and for critical ones always

response and for critical ones always put human in the middle so that human

put human in the middle so that human has to take active action to approve

has to take active action to approve those things. So security first approach

those things. So security first approach is very critical in here. As I

is very critical in here. As I mentioned, authentication and

mentioned, authentication and authorization mechanisms, how we access

authorization mechanisms, how we access MCP and how MCP server accesses uh the

MCP and how MCP server accesses uh the back end, they are critical. And for

back end, they are critical. And for this one, there is the whole section in

this one, there is the whole section in MCP specification which talks about the

MCP specification which talks about the use of extended or

use of extended or uh which talks about the use of dynamic

uh which talks about the use of dynamic client registration

client registration and observability which I also mentioned

and observability which I also mentioned uh makes u uh makes importance in here.

uh makes u uh makes importance in here. Um as the ecosystem grows now the

Um as the ecosystem grows now the critical uh the uh the criticality of

critical uh the uh the criticality of existence of registries and gateways for

existence of registries and gateways for MCPs similarly to what we have for APIs

MCPs similarly to what we have for APIs grows as well and there are different

grows as well and there are different vendors which already offer the

vendors which already offer the solutions. So some of them are registry

solutions. So some of them are registry solutions to actively discover agent uh

solutions to actively discover agent uh to register MCP servers to be able to

to register MCP servers to be able to discover available tools and to document

discover available tools and to document them. Some others are gateways to

them. Some others are gateways to enforce some policies uh before the

enforce some policies uh before the tools are invoked. So you can have I

tools are invoked. So you can have I just put a few examples in here and

just put a few examples in here and watch the space because for SAP

watch the space because for SAP integration suite there is a road map

integration suite there is a road map item for API management for the next

item for API management for the next year where we shall have uh a feature

year where we shall have uh a feature related to MCP to MCP server generation.

related to MCP to MCP server generation. watch the space uh uh SEP integration

watch the space uh uh SEP integration suite also progresses in in that

suite also progresses in in that direction

direction and um as a conclusion for

and um as a conclusion for considerations you you may have noticed

considerations you you may have noticed that MCP server is yet another microser

that MCP server is yet another microser a lot of good practices and tooling that

a lot of good practices and tooling that we use for microservices observability

we use for microservices observability monitoring security

monitoring security uh administration uh it's all relevant

uh administration uh it's all relevant to a large extent is relevant in here as

to a large extent is relevant in here as well. So if the organization already has

well. So if the organization already has some proven techniques for microservices

some proven techniques for microservices uh administration uh just reuse them in

uh administration uh just reuse them in here they are quite handy.

here they are quite handy. That is from my side and

That is from my side and as a conclusion I would like to just to

as a conclusion I would like to just to reiterate once again that hopefully uh

reiterate once again that hopefully uh you saw uh from this presentation from

you saw uh from this presentation from other materials or uh that MCP really

other materials or uh that MCP really helps to streamline AI to data

helps to streamline AI to data integration. So it really helps us to

integration. So it really helps us to bring data from external data sources

bring data from external data sources into the context of LLMs and to expand

into the context of LLMs and to expand the knowledge of LLM with real uh data

the knowledge of LLM with real uh data with internal data. But there is always

with internal data. But there is always but there are also quite a lot of

but there are also quite a lot of challenges. These challenges a lot of

challenges. These challenges a lot of them they are not MCP specific. lot of

them they are not MCP specific. lot of challenges that we discussed in

challenges that we discussed in performance space in uh life cycle uh

performance space in uh life cycle uh management space especially in security

management space especially in security space they are by no means unique to MCP

space they are by no means unique to MCP but MCP and the more intensively MCP

but MCP and the more intensively MCP service and MCP technology is used it

service and MCP technology is used it amplifies those risks and it introduces

amplifies those risks and it introduces a new ways how we can exploit

a new ways how we can exploit those risks

from my side >> also Awesome. Awesome. Uh thank you very

>> also Awesome. Awesome. Uh thank you very much um for for your presentation with

