Have you ever stopped to think, like, what really
makes an AI assistant powerful? Not just, you
know, a chat bot that can talk back. Yeah, it's
a great question. It's more than just smart conversation,
isn't it? Exactly. That key thing, the real game
changer, it seems to be its ability to actually
connect out to your other stuff. your tools,
your data. Right, like hooking into your calendar
or maybe digging into your company database,
even checking your emails or using your CRM.
That's where the power lies. It's that jump from
just understanding words to understand your whole
business setup. Precisely. And the tech that
makes this possible and crucially makes it scalable
for almost anyone is this open source standard
called Model Context Protocol, MCP for short.
And MCP is exactly what we're diving deep into
today. We want to walk you through everything
you need to know, what it is, why it matters,
and how you can actually use it. Yeah, the goal
is really to show you how to take your AI from
just talking to actually being a core part of
your business systems. OK, so. To really get
why MCP is such a big deal, maybe let's think
about what it was like before MCP. Good idea.
Imagine trying to manage a big international
team where everyone speaks a totally different
language. That's kind of what connecting AI to
tools used to be like. Right. So if your AI needed
data from, say, Salesforce, it had to learn Salesforce
speak. Exactly. And then if it needed something
from Google Analytics, OK, now it needs to learn
that specific language too. which meant custom
coding for every single connection, like hiring
a separate human translator for every single
app. Yeah. And you can see pretty quickly how
that just doesn't scale, right? What if you add
a new tool or you want to switch the AI model
you're using? You're back to square one, rebuilding
integrations. Just managing that web of connections
sounds frankly exhausting. It was a real efficiency
tiller. And this is precisely where MCP comes
in. Think of it as the the universal translator.
OK. So the AI doesn't need to learn 10 different
tool languages anymore. Nope. It just needs to
learn one. The MCP language. And when tool providers,
the Salesforce's notions of the world, adopt
MCP, they all start speaking that same standard
language. Which gets rid of all that constant
custom integration work on your end. Exactly.
And here's the really cool part for you, the
user. Your AI apps become model agnostic. Okay.
What does that mean in practice? It means you
can switch between AI models. Maybe you start
with chat GPT, then try Claude, or maybe some
new AI comes along. You can swap them without
having to rebuild all those connections to your
tools. Wow. Okay. So you're not locked into one
AI vendor just because you built integrations
for it. Precisely. It makes adopting AI much
more flexible. And that's why MCP is becoming
like really critical infrastructure. It's fundamentally
changing what AI can do within a business. Makes
sense. So let's break down. MCP itself, what
are the main pieces we need to understand? Okay,
so fundamentally, there are two core components.
First, you've got the MCP servers. Servers, okay,
what are those? Think of these as your actual
business tools, the database, the CRM, the analytics
platform. They're the ones providing the capabilities
or the data. Got it, the sources. And the second
part. That's the MCP clients. These are your
AI applications. Cloud, Chat, GPT, maybe a custom
system you've built. They're the ones using the
tools or consuming the data. And MCP is the bridge
connecting them. Exactly. It handles all that
translation work. So when your AI client says,
hey, CRM, give me last quarter sales figures,
MCP makes sure the CRM understands the request
and sends back the data in a way the AI understands.
It's that communication layer. OK. And you mentioned
servers. How do those actually run? Are they
complicated to set up? Well, you've got options,
which is nice. There are remote servers, which
are usually cloud -based. These tend to be easier,
right? Often it's just pasting a link somewhere.
They're managed by someone else. Easier setup,
less maintenance for me. OK, what's the other
option? Local servers. These run right on your
own machine or within your company's network.
Ah, so more control, maybe more security. Exactly.
Your data doesn't leave your environment. But
yeah, it does usually require a bit more technical
setup. So it's a trade -off between convenience
and control, really. OK. That clarifies the basic
structure. So now, how do we actually make that
connection between the AI client and the MCP
server? You said there are different ways. Yeah,
there are basically four main approaches, and
choosing the right one depends on things like,
you know, how easy you want it to be, your security
needs, how flexible you need the connection to
be. All right, let's walk through them. What's
the first, maybe the easiest? That would be native
integration. This is usually built right into
the big AI platforms like ChatJepiT or Claude.
