Welcome to episode 407
of the Microsoft Cloud IT Pro podcast recorded
live on 07/25/2025.
This is a show about Microsoft three sixty
five and Azure from the perspective of IT
pros and end users, where we discuss the
topic for recent news and how it relates
to you. In this episode, we dive deep
into the USB port of AI communications
as we discuss model context protocol or MCP.
This is a game changing specification that's extending
the capabilities of large language models or LLMs
to help you bring your data together into
a central LLM of your choice. We'll talk
about what they are, where you can use
them, and things you may want to think
about when it comes to using these MCPs.
So let's dive into the show.
Scott, we're between vacations. I'll pretend I'm surprised.
Yeah. I know. You just got back from
vacation. I'm about ready to leave for vacation.
Summer recording is always interesting. I had a
good vacation.
We went to
we we did Oregon,
Northern California
and just did a a ton of driving.
So we did about 2,000 miles in ten
days. How many kilometers
for our non American friends?
What is it? It's multi multiply
by, what, three? Something like that. I don't
know. I don't know. It's a bunch of
kilometers. But yeah, good trip, lots of hiking.
We went and saw like the redwoods and
did,
Crater Lake and all sorts of stuff. 3,218
kilometers is what you drove. Not too shabby.
Thank goodness for rental cars and somebody else's
mileage. That sounds good. I'm kinda I'm jealous.
Yeah. I have never been out, like, Northern
California,
Oregon area. Have you ever seen The Goonies?
I don't you're gonna make fun of me
again. I don't know that I have. Then.
Another one of those. If it's been a
while if I have, it's been a while.
I'll make you set up the projector outside.
I'll come over. You can cook me dinner,
and we'll watch you at least. The Goonies?
So that was kind of our one of
our last stops on that trip was staying
up in Cannon Beach where Haystack Rock is,
and it's like the big rock that the
pirate ship floats out of at the end
of The Goonies and things like that, so
that was kind of fun too. The other
really nice thing
about
that part of California, like the very tip
Northern California up through Oregon, you're in the
Pacific Northwest in the summer,
it is not like Florida in the summer.
So Florida in the summer, like I'm looking
right now, it's
88% outside with 82%
humidity and feels like 106%.
It is miserable and you do not want
to be outside.
In that part of The United States, it
is
because you're right on the coast, so you
get that coastal fog in the morning. Some
days you can't even see the sunrise or
the sunset because the fog is so thick.
70
degrees, 60 degrees,
it all just hits different. It was absolutely
beautiful. I got super sad when I got
there because I forgot to pack a pair
of shorts and I was like, Oh my
gosh, it's going to be like seventy, eighty
degrees, it's going to be hot. It was
perfectly comfortable every day in a sweatshirt
and long pants and all that stuff.
So it was really great, good little change
of pace. I'm ready to go back, I
think, on vacation. Doctor. On vacation? See, I'm
looking forward to it too. We're going up
north to Michigan, and then we're going out
to Denver. And same thing, I'm like, it's
not Florida in the summer.
It'll be beautiful. With that, can you use
an MCP
something or other to help you plan your
vacation? To plan my vacation? Was that was
quite a stretch there, Scott, for it. For
a segue, you were looking for one? Yeah.
It all went sideways. You mean a Microsoft
certified professional? Do Microsoft certified professionals? We like
acronyms. Right? It's what an MCP used to
be. I guess it still is. I was
thinking more like model context protocol
and
some of the new specs and things have
been coming out of anthropic and then the
really cool nifty functionality
that's been coming out along the way. Yeah.
I mean, I suppose you could. Right? Like,
with MCPs
and kind of the goal, like you said,
model context protocol, is that this kind of
extends LLMs to other data sources. So I
suppose
maybe if there's MCPs
out there to connect to
some type of data that you might want
to use to plan your vacation or something
that you're planning your vacation from, you probably
could use an MCP to plan your vacation,
Scott. Probably. What is an MCP? If it
hooked up to the right set of data
sources. Absolutely. Yeah. With vacations, this is gonna
be maybe the should we give people context?
We are doing a longer episode that we're
gonna break into two parts today because of
vacations. So we're gonna kinda start off with
what is an MCP
followed by how do you use MCPs? We're
jumping ahead of ourselves.