much um for for your presentation with him. Uh there was uh uh some interaction

him. Uh there was uh uh some interaction in the chat. Uh some some of the

in the chat. Uh some some of the questions I was able to answer. There

questions I was able to answer. There were some positive comments of well as

were some positive comments of well as well obviously like a very interesting

well obviously like a very interesting CPI use case uh that you could have used

CPI use case uh that you could have used uh API management and cloud connector to

uh API management and cloud connector to expose an internal host uh to public

expose an internal host uh to public internet instead of uh enrock. Uh yeah

internet instead of uh enrock. Uh yeah that's true. It's also possible. You can

that's true. It's also possible. You can do so much with the MCB server lately,

do so much with the MCB server lately, right? uh and yeah there's I guess

right? uh and yeah there's I guess there's so much potential uh regarding

there's so much potential uh regarding regarding the functionality that for

regarding the functionality that for example you can extend in an LLM uh by

example you can extend in an LLM uh by leveraging um

leveraging um uh MCP service right uh now

uh MCP service right uh now and this is um this is not a question

and this is um this is not a question from the audience but it's just my

from the audience but it's just my curiosity on uh on um on what your

curiosity on uh on um on what your thoughts are about the following. So we

thoughts are about the following. So we in in the examples that he show right

in in the examples that he show right it's like we are using

it's like we are using cloud right uh from anthropic um and uh

cloud right uh from anthropic um and uh obviously it's the main proponent of uh

obviously it's the main proponent of uh the MCP uh of MCP right they created the

the MCP uh of MCP right they created the protocol as such um and uh and you also

protocol as such um and uh and you also touch on for example remote MCP servers

touch on for example remote MCP servers and lots of the problems uh that you

and lots of the problems uh that you highlighted which are all correct um uh

highlighted which are all correct um uh involve some type of remote activity. So

involve some type of remote activity. So I'm wondering what if we bring all of

I'm wondering what if we bring all of that local. So local host as in you

that local. So local host as in you running your MCP service, you using a

running your MCP service, you using a local LM which is sort of possible,

local LM which is sort of possible, right? Uh you can you obviously need a a

right? Uh you can you obviously need a a sort of beefy laptop, but nowadays it's

sort of beefy laptop, but nowadays it's like it is possible to actually run it

like it is possible to actually run it run all of these tools locally. You

run all of these tools locally. You might not have access obviously to the

might not have access obviously to the latest and greatest MCP uh sorry LLM but

latest and greatest MCP uh sorry LLM but there but there are some LLMs that are

there but there are some LLMs that are quite capable of running locally. So

quite capable of running locally. So basically what are your thoughts of

basically what are your thoughts of running all this locally and also uh do

running all this locally and also uh do you think that we will be able to

you think that we will be able to mitigate some of these problems for

mitigate some of these problems for example by obviously not a

example by obviously not a hallucinations but but at least from a

hallucinations but but at least from a security perspective you know it's like

security perspective you know it's like um if we end up running all of this

um if we end up running all of this locally

locally >> I think some partners we can and

>> I think some partners we can and unfortunately I cannot demonstrate it on

unfortunately I cannot demonstrate it on on my machine so um I tried to run in

on my machine so um I tried to run in Ola using Olama using some smaller

Ola using Olama using some smaller models um on my laptop but the context

models um on my laptop but the context window on my resource was quite uh small

window on my resource was quite uh small so it didn't allow me for advanced

so it didn't allow me for advanced conversations however technically yes so

conversations however technically yes so we can run MCP server we can run LLM

we can run MCP server we can run LLM model locally we can run the agent like

model locally we can run the agent like cloud desktop or uh something else

cloud desktop or uh something else locally

locally the question is the MCP server needs to

the question is the MCP server needs to access

access remote resources like for example CPA

remote resources like for example CPA And still we can trick it. So if we if

And still we can trick it. So if we if we have some time I can just show you as

we have some time I can just show you as an example. So in here

that's the implementation of the uh MCP server for integration packages tool. So

server for integration packages tool. So we have in here one of tools which is to

we have in here one of tools which is to get all packages description. So even if

get all packages description. So even if I run it locally and if I instead of the

