Imagine a world where building really complex
business automations, well, it only takes a single
sentence. You just type what you want, and minutes
later, a robust system simply, well, it appears,
ready to go. No deep coding, no endless debugging.
Sounds almost too good to be true, doesn't it?
Let's unpack this a bit. Welcome back to the
Deep Dive. Today, we're plunging into something
truly fascinating. a revolutionary new method
that lets AI instantly build powerful multi -step
automations for you. We're talking about combining
advanced AI, specifically tools like Claude,
with something really cutting edge called the
Model Context Protocol or MCP. you know, another
update. It feels like a seismic shift, a real
game changer in the automation space. We're going
to kick things off by exploring why current automation
tools, powerful as they are, can often feel like
a bit of a paradox, like incredibly capable yet
strangely exhausting to really master. Then we'll
peel back the layers, look at how this MCP method
actually works. We'll peek under the hood at
its impressive three -repository power system.
We'll even walk you through a really compelling
real -world example, show you its practical magic.
Later, we'll sketch out how you could conceptually
set this up yourself, explore its huge business
applications, and then, importantly, we'll get
real about its current limitations and the exciting
possibilities ahead. Our mission, like always,
is to give you that shortcut, help you get truly
well -informed fast. Okay, let's start by digging
into the problem this whole new approach is trying
to solve. Our source material, it kicks off describing
NEN as incredibly powerful. They even call it
Zapier on steroids, which highlights its immense
flexibility, hundreds of integrations, advanced
logic. It really sounds like an automation dream
tool on paper. And it absolutely is. I mean,
it's a beast for power users, genuinely deserving
all that hype. But here's the, well, the stark
reality, the paradox, right? It's greatest strength,
that incredible flexibility, the sheer number
of options that also creates its biggest challenge.
Navigating hundreds of different nodes, you know,
those little building blocks, configuring them
perfectly, getting them to work together, especially
in a complicated workflow. It can be genuinely
exhausting. You find yourself thinking, maybe
just writing the code myself would actually be
quicker. It's kind of like being given every
single Lego brick imaginable, but zero instructions
and then being told, go build a skyscraper. So
it sounds like for all that potential, the real
core struggle for users is just mastering that
overwhelming complexity. It's a lot. Precisely.
It's the sheer weight of learning. You know,
that steep climb to really get it. And this brings
us to where things get really interesting. Our
source introduces the Model Context Protocol,
MCP, as the key. The answer to bypassing that
steep learning curve entirely. It promises to
sort of transform the whole experience. That's
the beauty of it, exactly. Yeah. Think of MCP
not just as another feature, but as a really
sophisticated system. It gives an AI like Cod
this deep, structured, almost native understanding
of a complex tool like any N. The old way, AI
would often give you, well, inconsistent results,
sometimes illogical ones, like it was just guessing
or making vague connections. But this new MCP
method, it completely changes that. It equips
the AI with these highly specialized tools and
gives it access to massive, meticulously structured
libraries of information. So the AI doesn't guess
anymore. It builds, builds with the confidence
and the precision of a seasoned expert. OK, that
level of precision, that expert like knowledge
sounds incredible. And you said it comes from
what the source calls the three repository power
system. Can you walk us through those? What are
they? How do they work together? Absolutely.
Imagine giving the A .I. not just one brain,
but like three distinct, powerful yet. perfectly
interconnected intelligence centers. First, you've
got the NAN MCP server repository. This is like
the AI's core blueprint library. It contains
complete structure documentation for over 525
different NAN nodes. So the AI knows how each
building block should be set up, how it's used
correctly, down to every little parameter. It's
the ultimate instruction manual, basically. A
complete guide for every single component. Wow.
Exactly. Then second, there's the Context 7 repository.