So what is an MCP, Scott? What is
the model context protocol
from Anthropic,
creators of also Claude? Model context protocol is
a specification,
like first of all, right? It's a Doctor.
Joshua Klooz (zero 50 three:fifty three): Yep. Doctor.
Joshua Klooz (zero fifty three:fifty three): Framework
and architecture
for
developing
servers
and clients
that can interact with large language models
and LLMs and become part of
agentic workflows
for LLMs. So they can surface very specific
pieces of functionality.
So for example,
you might have OpenAI out there and you're
using ChatGPT
four o, and you're chatting with that, and
that thing was trained on a specific set
of data.
It could be that you're maybe grounding it
in some additional data. Maybe you're doing rack
or things like that. But maybe you need,
like, additional functionality
or more
system or service
specific functionality
to do things. For example, let's do like
a pie in the sky thing. So
I have a Notion database, and one of
my Notion databases is recipes, where I capture
recipes that I find on YouTube. There's a
sub database that actually has, like, the recipe
itself. Here's the steps. Here's the ingredients,
all those kinds of things along the way.
So one of the things you might be
able to do is go out and ask
an LLM,
hey. Can you give me a new recipe
for
roasted chicken, roast potatoes, asparagus,
and make it a little spicy? And what
would be good if it had, like, these
flavor properties?
Well, if you had it tied into a
tool with an MCP agent, you could also
extend that prompt and you could say, once
you've have that recipe and I've iterated on
it, great. Hey, that recipe looks good. Can
you take it and save it in my
Notion database over here? And then because now
you have the ability to
have that Notion specific
functionality
tied into
your client that you're chatting with that LLM,
now you can start to chain that together
and you can, like, push that information down
from the LLM
out to an external tool. Or you can
do this in other ways. Like, let's say,
for example,
in Azure land, there's an Azure MCP server,
and that server lets you do things like
list the resources
in my subscription.
Great. Like, list all the resources.
Now, once you've listed the resources, go find
the properties for those resources. Now, once you've
found those resources with those properties, maybe take
them and write them out to this other
thing. Like, send them to Slack, send them
to Notion, send them to OneNote, something like
that, so that you can continue to have
it go further. So you end up in
this world where you can extend LLMs
with
domain specific knowledge and domain specific functionality that's
built by developers and by these services, and
then you can chain it all together because
now the clients that are interacting with these
LLMs and have the ability to integrate with
MCP servers,
they can also know, like, oh, hey. I'm
looking at an agent or a tool that
surfaces this piece of functionality.
Therefore, go talk to this, chain it into
this, things like that. So that's all really
cool because you can do that just through
a singular client
and kind of like a real time, like,
back and forth kind of interaction.
The other way that these things can be
used is as part of agentic workflows.
So rather than me being like, hey. I'm
gonna sit here and prompt you in real
time, maybe I just have a prompt that
kicks off overnight and does something in the
background for me. And it's like one agent
talks to another agent, talks to another agent,
talks to another agent, talks to another agent.
So if anybody's maybe been playing around with
some of the agents or tools, like, they're
called different things all over the place. Like,
in in Microsoft three sixty five Copilot, they're
called agents. If you go into like Copilot
Studio, you've got these things called tools and
other bits and pieces. Like this is a
more rich interaction
surface
on top of those things that have been
there before. I think one of the weird
things about MCP in general is when I
think about it, I think about more from
like the end user side, like how do
I as a user get started with it?
Well, there's a whole bunch of LLM clients
out there, like Copilot or heck, ChatGPT itself,
that don't allow you to actually interact with
MCPs,
but others do. So if you have maybe
like Claude or Perplexity on the desktop,
then those let you run and integrate with
MCP servers, both local servers and remote servers,
which that's a distinction that we should talk
through, tools like
AI driven IDs like Cursor, WindServ,
Versus Code as well, right, if you're just
looking to, like, pick this up and and
get going and get free free stuff along
the way. So
I think they are
a
weird new enabler, Like, they're definitely, like, cutting
edge and we're on some, like, precipice or
cliff, and it's not clear, like, does the
ground continue to extend in front of us?