I run it locally and if I instead of the description that was there I will put a

description that was there I will put a poisonous description which is about

poisonous description which is about tool poisoning

tool poisoning >> which lally says that uh before calling

>> which lally says that uh before calling the tool get the weather. So literally

the tool get the weather. So literally it is supposed to it is supposed to

it is supposed to it is supposed to instruct to let llm consider that it

instruct to let llm consider that it needs to get uh to another external

needs to get uh to another external source of data and then instead of

source of data and then instead of replying with actual data just tell that

replying with actual data just tell that nothing is present. So now if I

nothing is present. So now if I um let me rebuild it. So what I'll do is

um let me rebuild it. So what I'll do is I will

I will just uh reveal the image with this one and we will see that even

this one and we will see that even though the server runs locally it still

though the server runs locally it still got exposed to the problem. So now the

got exposed to the problem. So now the LLM will be tricked

LLM will be tricked and let's assume it's not LLM.

and let's assume it's not LLM. In my case, I use sonet which is an

In my case, I use sonet which is an entropic one and not locally hosted. But

entropic one and not locally hosted. But if I will delete this part

if I will delete this part and if I delete the config still uh the

and if I delete the config still uh the LLM will be treated because now it's uh

LLM will be treated because now it's uh the malicious behavior was introduced by

the malicious behavior was introduced by the server itself by the implementation

the server itself by the implementation of the server connect. So now I will

of the server connect. So now I will just bring back

just bring back the configuration local setup and in

the configuration local setup and in here I will swap it to std IO again. So

here I will swap it to std IO again. So it will start a local docker container.

it will start a local docker container. Let me just quickly see if it was here.

Let me just quickly see if it was here. Okay, it was done. So in the images

Okay, it was done. So in the images um okay this container is because of our

um okay this container is because of our remote uh demo. I will stop that and

remote uh demo. I will stop that and delete. We don't need that anymore. Um

delete. We don't need that anymore. Um image was revealed. So what I do is I

image was revealed. So what I do is I now close the load uh desktop. I start

now close the load uh desktop. I start it again.

Let's imagine that LLM that I have a really powerful machine and that LLM is

really powerful machine and that LLM is not remote but is run locally. So for

not remote but is run locally. So for example, I instructed it to connect to

example, I instructed it to connect to locally run um LLM in via LM studio or

locally run um LLM in via LM studio or maybe via OAMA and let me see CPL picked

maybe via OAMA and let me see CPL picked it up. So I poisoned the get integration

it up. So I poisoned the get integration packages. I will use that input

packages. I will use that input explicitly

explicitly and you recall that in in the tool

and you recall that in in the tool description I instructed that we need to

description I instructed that we need to get weather and then to say that there

get weather and then to say that there is no data in the tenant because the the

is no data in the tenant because the the LLM must think that the data is too

LLM must think that the data is too sensitive to get exposed. You see London

sensitive to get exposed. You see London weather that call it wasn't present in

weather that call it wasn't present in the demo at the beginning of the session

the demo at the beginning of the session because it wasn't there and now uh the

because it wasn't there and now uh the tool was poisoned in the way could tweak

tool was poisoned in the way could tweak LLM think that it really need to get

LLM think that it really need to get weather from London because otherwise

weather from London because otherwise something will not work and even though

something will not work and even though packages data was retrieved by the tool

packages data was retrieved by the tool >> thought LLM concluded that well the tool

