All right, so at the beginning of April I went to New York for
the MCP Dev Summit, and while I was there I got the chance to
record a few podcasts with attendees that were at the
event. This is one of those
conversations. Hope you enjoy.
I think the main thing is that people don't quite understand
what it is that easily. People think it's some way of
mixing AI into your API in some way.
Like API is going to change what it does and magically it's going
to sort of do new things, which of course isn't what it is.
It's AI using AP is there's a lot of questions you get that I
can't answer that well because they're about other ways to
actually like inject prompts back into the LM or you know,
other things. But I think part of it is just
that if you're using MCP to use your API, it's good to not
exposed to much of it, and it's good to tell us if API be very
secure in the first place. Hi, I'm Alex Saltkeever and I'm
here at MCP Dev Summit North America in New York City.
I'm here with Ben Morse of Deep Bell, which is a Tell us what it
is, please. What is Deepel?
Deepel is AAI language translation company based in
Germany. We've got about 1000 employees,
so it's like a popular like phone app and web app and such
thing in countries like Germany and other European countries and
also Japan and even Korea not as well known in North America.
So hire people like me to be here in North America and say
hey, we exist. Hello.
Hi, here we. Are thank you.
The reason I wanted to talk to you though is your recent MCP
roadshow. 10 countries in 10 days I believe it was.
It wasn't quite 10 countries, but there were 10 days.
But there were. How did?
This How did this come about? This mad plan to spread MCP to
the world. Yeah, it was kind of, it just
kind of grew organically. So when I got to Deep L and
became the developer advocate, a person had this remit to make it
to people knew what Deep L was in North America, and the
developers knew we existed. So I had to go out there in
communities and just find people.
And so I was looking for ways to do that.
I thought, here's a hackathon in San Francisco.
Like this was like May 2nd. In May, Sorry. 10 cities, yeah,
it was 10 planets and 10 light years.
That was, it was 10 cities. I think 10 talks in four
countries. I think, yeah, we're going to go
into the US of course, Canada, France, Germany.
I think those are the countries this demand in more places too,
certainly for this. So anyway, so I thought, OK,
we'll be bold. We'll go to San Francisco and
sponsor hackathon there. And I was, you know, I was
following AI, following agentic AI, listening to lots of
podcasts and trying things out. But I did what MCP was at all.
I just kept hearing about it. I thought, what are we going to
do to go to San Francisco all these cool, like I hipsters, you
know, with all their cool AI companies based on the Area 75
hackathon. So exactly.
Yeah. And like, I noticed you bell at
all we're going to do. But, you know, I'm pretty brave
sometimes. So I figured we would just go
there. And then I heard about MCP and I
thought we should build an MCP server.
This is like the latest hotness, just on the fly.
Like Yolo MCP. That was the idea.
And then there was an engineer who knew about it too and wanted
to try it out out and his manager thought was a good idea.
So we just built an MCP server, mostly him, his name is Akash
and he did most of the work and we just kind of helped him like
debug a little bit and then so you.
Did this in work essentially like a week or something right?
It was pretty quick yeah, but you can do with MCP, as we've
learned isn't that hard to actually use if you know how to
use it which we didn't of course.
So there were some confusing things that happens and
confusing bugs but basically finished it.
I went down to this hackathon give me a talk of the bidding
like hey you get MCP server. Everyone just figured one of the
many AI companies that existed people accepted us as one of
their own and it was actually pretty successful People didn't
use it much in hackathon honestly they kind of used API
instead but having having it meant that we were like a serve
a player in the scene. It was kind of like wearing the
right shirt or the right classes we should be knew what was going
on with MCP. That's how it all began, just
kind of as a spontaneous idea to fit into hackathon.
And so how did it progress? Because it sounded like you
learned a lot from being essentially 10 MCP hackathons in
four countries over a very short span of time.
I'm kind of curious A, what you learned B what you saw that was
different in the audiences, the, or even the tenor of how people
were approaching MCP, how it was different, you know, like what,
what did you learn? I.
Just thought it was really good topic and I just got really
interested in the topic myself. I mean, tech these days is
really fascinating. It's changing all the time and
AI is so interesting. I got really deep into actually
how it worked. I thought how could this
possibly happen? Like this is an LM.
It just like says stuff that talks to us.