Ah, so the AI platform itself already has connections
ready to go for certain tools. Exactly. Things
like Gmail. Google Calendar, Google Drive are
common ones. It's definitely the simplest for
non -technical folks. Zero setup, really, beyond
just logging in and giving permission. And it's
low risk because it's officially supported. OK,
sounds great. Any downsides? Well... Sometimes
the functionality can be a bit limited. For instance,
ChatGPT's native connectors right now are mainly
for what they call deep research, meaning it's
great at pulling information like summarizing
emails or finding docs, but maybe not so much
at doing things like sending an email or creating
an event, at least not yet. It's mostly read
-only for now. Gotcha. Still useful, but limited
scope. Okay, what's about the number two? Number
two is using official MCP servers. This is what
we generally recommend if native integration
isn't available for the tool you need. Official
servers. So these are built by the tool companies
themselves, like Notion or HubSpot. Precisely.
Major providers are building and maintaining
their own MCP servers. You can usually find a
list on the official MCP GitHub page. These can
often be installed either locally or used remotely.
And the big advantage here is... Reliability
and security. Because they're built by the original
company, you know they're likely to be more secure,
better maintained, and kept up to date with the
tool itself. It's the most trustworthy option
after native integration. Okay, makes sense.
Official is usually better. What if there's no
native or official server for a tool I use? Ah,
that brings us to option three. Community -built
servers. Community -built, so made by other users
or developers. Exactly. These can be great for
niche tools or things that don't have official
support yet, maybe something like Airtable. But,
and this is important, you need to be cautious.
Right, cautious. Well... Because they're not
maintained by the original company, the risk
is higher. There could be potential security
issues, maybe data leaks. You really need to
check who built it and how well it's maintained.
It's not necessarily bad, just requires more
due diligence. Right, proceed with caution. Okay,
and the last method. The fourth way is building
custom MCP servers. This is definitely the advanced
option. For specific, maybe unique business needs.
or connecting to internal proprietary systems.
Exactly that. Or if you need very specific functions
that aren't covered elsewhere, this usually requires
some development skills, maybe using Python or
JavaScript SDKs. Oh, okay. Sounds complex. It
can be, but there are also no code platforms
like N8n that can help you build these custom
servers without writing actual code, which makes
it a bit more accessible. Interesting. So...
Quick recap, native is easiest, official is recommended
for reliability, community needs caution, and
custom is for specific advanced needs. You got
it. And a key piece of advice, really a pro tip,
always start with native if it does what you
need. If not, look for an official server. Only
go down the community or custom routes if you
absolutely have to. Think crucially. Crucially,
whatever you connect, always grant the absolute
minimum permissions necessary. If the AI only
needs to read data, don't give it permission
to write or delete. Start with least privilege.
Always. Absolutely critical for security. Okay,
let's make this more concrete. Can we walk through
the idea of setting one of these up? Not a full
tutorial, but the principle? Sure. Let's take
that native integration with ChatGPT first. It's
designed to be simple. You'd typically go into
your ChatGPT settings, find a section like connectors
or integrations. And you'd see things like Gmail,
Calendar there? Yeah. you'd click the one you
want, say Gmail. It'll likely prompt you to log
in to your Google account and then ask you to
approve certain permissions like allow chat GPT
to read your emails. Okay, pretty standard permission
stuff. And then what? Does Gmail show up like
a plugin? Not usually, no. Instead, when you
use a specific function within ChatGPT, maybe
one called deep research, you'll see an option
to toggle on your connected Gmail account for
that specific query. Oh, so you enable it when
you need it for a task. Exactly. So you could
then ask something like, can you find all the
newsletters about AI I received in the last six
months and summarize them? And ChatGPT would
use that Gmail connection to do the research.
That's powerful. Turns it into a research assistant
for your own info. Definitely. especially for
companies with lots of internal documents or
emails. OK, what about setting up an official
server? You mentioned that might involve a bit
more configuration, maybe with something like
Cloud Desktop. Yeah, it's often a multi -step
process, but logical. Let's use Notion as an
example. First, you'd go into your Notion settings
and create a new integration or service connection.
You tell Notion you want to connect something
to it. Right. You'd give it a name, configure
what it can do again, minimum permissions, maybe
only allow it to read specific pages or databases,
not your whole Notion workspace. Okay, lock it
down. Then Notion will generate a secret key,
like an API token. You copy that token. Got the
key, now what? Now you go to your AI client,
say Claw Desktop. There's usually a developer
or settings area where you can edit a configuration
file. It sounds technical, but it's often just
text. And you paste the Notion token in there?
Pretty much. You'd find the section for MeICP
servers, paste in some configuration details
provided by Notion's MCP server instructions,
and replace a placeholder with your unique API
token. Then save the file, maybe restart Claude.
And if you did it right... Notion should appear
as a tool Claude can now use directly. You could
then ask Claude, draft a social media plan for
the new product launch using the brand guidelines
document in our Notion. And it would access that
specific Notion page and use the guidelines.