Do we fall off the edge, and what
does that look like? But it's definitely, like,
exciting just to, like, be able to get
hands on with some of this stuff and
leverage it and find ways to
use it in
your day to day workflows, be it like,
hey, you're a consumer. Like I said, maybe
you're sitting here and you're doing, like maybe
you're like a content creator and you're doing
video creation and you say, okay, hey, ChatGPT,
create my script. Well, great. Go into Versus
Code, have a chat with ChatGPT four zero,
create my script, now take my script and
push it out to here, and let me
leverage it that way or have it and
iterate it on it, things like that.
Or just in my day to day professional
job, I'm always looking at
documentation,
code, samples, all those kinds of things. So
there are very specific
MCP
server implementations that I can integrate with a
client
and be able to really kind of just,
like, accelerate
and augment those workflows within my day to
day, which is very exciting. Right? Like, I
think it's, like, I like cutting edge and
raw, and it's always fun to get hands
on with a new tool or a new
toolset. A lot of these and where I've
started playing with MCPs too is you mentioned
like Claude or OpenAI or Copilot.
It's you're limited in what data you can
access. You can go build an agent in
Copilot,
but in that case, Microsoft has very
defined connectors. They're starting to get more with
some of the custom connectors and different things
there, but like OpenAI. OpenAI,
the data sources,
kind of before MCPs, were a lot more
limited. OpenAI is set to go look at
the web or look at this data or
use this model. Claude, same thing. This is
really opening up that ability to say, now
I want Claude to go look at all
this other data that's more specific to me.
Like you mentioned, I want Claude to go
be able to look at my data in
Notion, or I want Claude to be able
to go look at the data in a
SQL database. I want Claude to be able
to go look at
data over in
Microsoft three sixty five in SharePoint land. It's
allowing you to bring your own it's almost
like bring your own data. Do we have
a BYO instead of BYOD for devices? It's
BYOD for data. Bring your own data into
these different LLMs so that as you're querying,
you can
get different responses, better responses, more accurate responses,
or responses very specific to
the data that you want these LLMs to
be able to parse over and
pull these results from or these responses from?
It's that domain specificity
and the ability to plug and play domain
specificity. So Anthropic, the folks who make Claw
and SONNET and all that stuff, they are
the ones who came up with this specification
and pushed it out there into the world,
and one of the ways that they frame
it is
MCP
is the
USB port of
AI communications
and kinda AI interaction.
So if you think about that, like, if
you think about, hey, I have this LLM
that's sitting here, and now it's got an
infinite number of USB ports on it. Just
think about, like, all the things that you
plug into your computer to help you do
your job better. You plug in the dongle
for the mouse. You plug in your monitor.
You might plug in a dock. I've got
an audio interface. I've got a camera. All
these different things sitting in front of me.
Well, those abstractions
also apply in this world of MCP, where
you've got an LM that's now kinda sitting
there, and you've it's got, like I said,
infinite USB ports, and you just start plugging
in that domain specific knowledge
and now the cool thing about the spec
and the way it's laid out is it's
kind of built around this core set
of building blocks that they see and that
sit out there. So clients can have a
whole specific set of functionality
where they can plug into a couple underlying
building blocks within the MCP protocol itself. So
you've got resources, prompts, tools,
there's some other more esoteric things that I
don't think I've seen involved in too many
MCP implementations like sampling and roots and elicitation
and things like that. But there's kind of
like these core building blocks in
tools,
resources, and prompts. So tools are these things
that
are specifically for AI actions
and AI interactions.
They are controlled by the model itself, so
the LLM is doing things. So that might
be like I have an MCP server maybe
that searches for flight information. Right? Maybe it
integrates with, like, kayak.com
or something like that. Well, that could be
controlled by the model where I could have
a tool in an MCP server that's directly
integrated with the Kayak API where I could
say, hey. Go search I just tell my
LLM. Go search for flights on this date
with this carrier from a to b. And
based on the context of, oh, I see
the user is searching for flights, and I
see I have this MCP server over here
that I can attach to, and it's offered
me an agent with this capability. Let me
go use that. Let me go get that
domain specific knowledge. So that could be like,
like I said, something corny. You're searching for
flights. You're working on your scheduling, calendaring,
simple things like that. Maybe you're using it
as a way to send messages back and
forth in Slack or things like that. So
that's one of your building block is tools,
which are model
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And then you have these things called resources,
which are application controlled or controlled a little
bit more by the
server implementation, the MCP server,
server implementation itself. So those are all for
contextual data. So those would be things like
documents,
calendar events, could be emails,
could be other domain specific data things like
that like anthropic I think in one of
their examples uses like weather data
as a thing that's there in that first
one where I had a tool I said
go search for the flight and go do
these things for me The other thing that
I could do is then I could have
a resource
that goes and actually reads out of my
calendar. So don't just go search for flights.