We know really what it is. It takes in tokens as input, put
that tokens as output. How does it like use tools?
And none of the explanations I saw talked about how it actually
worked. They're all kind of similar and
talked about the USBC for AI and didn't talk about what actually
happened. I did a lot of thinking about
it, investigating, asking friends who are more deeply
involved than I was, and so I really understood it well.
And then I thought, well, I have to give a talk in June in New
York. I'll give a workshop at MCP
because I think it should be a thing everyone understands how
to do. So this would be a good reason
for me to figure out like in a couple weeks how to really works
well enough to actually teach. People had to use it, which is a
big challenge to actually teach the thing you don't really know
at all. But again, you try to go for
challenges in life and this makes us learn things.
So I realized it wasn't that hard to actually make a server.
Like you could actually make a server and seven lines of
Python, you could explain it actually pretty easily.
I found like the inspirations for it, like the tool former
paper from a couple years ago that talked about how to
actually just train the model with the data that wasn't just
data, but also these faux function calls that then could
be spat out by the model later on as actual function calls
intercepted and then uses actual function calls.
So I gave this workshop and it was surprisingly successful.
Like the room was packed. People had a lot of questions.
They actually listened. I think for the most part they
didn't ignore me as much as they might normally ignore me
afterwards. People were like, the next day I
built an MCP server, I did it, here it is.
So I realized. There was demand.
This is great. Then you're like, oh.
Yeah, people really were interested in the topic and it's
a nice entry point to learning about how to use AI because I
realized you could build a server pretty quickly.
I made a sample server that was just basically it told jokes.
So the the the simplest tool just returned the same joke
every time. So very easy to understand.
You know, just return the same string every time and the joke
the API found is very easy to use The API cube is necessary.
You can just call the API as a REST API.
So I made more tools to get various kinds of jokes and I
thought it was pretty easy to understand how it worked.
It didn't look like complicated code or anything.
Once you install a fast MZP or your favorite client library,
you can just kind of do it. So I made some sample servers
like this and I started using these and more and more
workshops because people always wanted to hear about MCP.
It was a good topic for talks. Obviously.
So like over the next 7-8, we like what changed, what
progressed is was there like, yeah, sort of a, you know,
things that at the end that were very different or.
I did a bunch of Google Dough Fest, actually, because I used
to work at Google. I had friends there, so they had
me come in October, actually. My, my, my, my indie rock band
played, opened the show. Then I gave MCP Talk after that,
which is kind of a funny combination.
Possibly what changed there were mostly it was that there were
cases of people who were more interested or less interested.
People needed to know about it more in some cases, less in some
cases. But I think the demand is still
as tied as it was before. I did my last workshop on Pure
MCP in February in Philadelphia, and it was for an audience of
the eye specialist who really understood the train models more
than I do. And they were so interested in
the talk and didn't know these things about how it worked.
And I mean, I think the main thing is that people don't quite
understand what it is that easily.
People think it's a way of mixing AI into your API in some
way. Like API is going to change what
it does and magically it's going to sort of do new things, which
of course isn't what it is. It's AI using AP is.
So I think they like the idea that you can actually get the AI
to act in a rational way, like use one of these tools over
here, but use API other kinds of ways.
Yes, restricted, you know, it's, it's discreet, it's not as
stochastic. But to your question, what
actually has changed since then? Not much actually has changed,
to be honest. That's cool.
So essentially the the like the protocol people are starting to
get in and there's the questions are better.
And the desire to learn is still as big as it was before.
The questions are kind of the same questions like So what?
Are like the top five questions that people get or the
misconceptions or the I mean, if you were going to redesign the
course from the ground up, what, what would you do differently?
I would like to show how it worked by actually showing an
example. Actually, it's not that hard to
do. You actually can go to Claude or
your LM and just tell us, OK, now you can use tools with this
protocol over here and put in some fake XML or Jason and it
will say, OK, I'll do that. I'll begin spitting out things
using those tools. You could then say, what's 5 * 8
use your new math tool and uses the math tool it already knows
about. And instead of playing out 40,
it says, you know, like angle brackets math, 5 * 8 close angle
brackets math. So now the LMS are good at
actually using tools and smart about it.
I think I tried to talk about how it actually works.
You know, it actually generates a prompt and the prompt is the
thing which gets in the system prompt.