>> thought LLM concluded that well the tool description says that I'm not allowed to

description says that I'm not allowed to expose this information so I say that no

expose this information so I say that no integration packages were

integration packages were And that's the example. You see it was

And that's the example. You see it was very quick example. So I it didn't take

very quick example. So I it didn't take long time to to poison it. Uh it's

long time to to poison it. Uh it's in here it's quite visible. But on the

in here it's quite visible. But on the other hand if I go just go in here and

other hand if I go just go in here and if I go to some examples

smuggler. So there is the way how we can take this

So there is the way how we can take this instruction.

instruction. I'm I'm just copy copy pasting that

I'm I'm just copy copy pasting that instruction and we can set uh we can

instruction and we can set uh we can encode it to unprintable uh characters

encode it to unprintable uh characters in uni code. So now

in uni code. So now I go back to

I go back to I change this but

I change this but I say get integration packages.

I say get integration packages. But this time I also insert

But this time I also insert that one. I copy pasted it from the

that one. I copy pasted it from the smuggler. That's one of a text that can

smuggler. That's one of a text that can be used when the text is included in ask

be used when the text is included in ask characters which are not printable. They

characters which are not printable. They are still here but they are not

are still here but they are not printable. So they are not visible to us

printable. So they are not visible to us except that this icon because I used in

except that this icon because I used in the smuggler I selected so that end

the smuggler I selected so that end start and end step are still provided.

start and end step are still provided. So as a result some smart editors can

So as a result some smart editors can alert it but we don't see the text

alert it but we don't see the text anymore. How uh however LLM will will

anymore. How uh however LLM will will get it because LLM will be able to

get it because LLM will be able to decode it. It's still a sequence of as

decode it. It's still a sequence of as key characters.

key characters. Good news well shall I say good news is

Good news well shall I say good news is that still it's visible to us to some

that still it's visible to us to some extent. So if we go to hex editor and if

extent. So if we go to hex editor and if we search for description in here

we search for description in here [Music]

[Music] >> you see

>> you see >> yeah there is

>> yeah there is >> pl text in here

>> pl text in here >> in here it's not present oh well it's

>> in here it's not present oh well it's present sorry it's not it's present but

present sorry it's not it's present but it's not visible it's not printable to

it's not visible it's not printable to us but

us but >> if I look into here

>> if I look into here >> in description we have get integration

>> in description we have get integration packages but then we have whole lot of

packages but then we have whole lot of thing that is not visible in printable

thing that is not visible in printable range of as key characters but we have

range of as key characters but we have hex codes in here and if now I copy

hex codes in here and if now I copy paste it and decode it back to uni code

paste it and decode it back to uni code I will be able then for example using

I will be able then for example using the same website for asi smuggling or

the same website for asi smuggling or other tools uh I will be able to um

other tools uh I will be able to um create a sequence back from hex codes

create a sequence back from hex codes into asi characters which are

into asi characters which are nonprintable into what is the printable

nonprintable into what is the printable version of it so we definitely see from

version of it so we definitely see from here that there is something in the file

here that there is something in the file and we can decode

and we can decode But it's not visible not now anymore.

But it's not visible not now anymore. what the visible part is quite innocent

what the visible part is quite innocent but with integration packages and that's

but with integration packages and that's the example where I was quite quick to

the example where I was quite quick to poison the tools

poison the tools >> in here to instruct the LLM firstly uh n

>> in here to instruct the LLM firstly uh n react to content it faced from the tool

react to content it faced from the tool faced from CPI and also to get access or

faced from CPI and also to get access or to try to get access to the external

to try to get access to the external resource like to check the where the

resource like to check the where the focus

focus >> um and still the MCP server was running

>> um and still the MCP server was running locally.

locally. >> Yeah. No, that that is very true like uh

>> Yeah. No, that that is very true like uh that that's a very good example. Marin

that that's a very good example. Marin says so LLM needs to inspect the MCP

says so LLM needs to inspect the MCP servers first to check for malicious

servers first to check for malicious code. uh but you know so in the end like

code. uh but you know so in the end like at least the way that I see for example

at least the way that I see for example this FCP servers for example this like

this FCP servers for example this like you end up uh downloading it locally and

you end up uh downloading it locally and running it locally then it's similar to

running it locally then it's similar to for example any let's say node package

for example any let's say node package that you end up uh installing for

that you end up uh installing for example for your project right like in

example for your project right like in the end you sort of in reality not not

the end you sort of in reality not not that we do uh uh it's not just an npm

that we do uh uh it's not just an npm install x but you also need to be aware

install x but you also need to be aware of what's in that package right so this

of what's in that package right so this will be the similar approach, right?

will be the similar approach, right? Yeah.

