MCP - Revolutionary AI Tooling You Didn't See Coming (Model Context Protocol)

STARTUP HAKK
14 views • March 29, 2025

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I've seen standards come and go, but the Model Context Protocol is about to completely transform how we build with AI - and most developers don't even know it exists yet.
Think about what APIs did for web development in the early 2000s - MCP is going to do the exact same thing for AI systems, solving the massive integration headache that's currently holding back truly useful AI applications.
If you're tired of AI systems that can talk a big game but can't actually connect to your company's systems or access real-time data, MCP is about to blow your mind.
While everyone else is obsessing over the latest model with a few more IQ points, the really huge leap forward is happening in how these models connect to the real world - and that's what we're diving into today.

Let me break down what's really happening with AI tools right now - we've got these incredibly smart models that are basically trapped in a box, unable to access your systems, your data, or your tools without custom integration code for every single connection.
MCP (Model Context Protocol) is a new open standard created by Anthropic that acts as a universal connector between AI models and all the systems they need to talk to - like a USB port for artificial intelligence.
I've spent decades watching new tech standards emerge, and this one has all the markers of something that's going to completely reshape how we develop AI applications.
If you're building AI tools right now and not paying attention to MCP, you're going to wake up six months from now and wonder why everyone else's tools suddenly got ten times more capable than yours.
Point 1: The Big Problem MCP Solves
Every AI company right now is struggling with the same integration nightmare - how do you get these language models to actually connect with real business systems, data, and tools?
I've seen countless AI projects fail because the integration cost was higher than the value the AI itself delivered - you'd spend months building custom connectors just to get the AI to read your database or access your CRM.
What engineers call the "M×N problem" means that connecting M different AI models to N different data sources requires M×N separate integrations - a nightmare that grows exponentially as you add more systems.
MCP transforms this into a simple M+N equation - each model and data source only needs one connector to the protocol, and then they can all talk to each other seamlessly.
After 25 years in development, I've learned that the standards that win are the ones that solve real pain points, and integration complexity is the #1 obstacle holding back AI adoption in most businesses.
Just like how APIs revolutionized web development by creating a standard way for systems to talk to each other, MCP is doing the same for AI - and it's going to unlock a whole new generation of AI tools that can actually DO things instead of just chatting.

#AI #MCP #LLM #ModelContextProtocol #softwaredeveloper
#codeyourfuture #coding #learn2Code #learntocode