And this explains how the tools work is why your contacts went
dull and get you, not because you have a lot of stuff in
there. I would explain how it worked
and what it is. People wouldn't be as surprised
by what it actually is. So I still get questions about
that, like how it actually works and questions that reflect what
really is going on. But more common questions I get
are about security. I was going to set.
So what do you tell them? I tell them be careful because
it's kind of like shareware. I mean, if you're downloading
someone else's code to your laptop and letting it run, then
be careful about that kind of thing on the design.
Side what are the because I mean you know the project has a long
security best practices doc. What do you intend to starve
with? And helping people.
There's a lot of questions you get that I can't answer that
well because they're about are there ways to actually like
inject prompts back into the LLM or you know, other things.
But I think part of it is just that if you're using an MCP to
use your API, it's good to not expose too much of it and it's
good to tell us if the API be very secure in the first place.
And then it comes down to API security.
You can use the same kinds of things, I think, to secure API
and secure API against LMS using your API.
And then, yeah, look for those best practices and
authorization. Make sure that people can't just
like easy spin up gigantic queries that will, you know,
slow down your your servers and things like that.
Honestly, I don't have the best advice for security.
I think it's still a thing people have to deal with
themselves and looked into themselves.
My best advice is still just make sure your stuff is secure,
you know, be careful about it. Make sure you do the usual
things, sanitizing, input, all those kinds of things usually
would do. Look out for authorization
problems, those kinds of things. And so as you kind of traveled
from one of these to the next, did you see differences in the
way people thought about it, you know, like the AI experts versus
the noobs or people that are sort of full stack guys?
Or, you know, what were the difference in the questions you
got from the different audiences?
I couldn't always tell, honestly.
That's a good question. I should be able to tell, but
it's just different places and different kinds of questions and
different kinds of levels of interest.
I gave one talk actually at a it was actually at a Business
School in Canada. It was another Google Dev Fest,
but the students weren't that interested.
The professionals were more interested.
This talk I gave, I think at all was API days in Paris.
The room was really full. People were outside the room and
they're really just curious about what this thing was, how
they could use it themselves. I think we've estimate how
people actually might know things because people that are
really involved in a technology really don't think everyone
knows what they're doing. But everyone else out there is
just kind of like, I saw this thing, I saw an article about
this thing somewhere, but what is this thing?
I don't quite know what it is. Unless you actually use it, you
don't really get what it is. So if you were going to try to
summarize like, you know, the the quick start guide in at all
5 minutes or whatever, what what, what are the principles
you'd go over? I would just show how to build a
really simple MCP server. And yeah, I talked about the the
principles first of like basically what it is.
It's a way you can get an LM to actually make these faux
function calls. And you then control the
function calls yourself. It calls your server.
Your server is working over here.
It's just basically functions. It can call your API or not, but
it's all it is, is basically you write functions of them.
MCP server can go ahead and call if it wants to and then show how
to build some simple ones. You know, simple simple things.
Once you get simple ideas down, the risk can follow from there.
But I found harder those MCP apps and other kinds of new
things, they don't always work for me quite yet.
And the harder did you explain like the protocol for MCP apps
is more complicated, MCP UI, this sort of thing.
Yeah, I have not had a simple way to actually explain how that
works or to even show how that works.
So were you involved with the design and construction at DPL
of the MCP server? I mean, did you kind of watch
the process? Yeah.
I kind of maintained myself now, so.
So, So what, what were the conventions around that?
Because I mean, it's kind of interesting that I mean, the
idea of a language server over MCP, yeah, you know, like sort
of why or what's different or I mean, how do you design it, you
know, so it's different than a straight up API or something
like that. Yeah, it's not that different.
I mean, it's mostly about trying to find the most commonly used
functions of the API and make them really easy to use, like a
translation texts, you know, sending texts, getting text back
and just trying to make sure you can look for errors like our
language codes used correctly. Did you require a country code
over here or not? And give helpful information to
the LM so it knows what to actually do.
And when I first did this, I remember it would have failed
because we use language codes incorrectly.