That's the idea. It seamlessly pulls your company's
knowledge into the AI's workflow. It's incredibly
useful for keeping things consistent. I can see
that. Okay, one more example. The custom route.
Maybe using that NAN platform you mentioned.
Let's say I want to connect to Google Analytics
data. Right. So NAN is a workflow automation
tool. You'd start by creating a new workflow
in NAN. The trigger for this workflow would be
MCP server. So the workflow starts when the AI
asks for something via MCP. Exactly. Then within
the workflow, you'd add a node for Google Analytics.
You connect NAN to your Google Analytics account
and configure exactly what data you want the
AI to be able to access. Like maybe website traffic
from the last 30 days broken down by source?
Precisely. You set the parameters, date ranges,
metrics, dimensions. Once you've built this workflow
and activated it, the NAN and MCP server node
gives you a special URL, a production URL. And
that URL is what I give to the AI. Yes. You'd
go into your AI client, maybe the Cloud web app
this time, find the setting for add custom integration
or similar, and paste that NAN URL in. Save it.
And suddenly, my AI can talk to my specific Google
Analytics setup via an 8M. Bingo. You could then
ask your AI, generate a quick dashboard showing
website traffic trends from Google Analytics
for the past month, and Claude, through N8AN,
fetches that specific data and presents it. Wow.
That really opens things up. Connecting to almost
anything with an API becomes possible. It really
does. It bridges the gap for tools without official
support or for very custom data needs. This is
great. So with all these ways to connect, which
AI platforms are actually good at using MCP right
now? Is the support widespread? It's evolving
quickly. But there are definitely leaders. Claude,
I'd say, currently has the most comprehensive
support. It handles local and remote servers
well. It can do both read and write operations
in many cases. And they seem really focused on
expanding custom integration support. It feels
pretty baked into the experience. OK. Claude
is strong. What about ChatGPT? ChatGPT is definitely
making progress. They have those native connectors
for popular tools, which is great for ease of
use. But as we mentioned, it's still often limited
to that deep research read -only mode. So more
about getting info out than putting info in or
taking action. Generally, yes, for the native
ones right now. However, they are rolling out
support for custom MCP servers, especially on
their paid plans, like Pro and Teams. So that
capability is growing. OK, good to know. And
Gemini, Google's AI. With Gemini, MCP support
seems to be mainly available if you're using
the Gemini API directly, like through their SDKs
for developers. Ah, so not really a simple click
and connect thing on the main Gemini website
or app yet? Not typically, no. It requires more
technical implementation at the moment. Got it.
Any others worth mentioning? Yeah, you're seeing
other platforms pop up too. Tools like Windsurf,
Cursor. They're also integrating MCP support.
It's definitely becoming a feature that more
and more platforms are recognizing as essential.
The ecosystem is growing fast. That's encouraging.
OK, we touched on this earlier, but let's really
hammer it home. Security. When you're connecting
your AI to all your sensitive business data,
security has to be front and center, right? Absolutely
non -negotiable. It has to be priority number
one. So what are the key security best practices
people absolutely most follow? First, as we said,
minimum necessary permissions. Always start there.
Don't grant delete access if read is all that's
needed. And don't just set it and forget it regularly.
Review and audit the permissions you've granted.
Makes sense. What else? Choose reliable sources.
Strongly prioritize those official MCP servers
from the tool vendors themselves over community
builds whenever possible. They're just inherently
more trustworthy and better maintained. Okay,
stick to official channels if you can't. Also,
really understand the data access. Know exactly
what data each connected tool or MCP server can
see. Don't make assumptions. Read the documentation.
And finally, regular maintenance. Keep any local
MCP servers updated, patch them, and periodically
check your integration settings everywhere. Good
hygiene, basically. Keep things clean and updated.
Now, on the flip side, what are the common traps,
the mistakes people make that we should actively
avoid? The biggest one, hands down, is over -granting
permissions. It's so easy to just click allow
all for convenience, but that's a huge risk.
Be granular from the start. resist the urge to
give it the keys to the kingdom. Exactly. Another
common mistake is ignoring official servers.
People sometimes default to building a custom
solution or using a community one out of habit.
Even when a perfectly good secure official server
exists, always check for an official one first.
Don't reinvent the wheel if you don't have to,
especially if the official wheel is safer. Right.
And then there's the issue of cramming too many
tools onto one connection. What do you mean?
Trying to connect, say, seven different types
of tools through a single custom MCP server setup.
