Welcome to the deep dive. Today, we're tackling
something really exciting in the world of AI,
how it's moving from just thinking to actually
doing. You know, AI, especially the big models
like ChatGPT and Claude, they've become incredibly
good thinkers, right? They can draft that perfect
email, map out a whole business plan, even spot
bugs in code. It's like having a brilliant consultant
on tap. But here's the snag, the big disconnect,
what some people call the last mile problem,
that AI. It writes the email, but it can't hit
send. It analyzes sales data, sure, but it can't
actually go into your CRM and pull it. It's a
thinker, a planner, but it's stuck in a kind
of digital sandbox. It can't really interact
with all the tools we use every day. Exactly.
That limitation has historically kept AI, even
the really advanced stuff, in the role of an
advisor, a very smart advisor. But still, just
advising there were early attempts to bridge
this gap. People started connecting these language
models to external tools using APIs, application
programming interfaces. That was a key step,
letting AI do things like search the web. But
trying to scale that, it quickly ran into some
serious, almost crippling, complexity. Right.
And that's where today's topic comes in and why
it's so interesting. We're doing a deep dive
into a new standard that aims to fix this really
elegantly and at scale. It's called the Model
Context Protocol or MCP. Our mission today is
to figure out how MCP represents this huge leap,
moving us from just talking to AI to having AI
actually automate complex tasks for us. And crucially,
what does this mean for you? So to really get
why MCP is such a big deal, maybe we should start
with the problem it solves. What was that first
idea for getting LLMs to use tools? You mentioned
APIs. Yeah, the initial thought seemed pretty
straightforward. If the AI needs to, say, check
your calendar or look up a customer in Salesforce,
just hook it up to the Google Calendar API or
the Salesforce API. Simple, right? Well, no,
that idea just completely shattered when people
tried to do it at scale. The fundamental issue
is there's no universal language for APIs out
there in the digital world. Every single service,
every app speaks its own lightly different dialect.
OK, so it's not like USB where everything just
plugs in? Not at all. It's more like needing
a unique adapter and maybe even a voltage converter
for every single device you own. This lack of
standardization created a huge mess. You had
things like inconsistent authentication. One
tool needs an API key. Another uses 002 .0, which
is that whole multi -step secure login dance.
A third wants a bear token. So developers had
to build custom logic for every single authentication
method. Then there were divergent data structures.
What HubSpot calls a user and the information
it includes looks totally different from what
Zendesk calls a user. So you're manually mapping
fields, translating data back and forth. It's
tedious. And don't forget varying error handling
and rate limits. how one API tells you something
went wrong, or how often you're allowed to call
it, is unique. More custom logic for every single
endpoint. That sounds brittle. Extremely. It
became a maintenance nightmare. Imagine Notion
updates its API, which happens all the time.
Poof! Your custom integration breaks. Multiply
that by dozens, maybe hundreds of tools. You
end up with a system that's incredibly fragile,
super expensive to maintain, and just doesn't
scale. So it really is like that brilliant CEO
needing a personal translator for every single
department head. Massively inefficient. I mean,
I've felt that pain just trying to get two regular
software tools to sync properly. It's clear something
had to change. A universal standard was desperately
needed. Absolutely. The friction was just becoming
unbearable. Which brings us neatly to the model
context protocol, MCP. This is positioned as
the universal standard, right? A real leap forward.
It absolutely is. Anthropic introduced MCP in
late 2024, and crucially, they released it as
an open standard. Its whole purpose is to provide
one single standardized way for AI models to,
first, discover what tools are available, second,
understand how to use them, and third, actually
use them. OK, so like a universal translator.
That's a good analogy. Or think of it like a
universal power adapter for AI. Instead of needing
a different plug for every or every API, you've
got one adapter MCP. The AI only needs to learn
to speak this one language, the MCP language.
And any tool or service like Notion or Asana
or whatever, if it wants to be used by the AI,
it needs to implement an MCP -compatible server.