This acts like the AI's live fact checker. It's
dynamic memory, if you will. Software documentation
changes all the time, right? This repository
makes sure the AI always has the most up -to
-date version -specific documentation. And this
is crucial because it actively prevents errors
you might get from using outdated info. Keeps
the AI's knowledge fresh, relevant. So it's constantly
checking its understanding against the latest
information out there. Precisely. And third,
and this is maybe the most powerful, most fascinating
part, is the workflow reference repository. This
is like a massive case study archive, a treasure
trove of practical wisdom. It holds over 2000
proven real world MEN workflows. So when you
ask for an automation, the AI can actually look
through these and find similar battle tested
solutions to use as a starting point. Moment
of wonder, just whoa. Pause for a second and
think about that. Over 2 ,000 successful automations,
all documented, all available. It's like having
the collective wisdom, the direct experience
of thousands of expert builders right at your
fingertips. ready to instantly apply it's truly
astounding the kind of capability that gives
it it sounds like having yeah a whole team of
master builders on call this huge amount of knowledge
fundamentally changes how the ai approaches a
task it moves it way beyond just guesswork it
absolutely does the ai now builds with real deep
expert knowledge not just you know speculation
that's a really powerful foundation so okay with
this mcp system in place How does a simple request,
like in natural language from a user, actually
turn into a fully functional, production -ready
workflow? What are the steps? It's actually a
pretty elegant four -step dance, almost seamless
in how quickly it happens. First, you provide
a simple description. It all starts with you.
You give the AI, maybe in Cloud or a code editor
like Cursor, just a natural language description
of what you need the automation to do. Clear,
direct. Second. The AI performs intelligent research.
Now this is where the magic really starts humming.
The AI doesn't just start building blindly, it
does its homework. Real homework. It intelligently
accesses those three repositories we just talked
about, grabs the relevant up -to -date docs from
the Blueprint and the Fact Checker, and crucially,
searches that massive library of 2 ,000 tested
workflows to find good examples. This deep research
helps it find the perfect nodes, the most efficient
structure for what you asked for. It's like it's
reviewing every relevant textbook and case study
before drawing the first line. Third. The AI
builds and validates the workflow. Armed with
all this expert knowledge, it then constructs
the workflow's JSON structure. Beat. JSON structure,
for everyone not familiar, that's just a standard
text -based format for representing data. Super
common online. And every decision it makes is
based on that validated documentation. It uses
built -in tools to pre -validate configurations,
catching potential errors before deployment,
like a self -correcting blueprint. Fourth. The
AI deploys and manages the workflow. Once it's
built and validated, the AI uses its tools to
upload it directly into your AnnieNan workspace.
It does a final check to make sure everything's
integrated correctly, ready to run. That automated
validation part sounds like a huge differentiator.
What's the real advantage there for someone using
this, knowing that Step is built in? The real
advantage is catching errors proactively. It
means you deploy with way less risk, ensuring
flawless operation right from the get -go, less
debugging later. Right. To really make this concrete,
the source gives a great real world example,
a digital marketing agency wanting to automate
its client onboarding. Could you walk us through
that, the prompt and what the AI actually built?
Yeah, absolutely. It's a good one. The prompt
they gave was pretty straightforward. Something
like automate our client onboarding process using
HubSpot, Asana, Google Workspace and Slack. And
the AI using this MCP system took that one sentence.
and did an astonishing amount of work automatically.
It used all three documentation sources to understand
not just the individual HubSpot, Asana, Google
Docs, Slack nodes, but also how they typically
interact in these kinds of flows. Then it meticulously
mapped out this logical multi -step workflow,
triggered exactly when a HubSpot deal was marked
closed one. It found the specific nodes needed,
checked they were compatible, and then created
the whole complex multi -step workflow. Okay,
and the result? The outcome was pretty incredible,
actually. A fully functional multi -agent workflow
literally just appeared in the user's Anet in
account. This thing automatically created an
Asana project from a template, generated a Google
Drive client folder and a personalized welcome
packet, set up the initial tasks in Asana for
the team, notified the right Slack channels about
the new client, and even updated the HubSpot
client record with the new project details. The
only manual part. the human touch, was connecting
the API credentials securely. An API credential,
just simply put, it's like a secure digital key.
Let's one program talk to another safely. So
this really complex multi -platform setup just
materialized in there in an account almost instantly.
That's quite a leap forward. Nearly instantly,
yeah. With astonishingly minimal human effort.
beyond that first prompt and plugging in the
keys. It really redefines plug and play for these
kinds of complex systems. Midroll sponsor Reed
provided separately. This sounds genuinely transformative,
really powerful. For listeners who might be curious
now, thinking about the next step, how does someone
even begin to set up a system like this conceptually?
We're not doing a full tutorial here, obviously,
but what are the key phases involved? Good question.