Maybe I don't say search for flights between
these dates where I say search for flights
between these dates
that don't conflict with my existing calendar. And
if there's an integration with, like, Google Calendar
or Outlook or things like that, then you've
got all that. And then finally, a third
building block that kinda comes into play with
these that you'll see in a bunch of
client implementations
is prompts.
So these are for, like, interactive
templates basically.
So it's all user controlled.
So when you're kinda you can basically ground
these things and give them a set of,
like, base instructions
that help them understand
where you want them to go with it.
So if I'm back to, like, that thing
of kayak and search for flights, well, maybe
I'm planning to go on a vacation or
a business trip. So I could provide some
very specific grounding that says, I'm planning a
family vacation
for a family of four. My wife likes
to do this. My kids like to do
this. And now I don't have to put
all that into as I'm prompting the LMS
as I go along. It's just like a
base set of instructions that it knows. Maybe
you've got, like, multiple
MCP servers that you're talking to. Maybe you've
got one for, like, Kayak. Maybe you got
one for Google flights. Maybe you've got one
for, like, a carrier like Delta. Maybe you've
got one for, like, hotels like Marriott, things
like that. And you can actually tell it
in the instructions like, hey, I I really
prefer you to book and look for hotels
with Marriott or like me, like, I tend
to fly with Delta a lot. Like, please
only pick Delta flights with
no more than two stops and that go
through these hubs kind of thing. So they
just enable and open up this really kinda
interesting
world. So you've got a little bit of
a graphic up on the screen now that
kinda talks about and speaks to clients and
servers
and how they come together.
So with MCP,
obviously, you need an endpoint or a thing
that you can talk to, that you can
have the LLM and the ID or the
client that's able to recognize MCP servers that
it, like, knows how to talk to it
and get out there. So a server
and the implementation of a server can be
local or it can be remote. So for
example,
there is
an Azure,
there there's a Microsoft Learn MCP server. And
what the folks at Microsoft Learn did was
they actually stood up an MCP endpoint, and
it's an endpoint that's compliant with the MCP
protocol.
And you can install you can install an
MCP server for Microsoft Learn into
your local client. But when you're installing that
server, all it's really doing is talking to
a remote HTTP endpoint. It's doing, like, SSE
and stream mobile HTTP and things like that.
The alternative way you could do it is
and how some of these have manifested is
they've manifested as local server implementations.
So literally you go in and you say,
okay. Here's the configuration for an MCP server.
And quite often these things will be spun
up inside of, like, containers or just little
m MPM servers that are running locally, where
it's running a web endpoint
locally on the same client where you're executing
the client with the prompt and things like
that, and you've got a little bit of
a different flow there. But you can always
mix and match all these things. Right? Like,
this is all just, like, how many of
these things do I chain together? What what
what are they, and how do they all
manifest? So the biggest thing you gotta figure
out is, like, hey. Like, is there,
is is there a use case for me
with MCP, you know, given your persona,
you you know, your work identity,
whatever it is you happen to do and
and how you wanna use it? And if
yes, then what's the client I wanna use
to get there? Because there are a there's
a pretty broad set of clients that can
leverage MCP integrations.
But that said, you gotta kinda, like, pick
the right tool for the right job, and
and that's a little confusing right now because,
like I said, things like Copilot desktop, Microsoft
three sixty five Copilot, they don't have the
ability to integrate with an MCP server, nor
does the, I believe, the OpenAI desktop client.
But that said, the Claw desktop client does,
the perplexity client does, Versus Code does, Cursor
does, WinSurf does, like, like, all these different
things that are out there. And then and
then, like, I'm I'm missing a whole list.
Like, if you go on, like, the anthropic
documentation, like, they've got, like, gobs and gobs
and gobs of clients and which ones work
with resources, which ones work with prompts, which
ones work with tools, all those kinds of
things along the way. I would imagine for
most of the folks that probably listen to
our podcast who are living in the
Microsoft ecosystem,
you probably work with an MCP
capable client already. I know, like, I live
in Versus Code, like, 90% of my day.