Ah, so the responsibility shifts. Completely,
it flips the script. Okay, let's maybe unpack
this a bit more. You mentioned clients and servers.
What are the three core components making this
MCP ecosystem actually work? Right, there are
basically three key pieces. First, the MCP client.
That's the AI application itself, chat -chip
-a -tee, Claude, maybe a custom agent someone
builds. It figures out what you want, decides
if a tool is needed, and then makes the request
using the MCP protocol. Second, you have the
MCP server. This is the part built and run by
the tool provider, Notion, Asana, maybe your
company's internal database team. Its job is
to advertise what capabilities it offers, the
tools, the data, the actions the AI is allowed
to use, and then actually do those things when
the client asks. And the third piece is the MCP
protocol itself. That's the rule book. The standardized
set of messages for how the client and server
talk to each other, it defines how the client
asks, hey, what can you do? That's technically
a list tool's request. And now it says, OK, do
this specific thing, which is a call tool request.
Got it. Client asks, server answers and acts.
Protocol is the language they both speak. Exactly.
And this leads to that crucial paradigm shift
we mentioned. The burden of creating and maintaining
the translator, the complex API integration logic
moves away from the AI developer and onto the
tool provider. So if Asana wants AI agents to
use its platform, Asana is now motivated to build
a really good, reliable MCP server. It creates
this healthy competition. Service providers want
their tools to be easily accessible and powerful
for AI because that's the future. They handle
their own API complexities behind their MCP server.
That makes so much sense. It distributes the
workload and aligns incentives. Okay, so we've
seen the chaos of direct APIs and we've met MCP,
the potential solution. What does this actually
mean in practice? What are the big game -changing
advantages here? The advantages are pretty profound,
actually. First off, you get true model agnosticism.
What that means is your tool integrations aren't
locked into one specific AI model anymore. You
can swap out Claude for Gemini or maybe some
new open source model that comes along next year
with minimal fuss. You use the best AI for the
job, and you avoid getting locked into one vendor's
ecosystem. That's huge for businesses. Think
about future -proofing. You're not betting the
farm on open AI or Anthropic if a better, cheaper,
or more specialized model appears. You just plug
it in, assuming it's MCP. Precisely. That flexibility
is a massive strategic advantage. Second big
win. Radical simplicity and scalability. Instead
of that tangled mess of point -to -point API
connections we talked about, you get a clean
hub -and -spoke model. Your AI client learns
one protocol MCP and boom, it can talk to any
tool that also speaks MCP. Adding a new tool
isn't some multi -week coding nightmare anymore.
It's closer to just configuring it. Click, click,
done. Okay. Simpler, more scalable. What else?
Third, and this is really interesting for companies,
unlocking internal systems. Because MCP is an
open standard, businesses can build MCP servers
for their own internal, maybe even ancient, proprietary
systems. Wait, so you could have an AI talking
to that weird old inventory database in the back
office that nobody wants to touch? Potentially,
yes, or interacting with a custom -built HR portal
or pulling diagnostic logs from internal servers,
all using the same standard interface that it
uses to talk to Google Drive or Slack. But how
robust is that for those really quirky legacy
systems? Does the old system need a total rewrite?
Great question. No, usually not. You build the
MCP server as layer on top of the legacy system.
Think of it like a modern facade on an old building.
The MCP server takes the standard MCP requests
from the AI and translates them into whatever
weird calls the old system understands. Then
it translates the answers back into standard
MCP format. It's an abstraction layer. Better
internal access. Anything else? Yes. A really
important one. Enhanced security and governance.
Because communication flows through this standard
protocol and defined servers, you can implement
security controls centrally. An administrator
can configure the MCP server for, say, the company's
financial database, to allow an AI read -only
access for analysis but strictly deny it permission
to execute trades or modify records. Getting
that kind of fine -grained control is much, much
harder when you have dozens of ad hoc, one -off
API integrations everywhere. Right. Centralized
control points. That makes perfect sense. So
putting it all together, MCP isn't just about
making connections easier. It feels like it fundamentally
changes what AI is. It moves it from being a
chat partner. To being an autonomous worker,
an engine that can actually orchestrate workflows
across different digital tools. It takes requests
that used to require multiple manual steps and
just handles them. You use the analogy of asking
for a recipe versus having a chef cook the meal.