Yeah. >> Cool.

>> Cool. >> Think of supplying chain risks. So, will

>> Think of supplying chain risks. So, will we trust the vendor? So, what is inside

we trust the vendor? So, what is inside especially if it's compiled or for

especially if it's compiled or for example there is a docker image which

example there is a docker image which was shipped and we need to know what is

was shipped and we need to know what is inside of that docker image or and if

inside of that docker image or and if it's compiled binary that what actually

it's compiled binary that what actually it does it have any malicious

it does it have any malicious instructions or some poisoned tool

instructions or some poisoned tool descriptions. The the challenge here is

descriptions. The the challenge here is that when the AI agent starts and when

that when the AI agent starts and when the information about tools is loaded uh

the information about tools is loaded uh into LLM context, it's the descriptions

into LLM context, it's the descriptions as well that are loaded. So it become a

as well that are loaded. So it become a they become a part of the context. So

they become a part of the context. So they are not stored separately. They

they are not stored separately. They become a part of the context. So if I go

become a part of the context. So if I go in here and if I just start let me start

in here and if I just start let me start float again.

started. So if I go to context, um it's pretty much empty or not empty but

it's pretty much empty or not empty but it's very small amount of data in here.

it's very small amount of data in here. But if I would have invoked some uh data

But if I would have invoked some uh data through the MCP tool, we would see more

through the MCP tool, we would see more messages coming from here. Even now we

messages coming from here. Even now we see that the context there are some

see that the context there are some system prompts but also there are this

system prompts but also there are this one uh MCP tools.

one uh MCP tools. >> Yeah. So you see this part it's already

>> Yeah. So you see this part it's already sitting uh it already sits in the

sitting uh it already sits in the context and MCP tools here is not only

context and MCP tools here is not only about the name of the tool and MCP

about the name of the tool and MCP server that provides that as a part of

server that provides that as a part of startup as a discovery of which tools

startup as a discovery of which tools can be used but also things like tool

can be used but also things like tool description it also becomes a part of

description it also becomes a part of the it gets added to the context so that

the it gets added to the context so that LLM it's done for a good reason because

LLM it's done for a good reason because um an MCP tool descriptions can be

um an MCP tool descriptions can be descriptive enough to help LLM better

descriptive enough to help LLM better understand whether it needs to call this

understand whether it needs to call this tool or not in certain use cases and it

tool or not in certain use cases and it can contain some examples uh for MCP

can contain some examples uh for MCP tools usage uh so that LLM can better

tools usage uh so that LLM can better understand how when to call the tool and

understand how when to call the tool and how to call the tool but as an example

how to call the tool but as an example that I provided here tool poisoning

that I provided here tool poisoning quite often vector of attack is we can

quite often vector of attack is we can poison the description of the tool

poison the description of the tool >> very

>> very >> even though server runs locally

>> even though server runs locally >> already poisoned so already we are in

>> already poisoned so already we are in the trouble mode

the trouble mode >> yeah uh yeah that's true okay well you

>> yeah uh yeah that's true okay well you I'm conscious of time and we run past 20