That's all just there and ready to go
and
available to me. This stuff is, like it
looks a little geeky, like, when you start
to get into it, but, you know, you
need to know, like, hey. How do I
define an MCP server that already exists out
there? And some of the clients, you just
have to pull like, just post, like, and
copy and paste, like, raw JSON into these
things to get them going for the configs.
So that can be, like, a little intimidating.
But once you've done all that, like, really,
it just becomes chatting with an LLM, and
now you've extended the you've extended the capabilities
of that LLM. And by extending the capabilities
of that LLM, I think really what we're
saying is we're extending the capabilities of ourselves,
right, because we were using those LLMs to
enable our jobs and enable our workflows and
and move those things forward. So that that's
kind of an exciting thing. For sure. And
like you said, there's a whole list of
clients.
I think OpenAI is getting closer to having
more. Like, with some of the connectors they've
put in there, their desktop client is getting
close, and I've started playing with a few
of these clients.
Because of that, with Claude, with OpenAI, with
Versus Code,
all of those. But one thing that's interesting,
and this is something again, we're talking about
using these clients, hooking them up to other
data sources, pulling them into an LLM, kinda
like this architecture diagram
you were talking about where you have your
MCP host or your AI application, OpenAI Cloud.
You're using the clients, and you're going out
and connecting it to all this data. Tying
that into another concept you mentioned where
Anthropic described this as the like a USB
c port where you can have unlimited USB
c ports plugged it in. What is the
first thing you you're taught with data security,
Scott, with USB ports? You plug in any
random stuff you can. Plug them in. Right.
When you see a USB port, just plug
it in. I think that's one interesting aspect
too when you start thinking about these MCPs
is to that extent, what are you plugging
your data into? If I go in and
you mentioned
you give it access to your calendar, you
give it access to your Notion database, maybe
you're giving it access to a SQL server.
You're giving it access to your file server.
It's actually now pulling data from all these
different data sources
into an LLM. I know SharePoint is one
that has started coming up more and more
as well is
from that security side, like Microsoft three sixty
five, you can do sensitivity labels, you can
do security.
They've built a bunch of different controls into
SharePoint
so that you can control how
Copilot and
the LLMs behind
Microsoft three sixty five Copilot
interact with your data. What data they're allowed
to interact with? How does that handle
sensitive information? How does it handle Social Security
numbers that it may come across? How can
you define different controls? But now what happens
if you have
a connector, an MCP, that maybe goes and
looks at your Microsoft three sixty five data
and starts pulling all these files and documents
in because there's an API for them. You
can get them. But that MCP or that
connector
may not have all those same controls that
Copilot has in place for, especially, I think,
of things like sensitivity labels and how you
can filter things out there. Permissions, the ACLs
that are maybe on some of these files
are a little bit easier. But I think
you really also need to start thinking about
now from a security perspective,
whether it's at a corporate data level or
even your personal data, what's happening to your
data
as
you're bringing it into these different LLMs? For
sure. And I'll I'll I'll throw you one
more on top of there. So,
if you think about a
local MCP server, so like I said, like,
often you can spin these things up Yep.
Just like a simple m MPM Docker on
your machine. Yep. Do you do it in
Docker where it's isolated and you have maybe
some more of those operational controls, but then
you might have to contend with things like
container networking
and and routing and other constructs?
Do you end up in a place where
you have just a bunch of random web
servers running, like, locally on on your on
your machine? Right? Like, how many tie how
many MPM servers do you wanna spin up
in the background
for API endpoints for these specific pieces of
functionality?
Because really, like, in some cases, what we're
talking about sometimes is even, like, little helpers
to to do things. Right? Because you could
have, like, you could have an MCP interaction
where the MCP is interacting with, like, a
local file on your desktop,
and then it's taking information from that file,
pushing it to a cloudy service, a sassy
service, whatever that is, vice versa. So I
I there there are a whole bunch of
considerations there. You know, these things are not
super mature in the sense that, you know,
they've been out for years and years and
years, and we understand all the edge cases
and the flows and, you know, do they
have the right operational controls, things like that.