Exactly. And not just cook the meal, but maybe
clean up the kitchen and order the groceries
for next time, too. It's about handling the whole
process. OK, let's make this concrete. Give us
that overdue project task example again. But
walk us through the difference. First, the old
way, without MCP. Right. The old way, you'd prompt
your AI. Give me a template for a follow -up
email to a team member about an overdue project
task. And you get back? Text? Just the template.
You still have to find the overdue tasks, find
the email address, copy, paste, tweak the email,
save it as a draft. All manual. Precisely. Now
the new way. With MCP -enabled tools, your prompt
could be much more ambitious. Review my Q4 product
launch project in Asana. Find all tasks overdue
by more than two business days. For each one,
get the assigned person's email from our Google
workspace directory. Then, draft a polite but
firm follow -up email mentioning the task name
and due date. Save all these drafts in my Gmail
drafts folder so I can review them. OK, that's
a much bigger ask. How does the AI actually pull
that off behind the scenes? It's an orchestrated
sequence. The AI, the MCP client. First sends
a request to the Asana MCP server, lists tasks
in Project X, filter by overdue status. The Asana
server does that, sends the list back, the AI
processes it. Then for each overdue task, it
sends a request to the Google Workspace MCP server,
find email for user Y. Google Workspace server
responds. Then the AI connects to the Gmail MCP
server, create a new draft email with this subject,
this body, to this recipient. It does this for
each overdue task. Finally, it might send you
a message back saying, OK, I've drafted those
three follow -up emails. They're in your drafts
folder. All the connections, day lookups, and
actions are handled via MCP. That is genuinely
powerful. It's taking maybe, what, 15, 20 minutes
of annoying admin work and just doing it. What
other kinds of complex, multi -step things become
possible? Oh, the possibilities really open up.
Think about financial analysis and reporting.
You could say, access QuickBooks via its MCP
server, pull last quarter's P &L. Now, use the
AlphaVantage MCP server to get the stock performance
for our top three competitors over the same period.
Combine this, make some charts comparing our
P &L to their stock trends, and save it as a
report called Q2CompetitiveAnalysis .docs in
our Google Drive executive reports folder. Wow.
QuickBooks? A stock data service? Google Drive,
all working together from one request. Exactly.
Or consider automated customer support triage.
Imagine this running constantly. Monitor our
intercom inbox. If a new ticket comes in with
keywords like refund request or billing error,
automatically do this. One, create a high priority
ticket in JIRA under the Effion billing project.
Two, assign it to the finance team. Three, post
a Slack message and hashtag urgent support alerts
with the customer name and a link to the new
Jira ticket. So it's proactively monitoring and
taking multi -step actions based on triggers.
That's way beyond just responding to a prompt.
It's a fundamental shift. You state the high
-level goal, the desired outcome, and the AI,
armed with MCP connectors, figures out and executes
the sequence of digital tasks across different
platforms to get it done. You're managing outcomes,
not individual clicks. OK, I think people listening
are probably getting excited about this. So how
can you, the listener, actually start using this?
Is it just for developers, or can regular users
get involved? That's the great thing about a
growing ecosystem. There are entry points for
everyone. For most users, the let's call them
no coders, you're not building anything. You're
just connecting existing pieces. The absolute
easiest way in is through native built -in connectors.
AI platforms like ChatGPT and especially Claude
are rapidly adding MCP support for common apps
right into their interface. You might see options
to connect Google Drive, GitHub, Slack. Claude's
been particularly active here with really capable
connectors for Gmail reading, drafting, sending,
and even things like Claude for Mac or Windows,
which lets the AI interact with your local computer
files and apps. Those native ones are usually
the simplest and safest place to start. So, look
inside your AI tool first for built -in connections.