I'm conscious of time and we run past 20 minutes. So uh thank you very much for

minutes. So uh thank you very much for well you know what sharing all your

well you know what sharing all your expertise as part of uh this session. It

expertise as part of uh this session. It was fantastic honestly. Uh and would you

was fantastic honestly. Uh and would you mind just

mind just >> just one last moment we where you we may

>> just one last moment we where you we may find some examples. So recently and last

find some examples. So recently and last week entropic released entropic agent

week entropic released entropic agent skills. It's a separate area. I will not

skills. It's a separate area. I will not talk about it now. It's very useful

talk about it now. It's very useful interesting area but in the repository

interesting area but in the repository for entropic skills there is one skill

for entropic skills there is one skill which is MCP builder and in the

which is MCP builder and in the reference documentation entropic

reference documentation entropic published MCP best practices. So that's

published MCP best practices. So that's intended for LLMs for auto generation of

intended for LLMs for auto generation of MCP servers but some things that we

MCP servers but some things that we discussed including some security things

discussed including some security things you can really get it in here. It's it's

you can really get it in here. It's it's it's condensed format but it provides

it's condensed format but it provides quite a lot of aspects also security

quite a lot of aspects also security related how a good MCP uh server shall

related how a good MCP uh server shall look like from entropic perspective. So

look like from entropic perspective. So sorry for for getting into that's the

sorry for for getting into that's the last bit of information that I wanted to

last bit of information that I wanted to show just as a reference uh document

show just as a reference uh document that's another source where we can read

that's another source where we can read more about guidelines for MCP server

more about guidelines for MCP server development and also about security

development and also about security aspects.

aspects. Okay, actually that's uh that wasn't uh

Okay, actually that's uh that wasn't uh under my radar, so I'm just trying to

under my radar, so I'm just trying to get to the URL so I can end up sharing

get to the URL so I can end up sharing it with everyone on the chat. Uh that

it with everyone on the chat. Uh that was quite good. Uh thank you very much

was quite good. Uh thank you very much uh for sharing this uh and I just put it

uh for sharing this uh and I just put it uh on the chat. Um War if you use MPX

uh on the chat. Um War if you use MPX config in MCP config for your ID this

config in MCP config for your ID this update automatically. So another thing

update automatically. So another thing that could go wrong. Yeah, there are

that could go wrong. Yeah, there are many things that can go wrong here like

many things that can go wrong here like uh we need to be aware of uh of all of

uh we need to be aware of uh of all of them. So yes uh well thank you very much

them. So yes uh well thank you very much Vim. It was great uh another great

Vim. It was great uh another great session part of the tour as well. So

session part of the tour as well. So thank you very much for delivering it.

thank you very much for delivering it. Um and uh I want to also thank uh the

Um and uh I want to also thank uh the viewer for staying us uh for a bit

viewer for staying us uh for a bit longer. Uh but it was just an

longer. Uh but it was just an interesting session. So yeah I hope you

interesting session. So yeah I hope you all enjoyed as much uh as um as I did. I

all enjoyed as much uh as um as I did. I I learned a lot. So it's great. It's all

I learned a lot. So it's great. It's all about learning as part of the to right.

about learning as part of the to right. So thank you and um really thankful

So thank you and um really thankful Antonio really thank you and sorry for

Antonio really thank you and sorry for taking extra extra time on the session

taking extra extra time on the session but I'm really thankful that you posted

but I'm really thankful that you posted the session and all other sessions

the session and all other sessions >> and it's it's an honor for me to wrap up

>> and it's it's an honor for me to wrap up uh this year's integration track really

uh this year's integration track really uh really really interesting uh thank

uh really really interesting uh thank you for your time and for all

you for your time and for all participants and for all questions for

participants and for all questions for everybody who listened or maybe who will

everybody who listened or maybe who will watch recording uh thank you for your

watch recording uh thank you for your time

time >> thank you thank you all right and that's

>> thank you thank you all right and that's it all See you.

it all See you. >> Take care.

>> Take care. >> Bye. Bye.

>> Bye. Bye. >> Goodbye.

>> I've stopped the live stream. Uh so yeah, we had a peak

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YouTube Transcript:🔴 A(P)I: Unified connectivity for AI applications using MCP