So it's like, yeah, I think you need
to be a little bit, like, careful with
them, but I don't know that, like, where
things are today
that it's as big of a concern as
it might be. Like, I see a lot
of these things as
accelerators for developer,
workflows, accelerators
for, like, no code, low code kind of
workflows, those kinds of things.
And, you know, if you're scared of the
destructive nature of something, like, just don't use
it. Keep doing it the way you've you've
been doing it. Right? Like, nobody's saying, like,
hey. You have to do these things. But
certainly, yeah, like, treat them safely, right, if
you can. Run-in a sandbox the first time.
Figure out, like, hey, is is is this
the right thing for me, my workflow, and
what I'm trying to
accomplish right now? Yeah. And I think there
are some scenarios I think about when we
get into some of the examples and start
talking about maybe
how we've used them that I think about
a little bit. There's an aspect to some
of these too, depending on the MCP you're
using. Like, I would love to see these,
and I'm hoping they will get there eventually
come to
something like Copilot so I can tie some
of these MCPs that I wanna use into
my Microsoft three sixty five environment so that
I can start maybe supplementing Microsoft three sixty
five Copilot with some of the data that
would come from an MCP. Right now, I
feel like I'm in the opposite boat where
things like Cloud and OpenAI are building all
these connectors and allowing me to pull data
in from all of those, including from SharePoint,
where I'm starting to sometimes even find myself,
it's like, well, it's almost easier to use
OpenAI
or
Claude
or some of these other LLMs
just because
the openness is there to get
some of this other data in from other
tools that I use
where I mean, personally, I would love to
just have it in Copilot. Some of it,
again, due to the nature of the data,
some of it is, like, a lot of
these MCPs yet, they're still paywalled. I'm paying
for Claude right now. I'm playing for OpenAI
right now, and I'm paying for Copilot right
now. Just so I can test all this
out where if there started to be some
of that feature parity, I could not pay
for all three of them.
Yeah.
I think it is tough to get there,
right? Like, there's certainly, like, an ecosystem
kind of thing. I I will say, like,
you can get by a lot with a
lot of the stuff just, like, on the
on the on the free side.
So, you know, like, if you're using, like,
Claude, Claude desktop, Perplexity, things like that, you
don't need to be on, like, the paid
plan to integrate an MCP server with with
Claude. I think most of these even like,
all all the ones that I can think
of, like, they do have some free version.
Even, like, GitHub, like, hey. I'm gonna turn
on GitHub Copilot because I need GitHub Copilot
to be able to do the chat with
the LLMs,
and then I'm, like, adding these agentic flows
to it. That stuff's available and ready to
go, and and you can do that pretty
turnkey. So you might be limited in some
functionality and some other things that are out
there. I don't think that stops folks from
trying to get, you know, hands on with
it and and see where some of that
value is. And as we get into
our later conversation about, like, maybe some specific
MCP servers, at least everything I plan to
talk about, like, it's all free or it's
already integrated into the ecosystems that you're probably
in anyway. You're paying for, like, the underlying
thing. Right? Like, if you use, like, the
Azure MCP server, then you're probably already an
Azure customer. Yeah. It'll be interesting to see
where this goes, see where the architecture goes,
see how these different clients start bringing them
in, incorporating all of it together. Yeah. So
why don't we,
wrap this one, and then we'll come back
for a part two, and and we'll talk
about some
specific
MCP servers and kinda like how we're using
them in in our day to day and
and kinda how our journey has been here
a little bit, and maybe that informs some
others. You know, if anybody's out there and
you're using an MCP server, like, we we'd
love to hear from you, like, hey, What
are you using it for? What's going on?
Like, what's your specific
use case use cases?
You know, I I think Ben and I
are, like, pretty big on just, experimentation. Ben
always talks about his list that is ever
growing. I've got a similar one. Like, we'd
love to hear how others are enabling, like,
their workflows. For sure. So reach out. Let
us know.
Again, LinkedIn is probably the best. I am
finding that as becoming my social network
of
choice lately Mhmm. Or contact page on the
website, mscloudi2pro.com.
We'd love to hear from you. Alright. Sounds
good. Hopefully, this was enthralling and folks come
back for part two.
Absolutely. Alright. Thanks, Ben. Yep. Thanks, Scott.
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