What if the tool you need isn't built in yet?
Then you look for official third -party MCP servers.
Major SaaS companies think Notion, Microsoft
365, Salesforce are increasingly building and
offering their own official MCP servers. You
can usually find lists of these on places like
the official MCP GitHub repository. Setting them
up is often pretty straightforward for a power
user. Typically, you go to the service like Notion,
generate a special API key or token. Then you
go into your AI tool settings, spot for MCP connections,
paste in that key and maybe a server address,
and restart. It's usually manageable without
coding. Okay, official servers from the companies
themselves. Are there other options? Yes, there
are community connectors. Because MCP is open
source, the community is building connectors
for all sorts of niche tools. You might find
these on websites dedicated to MCP, like mcp
.sosa is one I've seen. But a really important
word of caution here. While many community connectors
are fantastic, built by talented developers,
some might be buggy or might stop being updated.
Or, in the worst case, could even be malicious,
designed to steal your data or credentials. Right,
like browser extensions or apps from unknown
sources. Use with care. Exactly. Use community
connectors for less critical tasks first. Test
them out. And definitely think twice before giving
one access to highly sensitive data like your
email or financial accounts. Stick to official
or native connectors for the really important
stuff if you can. Good advice. Okay, so that
covers users. What about developers who want
to build with this? For developers, MCP is incredibly
powerful. You can build those private MCP servers
we talked about, giving your AI applications
secure access to your company's unique internal
tools and data. Imagine an AI interface for your
custom CRM or inventory system. The MCP standard
itself is well -documented, and there are official
libraries available for popular languages like
Python, TypeScript, Java, making it easier to
get started. great learning resources popping
up, like courses from deeplearning .ai, specifically
on building these kinds of rich context AI apps
with Anthropix tools. When you actually go to
build an MCP server, there are generally two
ways to approach it. You can do a low -level
implementation, basically coding directly against
the MCP protocol specifications. This gives you
maximum control but requires a deeper understanding
of the protocol details. Or you can use high
-level frameworks, libraries like Fast MCP for
Python or Easy MCP for TypeScript, Abstract,
away a lot of the protocol complexity. You can
often just define your tools as regular functions
in your code, add some decorators, and the framework
handles the MCP communication layer for you.
Much faster development. So options for both
deep control and rapid development. It sounds
like the ecosystem is really starting to mature.
We should acknowledge, though, MCP is still pretty
new, right? It's not perfect yet. Oh, absolutely.
It's definitely a young standard. Like any emerging
tech, it has its rough edges. Some connectors
might still be a bit buggy or limited in what
they can do. Setting up a local development environment
can sometimes be fiddly. And maybe most importantly,
the security implications of giving AI autonomous
agency to act on your behalf across multiple
systems. Well, those are profound. We need...
really robust security models, granular permissions,
clear audit trails. It's all still evolving and
requires very careful thought. Definitely something
to keep a close eye on. But even with those growing
pains, the direction seems pretty clear. MCP,
or something very much like it, looks set to
become as fundamental to the AI stack as, say,
REST APIs became for the web. It's that missing
link, that standardized way for intelligence
to actually translate into action in the digital
world. For you listening, whether you're using
AI every day at work or you're a developer building
AI apps or a business leader thinking about strategy,
now really is the time to start exploring. See
what MCP connectors are available for the tools
you rely on. Getting familiar with this, maybe
even contributing back if you're a developer,
feels less like an option and more like a strategic
necessity for the future. Couldn't agree more.
The era of the AI just being a standalone chatbot
in a window. That's ending the age of the integrated
automated AI powerhouse that works across your
digital landscape. That's really just beginning.
So here's something to think about. Looking at
your own work, your own daily digital tasks.
What complex multi -step process, something tedious
or time consuming today, could you imagine an
AI orchestrating for you using this kind of technology?
What could you achieve if that time was freed
up?