0:00 all right so AI is going crazy we see
0:03 everybody talking about AI but we've
0:05 obviously talked a lot about the hype
0:06 cycle with AI here but this is a change
0:08 that actually really will change the way
0:10 we're using AI right so I've talked a
0:13 lot about what it means to have ai how
0:14 we're not going to hit AGI and all that
0:16 but this is the model context protocol
0:19 or mCP and this is changing the way that
0:21 we interact with these llms that we're
0:23 using so think about what API did for
0:26 web development in the early 2000s M mCP
0:29 is going to do the exact same thing for
0:30 AI systems if uh solving the massive
0:33 integration headache that's currently
0:34 holding back truly useful AI
0:36 applications so if you're tired of AI
0:38 systems that can't talk that can talk a
0:40 big game but can't actually connect to
0:42 your systems or can access data real
0:44 time mCP is about to change this so
0:47 while everyone else is obsessing over
0:49 the latest model with a few more IQ
0:51 points the really huge leap forward is
0:53 happening in how these models connect to
0:55 the real world and that's what we're
0:56 going to dive into today so let's get
0:58 started
1:07 welcome to Startup pack I'm Spencer
1:08 Thomson here startup pack we love train
1:09 software developers and our license
1:10 coding boot camps as well as build
1:12 custom software solutions for companies
1:14 with a decade of executive leadership as
1:15 a fractional CTO and 25 years in
1:17 software development I've mastered
1:19 transforming Tech teams and products all
1:20 right so let's break down what's really
1:22 happening with AI tools right now we've
1:24 got these incredibly smart models that
1:26 are basically trapped inside of a box
1:28 unable to access your systems your data
1:30 or your database or your tools without
1:32 custom integration code for every single
1:34 connection now mCP which stands for
1:37 model context protocol is a new open
1:39 standard created by anthropic that is
1:42 that acts as a universal connector
1:44 between AI models and all the systems
1:46 that they need to talk to so it's like a
1:48 USB port for artificial intelligence now
1:50 I've spent a long time looking at new
1:51 tech starts and this is one of the
1:53 things that I think has the markers of
1:55 something that's really going to go huge
1:56 right because if you're building AI
1:58 tools right now and not paying attention
2:00 to ACP you're going to wake up 6 months
2:02 from now and wonder why everyone's tools
2:03 suddenly got 10 times more capable than
2:05 yours so let's talk a little bit about
2:07 the big problems that it that um mCP um
2:11 solves here right so uh move my head out
2:15 of the way here so what we've got here
2:17 is we've got uh the general architecture
2:19 so you can see that a host with an mCP
2:22 client right so think of like Claude
2:23 because this is this is uh you know
2:25 claude's um Claude is the one who's
2:27 really host driving this charge here
2:30 so Claud ID tools Etc so the host comes
2:33 in and this is where you might send in a
2:35 chat request or something like that this
2:38 goes to the model context protocol which
2:39 will go to server a could go to a
2:41 database can do all these things but
2:43 this host is then driving back through
2:45 these mcps and allowing it even like a
2:47 web API to go out to a remote service
2:49 right so if you want to go read a
2:50 browser or if you want to have something
2:52 that's going to integrate with data from
2:53 a database this is a new open protocol
2:57 that anthropic is publishing and that
3:00 everybody's getting on board with so
3:02 this will allow us to get into like uh
3:04 where you can see there's for servers
3:06 and for other things um this is their
3:09 open dial uh their model context
3:11 protocol um documentation here for them
3:13 to start building into this so this is
3:15 going to be a big shift because we're
3:17 going to start to see more and more
3:19 things be connected to llms which are
3:21 going to make them more useful right so
3:23 every company right now is struggling
3:25 with the same integration Nightmare how
3:27 do you get these language models to
3:28 actually connect to reals buiness
3:30 systems datas and tools so what
3:32 Engineers calls M M time n uh problems
3:36 right meaning that the connecting M to
3:39 different AI models to n different data
3:41 sources requires M time M separation
3:43 integration so it's a nightmare that
3:45 grows exponentially as you add more
3:47 systems well mCP transformed this into a
3:49 simple M plus n equation with each model
3:52 and data source only needs one connector
3:54 to the protocol and then they can talk
3:56 to each other seamlessly so I've learned
3:59 that the standards win are the ones that
4:01 solve real pain points integration
4:03 complexity is the number one obstacle
4:04 holding companies back so if your
4:06 company is struggling to get your
4:07 systems connected make sure you reach
4:09 out to us because here at starter pack
4:10 our specialty is connecting systems so
4:12 that your company can work like a well
4:14 oiled machine so just like how API
4:16 revolutionize the web development uh by
4:19 creating standards like rest for systems
4:22 to talk to each other mCP is going to do
4:24 the same thing for AI and it's going to
4:26 unlock a whole new generation of AI
4:28 tools that can actually do things
4:30 instead of just chatting and I think
4:33 this is going to where we're going to
4:34 start to see things really start to
4:35 accelerate uh and accelerate into the
4:38 business right so the model context
4:40 protocol Works through a straightforward
4:41 client server architecture where the AI
4:43 assistant is the client and your data
4:45 sources or tools run mCP servers that
4:48 expose their functionality so anthropic
4:51 describes mCP as a usb-c for all AI so
4:54 just like a USB standard how devices
4:56 connect mCP standardizes how AI systems
4:59 will connect so the beauty of mCP is its
5:01 Simplicity it defines just three primary
5:03 things resources and that's data that
5:06 can be accessed tools an action that can
5:08 be formed and prompts templates for
5:10 interaction so instead of your AI
5:12 getting direct access to your systems
5:14 which would be a security nightmare the
5:16 mCP server acts as a secure Gateway
5:18 controlling exactly what the AI can and