It would use lowercase and uppercase sometimes and things
like that. So it's not that forgiving yet,
but now it's much more forgiving because one, we made it more
forgiving by giving more information in the annotations,
annotations and so on. And two, LM service has gotten
smarter of what they try. So when you started, what were
what were you working on? I mean what was the LLM in the
back what you were piping? Into I usually use cloud because
cloud desktop was the most fun thing to demo in and it's kind
of make make conversation with you and it would try things out
and try to be helpful. But it's gotten much better at
this stuff. Like last time I gave him this,
I, I did the talk at Drupacon last week and I wrote an MCP
server that would look for translations existing in your
Drupal site. If there weren't any, it was
just making new ones for you and send those over for you.
And it began to do all this stuff by itself.
I gave it a couple of tools and it would, it would use DPL's MCP
server. It's already using my clawed
desktop and use my, when I made for Drupal.
And it was just all kinds of things.
Do you want me to translate this into Danish for you?
And here it is. And it also would comment on
translations because it also knows translations itself being,
you know, also clawed behind the scenes to comments on the
translations and other kinds of ways did.
You I mean, so essentially you had it almost like Co browsing
and like, Hey, look at this Drupal site and you know, and
support of the CMS and look at sort of all the meta text or
whatever and and tell me what you think it I mean, and then it
went on its own. It's like, Oh, by the way.
Yeah, I give access to look at the nodes and see what was in
the nodes, the article nodes, other kinds of those that we
just look at these things and start making suggestions.
But I didn't expect at all. I thought require a lot more
human intervention but really tries to be helpful.
And and that's like something that in the past weren't there
specialized tools that you would, you would, you know, use
for translation versus, you know, sort of generic, you know,
LLM. That was the idea exactly
because people use these things called cat tools.
Whether these large things are often very old looking
interfaces that are kind of like a scary looking version of Excel
where the send off stuff from machine translation has a first
draft, Then the translator will then modify them as they want to
and it can go and find with their text you've translated
before is present in new text and they can try to suggest more
things for you. So I thought if you're actually
a translator working for a Drupal site, you're not going to
want to go and probably walk in and like start typing things as
a Drupal because it could be a little confusing.
Maybe you're going to want to use your favorite LM and just
talk to it, you know, use claw or ChatGPT or something.
And it was surprisingly effective at that.
Like it was really pretty friendly.
So you almost discovered a new product category for deep L
without realizing what you're. Doing yeah, maybe we'll actually
make this into a product people could use.
I mean, I think in these MCP apps you could actually.
What it can't do now that what server I made like in a couple
of days is you, you can't really modify things yourself that
easily. You can, but it's not.
It's mostly read only. It's like, you know, it'll say,
OK, here's the translation for you.
Do you want to use this or not? You can type a new translation,
you can copy and paste it and change things.
It should be more like an interface, more like a cat to
where you have little boxes and here's the translations.
Do you like these things or not? Feel free to modify these
things. Send them all off as a batch to
the Drupal site. That'd be a better way to do it.
From my understanding to the audience is understanding how is
Deep L's translation layer differentiated or or you know
better than what you might get out of the frontier LMS right
now. Yeah, that's an interesting
question. Because Deep L first came out in
2017. They had the best translation
anywhere because they had their own models.
They trained to do these things with linguist help, and it's
better than Google Translate, better than anything else.
LMS came out with a pretty good translation, just kind of
out-of-the-box. So Deep L has now trying to stay
ahead by just taking models, building its own models, fine
tuning models, and just doing more and more testing with
linguists. More and more fine tuning and
studies and tests do show that Deep L is better for most
language pairs in most cases, often substantially better.
But it's more of a race now because LMS can do pretty good
translation. If you want very good
translation, you should use though, like a company like Deep
L that does it. You know, only this kind of
stuff. People often say they prefer it
and people will use it because it's just better.
You're translating stuff kind of casually for fun or just a few
strings on your site. You could probably use an ALM
out there and do it that way. So if anybody wants to use the
Dibel MTP server, they can basically integrate it into like
as a front layer in front of anything that they're doing.
Yeah, just stick it in there, try it out, and your LM will
know to use DIBEL usually instead of its own translation
these days, because it will know there's a tool for that.
They're kind of biased towards using tools as opposed to not
using tools. Yeah, and just gives you a
higher quality of translation. If you have things where your
translation quality really matters to you, You know it's
pretty cheap actually to DPL. So were people using, were
people actually building things, MCP apps or whatever with the
DPL MCP during this tour? I mean, they just start to see
people try to do translation stuff.