It's a natural next thought, right? The source
breaks it down pretty cleanly into two distinct
phases. First, there's phase one, the basic MCP
setup. This basically gives your AI, like Claude,
read -only access to all that knowledge. You
essentially configure Claude by feeding it a
single unified JSON file. Beat a JSON file. Again,
just a common organized text format for data.
like a structured recipe. This file is the instruction
set. It tells Claude exactly where to find those
three repositories we talked about, the N18 node
docs, the live fact checker, and that treasure
trove of real -world examples. Once that's done,
Claude gets incredibly accurate at designing
workflows. It'll give you the complete JSON code,
and you just manually import that into your N18
setup. Okay, so it gives you the perfect blueprint,
and you just drop it in yourself. Makes sense.
Exactly. Then you move to phase two, advanced
integration. This is where you unlock the direct
NA10 deployment, meaning the AI can actually
push the workflows straight into your system
for you. This phase involves using Docker. Docker
is a fantastic tool. packages applications in
these isolated environments, like little self
-contained computers. You use Docker to run a
local server that acts as a secure bridge between
Cloud and your NNN account. You pull a specific
Docker image, run it, then update Cloud's config
with your NNN URL and a unique API key that you
generate inside your NNN settings. This gives
the AI the necessary secure permission to connect
and actually modify your NNN workspace. Beat.
I'll admit, I still occasionally wrestle with
making sure all the correct API keys and permissions
are perfectly in place myself. It's, you know,
a critical security step, obviously, but sometimes
it can trip you up. A misplaced character, an
expired token. It happens. Yeah, it sounds like
for anyone looking to actually get this running,
aligning all those security credentials correctly
is maybe the main hurdle. It absolutely is. Getting
all the security credentials lined up perfectly
is often the trickiest part of the whole setup.
Okay, now that we have a clearer picture of the
how, let's pivot back to the what. Where can
this incredibly powerful automation building
be applied in the real business world? Seems
like the possibilities are pretty broad. They
truly are. This tech isn't just for one industry.
Its power is in automating any structured, repeatable
process. Think about a law firm, right? Imagine
AI -driven client intake. New client data triggers
drafting engagement letters, sets up tasks in
Asana or Clio, starts automated communication.
Or for a marketing agency, beyond that onboarding
example, it could automatically create dedicated
Slack channels for new clients, send out detailed
onboarding questionnaires, or even pull daily
PPC performance data from Google Ads, format
it into a neat summary, and post it to a team
dashboard or Slack. All before anyone's had their
first coffee. It's really about leveraging this
intelligence wherever there's a predictable flow
of info or tasks. So it's about identifying those
routine high -volume tasks and letting the AI
handle the heavy lifting. Makes sense. And to
get the absolute best out of it, what are some
of the pro tips the source suggests? Yeah, the
source offers some really good practical advice
for getting maximum results. First, be extremely
specific in your prompts. The AI is brilliant,
but it's very literal. So instead of something
vague like build me an automation for new clients,
you need crystal clear instructions. Try something
like create a workflow. Trigger. HubSpot deal
stage changes to close one. Action one. Create
new Asana project using new client onboarding
template. Action two. Generate Google Doc welcome
packet from template. Merge client name. Action
three. Send Slack notification to hashtag new
clients with deal details. See, more details
mean less guessing for the AI. Much better results
for you. Much clearer. Second, use a project
template in Claude. Don't start from a blank
chat every time. Create a persistent project
with some permanent instructions for the AI.
Like, you are an expert in ADAN automation architect.
Always start by analyzing the trigger. Gives
it a consistent starting point. Also, monitor
usage carefully. This kind of powerful AI generation
can eat up credits on platforms like Claude.
So plan your query strategically. Maybe bundle
smaller requests into one big one. Another crucial
tip, plan your automations. Sketch it out on
paper or in your head before you start writing
the prompt. Clarifies your thinking. Good advice.
And related to that, capitalize on each query.
Try to get as much done as possible in one detailed
prompt instead of lots of back and forth for
every little step. Finally, and this is key,
start simple, then scale up. Don't try building
a massive 20 -step system first time out. Start
basic. Two, three steps. New Google Sheet row.
Send Slack message. Get comfy. Then build complexity.
That's excellent practical advice. If you had
to pick just one, what's the single most important
tip for success with this? Oh, without a doubt,
specificity in your prompts. Every single time.
