Okay so Gartner just dropped a report saying over 16,000 MCP servers were deployed in 2025 alone, and if your reaction is "what the hell is an MCP server," congrats—you're in the same boat as like 90% of people outside the AI world.
But here's the thing: MCP (Model Context Protocol) is quietly becoming one of the most important pieces of AI infrastructure, and most people have no idea it exists. Which is kind of a problem, because it's about to be everywhere.
What Even Is MCP?
Think of MCP as the universal translator for AI. It's how AI agents talk to your data, your APIs, your tools—basically everything that isn't just the AI itself.
Before MCP, if you wanted your AI to access your Google Drive, you'd build a custom integration. Want it to also check your calendar? Another custom integration. Want it to pull data from your CRM? You guessed it—more custom code.
MCP basically said "what if we just standardized all of this?" And apparently 16,000 companies said "yeah, good idea" and deployed servers this year.
Why This Matters More Than You Think
I'll be honest, when I first heard about MCP a few months ago, I thought "cool, another protocol" and moved on with my life. Then I actually tried to build an AI agent that needed to access multiple data sources, and I immediately understood why this exists.
Without a standard protocol, you're basically reinventing the wheel for every integration. With MCP, you write the integration once and any MCP-compatible AI can use it. That's... actually huge?
It's like when USB became standard. Before that, every device had its own connector, and it was a nightmare. Now you just plug stuff in and it works. MCP is trying to do that for AI agents.
The Security Nightmare Everyone's Ignoring
Here's where this gets spicy though: 16,000 servers means 16,000 potential security holes.
Gartner's report mentions that without a proper control layer, companies are "scattering credentials, spinning up ad-hoc connections, and creating security blind spots." Which is corporate speak for "everyone's doing this wrong and it's going to bite us later."
I talked to a friend who works in enterprise security, and when I mentioned MCP servers, he literally groaned. Because now his team has to track how AI agents are accessing data across who-knows-how-many different MCP connections, and nobody built proper logging or governance into this from the start.
It's like we collectively decided "let's make AI agents super powerful at accessing everything" before asking "should AI agents be super powerful at accessing everything?"
TrueFoundry and the Gateway Solution
Apparently TrueFoundry is leading something called MCP Gateways, which Gartner mentioned as bringing "enterprise-grade governance and observability" to this mess.
Translation: someone finally realized that maybe we should track and control what AI agents are doing instead of just letting them run wild through our infrastructure.
This is the kind of boring infrastructure work that nobody talks about but absolutely matters. Like, AI agents are cool until one of them accidentally leaks customer data because someone misconfigured an MCP server. Then it's a congressional hearing.
What This Means for Developers
If you're building AI stuff and you're not thinking about MCP, you should probably start.
The point-to-point integration approach—where you custom-code every connection—is dying. Which is good! It was a pain. But it also means you need to learn a new way of doing things.
I've been experimenting with MCP integrations for a side project (can't share details yet, but it involves automating research workflows), and the learning curve is... moderate? Like, it's not brain surgery, but it's also not "copy paste this code and you're done."
The docs are getting better though. And the ecosystem is growing fast. There are already MCP implementations for most common tools—Slack, Google Workspace, GitHub, Notion, you name it.
The Enterprise Adoption Wave
What really caught my attention in the Gartner report was the enterprise adoption numbers. We're not talking about startups experimenting—we're talking about Fortune 500 companies deploying this stuff in production.
SAP reported 22% cloud revenue growth partly driven by AI agents using MCP to orchestrate workflows. That's real money flowing through these systems.
And once enterprise adopts something, everyone else follows. It's like when big companies started using AWS—suddenly cloud infrastructure became the default, and on-premise everything started looking outdated.
MCP is probably on that same trajectory. In two years, not having MCP integration will be like not having an API. Technically possible, but why would you?
My Honest Take on Where This Goes
I think we're at the "early adopter" phase right now. The 16,000 deployments are mostly tech companies and AI-forward enterprises. But that number is going to explode.
Within a year, I'd guess we'll see:
- Major platforms (Google, Microsoft, Salesforce) offering built-in MCP support
- Security frameworks specifically designed for MCP governance
- MCP integration becoming a checkbox requirement for enterprise software
- Probably some high-profile security incidents that make everyone take this seriously
The security thing is what worries me most. We're moving fast because AI is moving fast, but security usually needs time to mature. We're giving AI agents keys to our entire digital infrastructure before we've really figured out how to audit what they're doing with those keys.
What You Should Actually Do About This
If you're technical: Start learning MCP. Like, now. Read the spec, build a test integration, understand how it works. This is going to be a required skill soon.
If you're managing technical teams: Start asking questions about how your AI implementations handle data access. Do you have MCP servers? Are they secured? Who's monitoring them? If the answers are "uh..." then you have work to do.
If you're neither: Just be aware that the AI tools you're using are probably accessing data through these systems, and you should care about that. Ask your IT team about security controls. Push for better governance.
The Bigger Picture
What's happening with MCP is part of a larger shift from "AI as novelty" to "AI as infrastructure." We're past the demo phase. Companies are deploying agents that do real work with real consequences.
That's exciting! It means AI is actually useful, not just hype. But it also means we need to grow up real fast about security, governance, and accountability.
The 16,000 number tells me we're racing ahead with deployment. The security concerns tell me we haven't caught up with the implications. That gap between "what we're doing" and "what we should be doing" is where problems live.
Why I'm Cautiously Optimistic
Despite the security concerns, I'm actually bullish on MCP's potential. Standardization is good. Interoperability is good. Making it easier to build powerful AI agents is... probably good?
The key word there is "probably." Because whether this turns out great or terrible depends entirely on whether we take security seriously now, or wait until after something breaks.
I've been in tech long enough to know how this usually goes: we move fast, break things, patch the worst holes, and eventually figure out best practices. That works fine for most software. But when we're talking about AI agents that can access sensitive data, make decisions, and take actions autonomously... maybe we should skip the "break things" phase?
Just a thought.
The Thing Everyone's Missing
Here's what I find most interesting about the MCP explosion: it's happening quietly. No big announcements. No flashy launches. Just companies deploying infrastructure because they need it.
That's how you know something is real. The hype cycle gets headlines. The actual infrastructure that makes things work just... appears, because people are solving real problems.
MCP isn't sexy. It's not going to be on the cover of tech magazines. But it's probably more important than half the AI products that do get headlines, because it's the plumbing that makes everything else work.
And if there's one thing I've learned about technology, it's that the boring infrastructure layer always matters more than we think it does. We just don't realize it until something breaks.
So yeah, 16,000 MCP servers deployed this year. That number is going to look quaint by this time next year. And whether that's exciting or terrifying depends entirely on whether we solve the security problem first.
My bet? We'll figure it out eventually. After some painful lessons. Because that's how we always do things in tech.
But hey, at least now you know what MCP is. You're ahead of 90% of people, which honestly isn't saying much, but it's something.