I. Can't tell if they're
coordinated or not. We've seen growth quite a bit in
our DPL and CV server usage is going growing.
It's still very small compared to the actual API, which is very
large, of course, but is growing very fast.
Even folks in that as you were building things with it and
they're making various things with it.
I think they just find it more convenient than using the API
sometimes because our tools are more carefully defined.
You know, like here's this translation text, here's
translating a document. And there are more more
guardrails around these things so that I think it's just easier
to use. So like essentially the
flexibility of MCP was something that as you were traveling
around people were it's just much easier for or people that
don't want to have to think in terms of the structures of an
API or. API is the reverse because API
may be a whole new thing, like what is this API?
How do I use this thing? MCP?
I think it's just sort of simpler.
Here's the tool you know. You give this tool these two or
three parameters and then it gives you the results you're
looking for. Very cool.
So when's the next tour? That's a good question.
The last talk was just last week, but actually trying to
focus on making some scale content like writing more of the
traditional devrel things like more documentation, make some
videos, things like that. So I'm taking a little break
from traveling out to do those things and I want to go back out
there and talk again with you. More magic AI and more of those
sorts of topics that are also of course hot topics, you know,
building agents, which is super hot as you know.
But MCP, I mean, people still don't know what it is in most
cases. Outside of in New York, San
Francisco. Or people that are just
following these things and who are working with it and try it
themselves. But people are still like, what
is this thing outside the demand bubble?
We always forget the demand is still there for just, you know,
introductory content. It's always there for
everything. So the next time you do a tour,
what? What are you going to do
differently when you design your MCP curriculum?
I'm going to try to make MCP app this time to see if I can get
all the way to making apps and using the new fancier features
and seeing how that goes. It's a good question.
I feel like what I had did before, how can I improve it?
I feel like actually if I could get people to do more coding
themselves, that'd be nice, because when they learn things
for real, I'd have workshops. People often would come in
without a computer and wanted to listen to a talk.
Right. So they didn't do hands on as
much actually. I first did.
I thought I'd do a workshop and people just came and they were
looking at me like, OK, talk to us.
They made it into a talk spontaneously and they did it
later in some cases, try it themselves.
I think it's probably better. So I think if I was going to do
it again, if I do do it again, I'll try to get folks to
actually really write some code and know that you can use
Python, you can use TypeScript, you can use your favorite
language. You can do it pretty quickly.
As long as you've got something installed on your computer,
which is an LLM, which is an AI client, which maybe you build
people don't haven't got yet. If they have an AI client, they
can do it. You can install your library,
you can just go and do it. And it feels, it feels like
magic actually, an LMS are actually like using your tool
and you see it doing that and then responding with your
information you've given it is really kind of cool.
I, I mean, I, I personally, I'm not a coder.
I've been, well, I'm a dangerous coder, right?
Like I could code stuff you would not.
If one's a coder. You would not want me to put it
in prod, but yeah, it's just blowing me away.
How you know, you can essentially go into Claude code,
give give it a spec. It'll recommend the tool.
Or if there's MCP servers out there that'll make sense, it'll
recommend them. You know, it's a, it's, it's,
it's really, really different. Yeah, watching like you get like
Claude or Taji and they actually use your tools in the call to
three different tools. Like I could do a thing, I could
give a demo, which is like, OK, I'm going to have it use the
translation tool now. I'm going to ask it to translate
this thing, like find a joke and then translate the joke.
We'll call 2 tools. People are on the edge of their
seats. They're going to have actually
like tell, tell a joke, translate it into French and put
it into a spreadsheet. Three tools he watches Claude
spins. That actually usually does it
right. So it's just, it's exciting,
it's oddly suspenseful and gratifying because.
Usually it works. Thank you very much for coming
out on the. Podcast.
Thank you. All the audience one last time,
you know, like who you are and what you do again in case you're
catching a tail end. In case you forgot or missed the
beginning because whatever reason you're doing something
else, I'm Ben Morris. I'm from DPL Developer Advocate
and we're talking about MCP servers, how they work and
Deepel is a language translation company that uses AI makes on
models to make language translation as good as possible.
Thanks for coming. Thanks for having.
Me enjoy MCP dev solar. I will, thanks.