Clarity is king. Right. It sounds almost magical,
honestly, but let's ground this with a reality
check. What parts does this system handle incredibly
well and what still absolutely needs that human
touch? Yeah, that's an important distinction.
It's amazing at generating the complete workflow
JSON, selecting the right nodes, building complex
logic, validating it all. That's huge. But what
still needs you? First, API credential setup.
You will always... Always manually add your API
keys for security. The AI won't handle those
sensitive keys, nor should it. Second, custom
business logic. If you have really specific,
intricate formulas, unique decision trees, very
nuanced if -then stuff, you might still need
to manually tweak the code or IF nodes after
the AI generates the main structure. And always,
final testing. You should always, without fail,
do an end -to -end test before activating any
workflow for real use. Think of the AI as your
master architect, but you're still the final
inspector, the QA engineer. Cost is also, you
know, a factor to keep in mind. Generating really
complex workflows can be credit intensive on
Claude, and its usage credits often reset every
four, eight hours, even on pro plans. So planning
your big queries around that cycle can be smart.
And, you know, while we focus on Claude here,
the MCP method isn't exclusive to it. You can
adapt it, use other AI dev environments like
Cursor, Windsurf, even LM Studio if you want
to run things locally for privacy. So it's incredibly
powerful, automates so much, but it's not quite
set it and forget it just yet. Human oversight
is still critical. Precisely. Human oversight
remains key for security, for that final fine
tuning and for ultimate peace of mind. OK, beyond
just making existing things easier, what new
business opportunities does this tech really
create? How does it reshape the landscape for
entrepreneurs or existing businesses? Yeah, this
isn't just about small efficiency gains. It's
about unlocking entirely new competitive edges,
new business models even. Imagine automation
consultancy. You can now offer rapid prototyping,
build custom workflows at a speed and cost that
was just impossible before. Delivering high quality
validated automations in a fraction. of the traditional
dev time. That's a massive differentiator. Huge.
Or think about a workflow template marketplace.
You could build and sell industry -specific templates
like a new real estate client onboarding package
or a healthcare patient intake workflow, all
pre -built with AI help ready to plug in. And
of course, training and education. As this gets
more mainstream, there's going to be massive
demand for people who can actually teach others
how to use it effectively. Makes sense. This
really is a fundamental shift in how complex
automations get built. The old way. Long, manual,
often expensive, a real bottleneck. The new MCP
method. It flits that script. Now, a business
expert can just describe their desired outcome,
you know, in plain English. And the AI acts like
the master developer. It researches, generates,
validates, deploys a production -ready workflow
in minutes, not days or weeks. Minutes. That's
a huge shift. So how does this fundamentally
change the actual job of an automation developer
or a solutions architect? It transforms it. Less
manual building, much more strategic prompting,
design, and oversight. Less hands -on keyboard
time clicking nodes, more high -level thinking
about the best way to instruct the AI. Interesting.
So to bring it all together then, the model context
protocol, when you combine it with powerful,
knowledgeable AIs like Claude, it's radically
simplifying how we create these really complex
automations in platforms like NENN. It's truly
democratizing what used to be a very specialized
skill. Yeah, that's exactly it. It's all about
making powerful automation accessible to everyone.
For beginners, it's a direct shortcut to building
professional -level automations that were just
out of reach before. For the experts, it turns
into this massive productivity multiplier, turns
hours of tedious work into, well, minutes. And
for agencies, for businesses, it's a profound
new competitive advantage, lets them deliver
higher -quality, more robust results way faster
than ever before. Okay. Feeling inspired to maybe
explore this transformative power for yourself,
our source lays out a pretty clear, actionable
plan to get started. First, they suggest joining
an online community focused on AI automation.
These places are invaluable for learning, support,
sharing tips. Second, start simple. Get that
basic read -only MCP configuration set up first.
Then challenge yourself. Generate a really simple
one -charger, one -action workflow. Just build
that initial confidence. Third, scale gradually.
Once you're comfortable, move to multi -step
automations. Experiment with more complex logic,
different nodes. And finally, document and share.
Keep track of what works, what doesn't. Share
your successful prompts, your solutions with
others in those communities. The barrier to building
really powerful business transforming automations,
it has just dropped to nearly zero. The question
isn't if you should learn this method, it's what
will you build first? Think about the possibilities.
Really open things up. Thanks for diving deep
with us today. We'll catch you on the next deep
dive. Outero music fades in, then fades out.