It sounds efficient, but it can actually confuse
the AI. If you ask it to summarize recent activity,
it might not know if you mean emails or CRM updates
or project tasks because it has too many options
linked through that one channel. It dilutes the
AI's focus. We generally advise sticking to maybe
five or fewer distinct tool times per server
connection for clarity. Interesting. So give
the AI focused access, not just a massive jumble
of everything. Precisely. And the last big mistake,
skipping the documentation. Seriously. People
run into problems because they miss one small
configuration detail mentioned in the setup guide.
Read it carefully. It can save you major headaches,
especially security ones. OK, excellent advice.
So we've covered the tech, the setup, the security.
Let's talk impact. Where are we seeing MCP really
make a difference in the real world? How are
teams using this today? Oh, the use cases are
fantastic. Take marketing teams, for example.
Yeah, what are they doing? Think about automated
reporting. Instead of someone manually pulling
data from Google Analytics, then HubSpot, then
maybe Facebook ads, the AI can connect to all
three via MCP. It automatically generates a consolidated
weekly performance report, maybe even pulls out
key insights, and emails a summary. Wow, that
saves a ton of manual work. Huge amounts. It
frees up marketers to actually analyze and strategize,
not just compile numbers. Another big one is
content creation. The AI can access brand guidelines
stored in Notion or Google Drive. So it can write
stuff that's actually on brand. Exactly. generate
content briefs, draft social media posts, write
initial email copy, all adhering to the company's
specific voice, style, and messaging guidelines
because it can read them directly. OK, that's
powerful for consistency. What about sales teams?
For sales, lead research becomes turbocharged.
An AI connected to your CRM, like Salesforce
and maybe LinkedIn via an MCP connection, can
automatically gather info on prospects. So what
does the background check for the sales rep?
Yeah, it summarizes key info, identifies potential
talking points, maybe even suggests personalized
outreach angles based on the prospect's activity
or company news. It makes outreach much more
effective. And related to that is email automation.
Going beyond just mail merge. Way beyond. Using
data from the CRM, the AI can generate highly
personalized outreach emails. Not just inserting
a name, but referencing specific interactions,
needs, or interests pulled directly from the
customer record. It's personalization at scale.
I can see how that would improve response rates
and customer support. Huge potential there too,
mainly through quick information retrieval. Imagine
an AI connected to your help desk system, like
Zendesk, and your internal knowledge base. So
when an agent gets a query... The AI instantly
pulls up that customer's history, details about
the products they use, and relevant troubleshooting
articles or solutions from the knowledge base.
All right there for the agent. Faster resolution,
happier customers, more efficient agents. Exactly.
It provides the agent with instant context and
answers, dramatically improving the support experience
for everyone. These examples really bring it
to life. It's clear MCP is already enabling some
amazing things. What's next? Where's this technology
headed? Oh, it's moving incredibly fast. We're
seeing platform support just exploding, more
AI clients adding MCP, more tool providers building
official servers. It's becoming table stakes.
So broader adoption. Definitely. And we're seeing
enhanced functionality. Those read -only limitations
we talked about, they're disappearing. Full read
-write capabilities are becoming much more common,
allowing AI to not just see data but actively
work with it across systems. More action, less
just observation. Right. We're also expecting
more advanced enterprise features, things focused
on robust security controls, compliance auditing,
granular governance, stuff larger organizations
absolutely need. Makes sense for wider business
adoption. And just continued standardization.
As more providers build official MCP servers,
it creates a more reliable, secure, and interoperable
ecosystem for everyone, reducing the reliance
on those riskier community builds. So wrapping
this all up, it feels like MCP is really the
key to unlocking that next level of AI capability
in business. It absolutely is. It's what transforms
AI from being just a clever conversationalist
into a truly integrated, intelligent partner
that understands and interacts with your actual
business processes. and data. So for listeners
wanting to get started, what's the final takeaway?
The advice? Start simple. Play around with the
native integrations first. See what they can
do. Get comfortable with the concept. See the
value firsthand. Dip your toes in the water.
Exactly. Then, as your needs grow, look towards
those official servers. Only venture into custom
solutions when you have a clear, specific need
and understand the implications. And always,
always put security first. Start small. Prove
the value. and then scale up carefully. Sound
advice. It really feels like the future of practical
AI in business isn't just about the AI model
itself, but about how well it connects to everything
else. That's the core of it. Integration is the
intelligence multiplier, and MCP is the standard
making that integration seamless and scalable.
So maybe the final thought for everyone listening
is this. The question isn't if you should adopt
MCP, but really, how quickly can you start using
it to transform your AI from just a chatbot into
a truly intelligent business partner?