5:20 can't do now the protocol itself uh runs
5:23 on Json rpc2 so it's running in a Json
5:26 format to pass the messages back and
5:28 forth with makes it which makes it
5:29 really easy to stream that data and also
5:32 to be human readable so what impressed
5:33 me about this when I dug into mCP was
5:35 how lightweight it was we're not talking
5:36 about some massive complex framing work
5:38 but a clean simple minimal J uh Json
5:42 transfer that's just got some good
5:44 standards built around it so despite
5:47 being released just a few months ago by
5:49 anthropic the mCP ecosystem is already
5:51 growing at a really fast pace which
5:53 shows how much interest and how much
5:55 this is solving there's now over 250
5:57 community-built mCP servers connecting
5:59 to everything from GitHub and Google
6:01 drive to SQL databases and 3D modeling
6:04 so I've been tracking open source
6:05 movement for decades and the speed of
6:07 mCP adoption reminds me of how fast
6:09 Docker took off right startup companies
6:12 are jumping on mCP because it lets them
6:14 connect their AI to a vast range of
6:15 tools without investing millions in
6:18 custom integration code so platforms
6:20 like cursor Zed repet and codium have
6:23 already ingrade mCP enabling their AI
6:25 systems to access and manipulate code
6:27 repositories in ways that weren't
6:29 possible before the GitHub repository
6:31 for NPC servers looks like a developers
6:33 dream catalog so with tools like Claude
6:36 desktop developers can now test their
6:38 mCP servers locally making the
6:40 development experience smooth and
6:42 productive um now the fundamental shift
6:44 mCP enables is moving from passive AI
6:46 model passive AI which is models that
6:49 just respond to prompts to active AI
6:52 which are systems that can access
6:54 real-time information and take action in
6:56 the world so I've learned that systems
6:59 that can only talk but not act are
7:00 really a limited utility and so far what
7:02 we've seen with a lot of the llms is
7:05 just a neat party trick to just be able
7:07 to do chat like it's talking to a human
7:09 right but getting started with mCP is
7:11 surprisingly straightforward anthropo
7:13 provides an SDK which we're going to
7:15 working on some uh code samples here for
7:17 you uh provides SD for both typescript
7:20 and python that handle all the protocol
7:22 details and it lets you focus on
7:24 actually doing the integration now for
7:26 teams just getting started I'd recommend
7:28 checking out some exist existing servers
7:30 on GitHub to see how they're structured
7:32 they're excellent templates for
7:33 practically every type of system you
7:34 might want to connect to now before mCP
7:37 a AI developers were typically using
7:39 approaches like rag which is retrieval
7:41 augmented generation and using those
7:43 functions or calling or custom plugins
7:45 all with significant limitations
7:47 compared to what MTP enables rag is
7:49 fantastic for querying static document
7:51 collection but it can't perform actions
7:53 or access access Dynamic real-time data
7:56 the way the mCP can so open AI function
7:59 calling approach was a step in the right
8:01 direction but remained proprietary and
8:02 tightly coupled to their specific models
8:05 so popular Frameworks like laying chain
8:07 tried to solve similar problems but
8:08 focus more on chaining AI capabilities
8:10 rather than standardizing connections so
8:14 where other applications fill temporary
8:15 patches mcps feels like a proper
8:17 protocol that can be that could become a
8:19 fundamental to ai's http is to the web
8:23 now some of the limitations of mCP right
8:25 now is that mCP is still emerging
8:27 standard with some Growing Pains the
8:29 protocol is evolving really quick which
8:31 means there's still a risk of breaking
8:33 changes in future updates security is
8:35 also an area that's maturing uh early
8:37 mCP invitations FOC on local focus on
8:39 local connections and Enterprise gr at
8:42 remote authentication is still being
8:43 developed now another challenge is a
8:45 lack of central registry or Discovery
8:48 mechanisms for NPC servers currently
8:50 it's a bit of a wild west with servers
8:52 scattered across different repositories
8:54 now I know with Docker we ran into some
8:55 of this in the early days as well now
8:57 based on my experience with emergency
8:59 Emery Tech standards these issues will
9:01 likely be resolved as M mCP matures but
9:04 organizations adopting it today should
9:06 be prepared for some bumps as you go
9:08 down the road now for Enterprise clients
9:11 I've worked with these big ones the
9:13 biggest AI concern is always securing
9:15 data privacy and they're terrified of
9:17 sending sensitive data to an external AI
9:19 provider mCP offers a compelling
9:21 solution to this problem the AI
9:23 assistant never accesses your data
9:24 directly but instead makes requests
9:26 through mCP servers that you can control
9:29 can secure and also can log and track so
9:31 you know exactly where the data is going
9:33 so in Practical terms this mean
9:34 companies could deploy Claud to help
9:36 customers support for example while
9:38 keeping all their customers data within
9:39 their own system a huge win for
9:42 compliance and security Now if your
9:44 systems aren't connected make sure you
9:45 reach out to us because here at startup
9:47 pack our specialty is connecting systems
9:49 so that your company can work like a
9:51 well oiled machine so finally talking
9:53 about looking to the future I see
9:54 several clear EV evolutionary paths for
9:57 mCP that will make it even more powerful
9:59 uous to the AI ecosystem Enterprise
10:02 grade security features are coming
10:04 really soon including oauth integration
10:06 and some robust authentication
10:08 standardized Discovery mechanisms are
10:10 also likely on the horizon enabling AI
10:12 systems to dynamically find and utilize
10:14 mCP servers without requiring man
10:16 configuration so for developers and
10:18 companies planning their AI strategy the
10:20 message is clear mCP has quickly
10:22 established in itself as a standard for
10:25 AI integration and the early adopters
10:27 will have a significant advantage in
10:28 building the next generation of AI
10:30 applications now what are your thoughts
10:32 do you agree do you disagree i' love to
10:33 have a great discussion here at starter
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