Somewhere above you, a decision has been made that the company is going to use AI. It may already be in your objectives. That decision runs downhill into every system you operate, because an AI is only useful if it can reach your data, and today the standard way it reaches anything is MCP. So the real question was never whether you would support it. It’s how soon your own security reviews, RFPs, and architecture standards start requiring it. That’s what makes MCP table stakes, and it’s closer than the noise around it suggests.
We've all seen how fast this can happen. Two years ago, using AI at work was a novelty you tried on your own time. Then it was a pilot, then it was in the strategy deck, and then it was a line in your objectives with a date attached. The technology didn’t set that pace. The pressure not to be the team left behind did. MCP is the next piece of that same wave. It’s the plumbing that lets all of that mandated AI actually touch your systems, and it’s going to follow the same curve, from interesting, to expected, to assumed.
Right now, MCP still looks like a buzzy capability you can take or leave. That’s exactly how AI looked right before it showed up in everyone’s goals. I would not mistake the current quiet for time you have. The technical people who treat MCP as optional are the ones who will be scrambling when it lands in a procurement checklist or a security standard, explaining why the tools they own don’t support the thing the business now expects.
You get to decide how you meet that. Resist it and it arrives anyway, on someone else’s schedule and shaped by someone else’s defaults. Lean in now, while it’s still yours to shape, and you become the person who already has the answers when the mandate reaches your desk. I would lean in. But leaning in well means knowing what actually matters once the connection exists, because that’s the part the noise skips.
Why the window closes so fast
None of this is a hunch about where the market is heading. MCP is a standard, and standards are built to become universal. Anthropic introduced it in late 2024. A year later, in December 2025, it was donated to a neutral foundation under the Linux Foundation, co-founded with OpenAI and Block and backed by Google, Microsoft, and AWS (Anthropic, December 2025). Every major AI platform speaks it now, and there are thousands of public servers. When a connector becomes infrastructure that fast, "we have it" stops being a reason to choose you, the same way no laptop maker advertises that it has a USB-C port.
So checking that a tool supports MCP is exactly the right thing to do today. In six months, you won’t think to check, because you’ll assume it, the same way you assume a power cable. And once you assume it, the thing that actually decides whether the AI is worth connecting isn’t the connector at all. It’s one layer down.
A protocol is a port
Here’s the trap I watch vendors walk into. A protocol is a port. What matters is what’s plugged into it. When a vendor leads with MCP, they’re advertising the port and hoping you don’t ask what’s on the other end. Ask. Because the answer is where the real differences are, and those differences don’t disappear when the acronym does.
There are four questions worth asking before you choose a platform, and none of them is about the connector.
1. What can the AI actually see?
Most vendors shipping an MCP server expose a slice of their own telemetry: what their sensors captured, where those sensors were placed, for as long as they happened to keep it. Point an AI at that and it can only reason over that vendor’s slice. The alternative is a vendor-neutral record of every conversation that crossed the network you already built, exported by the routers, switches, and firewalls already in place. Same protocol, a completely different depth of answer, because the difference is the data, not the connector.
2. Whose model is running, and on whose infrastructure?
A lot of what gets called AI in this market means the vendor’s model, running on the vendor’s cloud, fed by your data sent out to it. That’s fine until it’s not: a regulated environment, a data-sovereignty requirement, an air-gapped network, a security team that doesn’t want network metadata leaving the building. The durable version is the opposite of that. Your AI, your model, on your own network data. Point the AI you already run at your flows, run it self-hosted, in a sovereign cloud, or air-gapped with your own model, and buy no sensors to feed it. A vendor whose whole value is their closed model can’t offer you that, because offering it means giving up the thing they sell.
3. How far back can it see?
This is the one people skip. An AI connected to your network can only answer for the period your data still covers, and the investigations that matter most arrive months after the traffic. If your history is a few weeks deep, the connector is impressive and the answers are shallow. I wrote a whole piece on this, ""Your AI is only as good as how far back it can see." and the short version is this: retention depth is a property of the data layer, not the AI, and it’s the quiet thing that decides whether any of this is useful the day you actually need it.
4. Can you check the answer?
An AI that hands you a conclusion you can’t verify is a liability, not a tool. The version that lasts shows its work: every answer traces back to the specific flow records behind it, so an analyst, a board, or an auditor can see why. And it investigates on its own without acting on its own. Read-only until you deliberately decide otherwise. The connector is the easy part. Being trustworthy is the hard part, and it’s a property of the platform, not the protocol.
What we built, and why
This is the bet we made at Plixer, and it’s why I’m comfortable telling you the MCP box is a temporary advantage. We have been collecting and analyzing flow for 25+ years, so when we shipped an MCP server in Scrutinizer, we were not exposing a slice of proprietary telemetry. We were opening the vendor-neutral record of everything the network already exports, to whatever AI you already run. Point Claude, ChatGPT, or your own agentic platform at it. Keep months to years of that history on storage you control. Get answers that trace to the flow behind them, from a system that investigates but leaves action in your team’s hands. None of that is the protocol. All of it is what the protocol connects to.
Evaluate the connector. Choose the platform.
So evaluate the connector today. It matters today. But evaluate it knowing that in six months it’s the floor, and what you’ll still be living with is the data underneath it: how complete it is, whose model reads it, how far back it goes, and whether you can prove what it told you. That layer is where we chose to compete, because it’s the same flow record that fixes a performance problem and reconstructs a security incident. Plixer fixes performance issues and detects threats from the same network data. Same flow record, both jobs. That part doesn’t become table stakes.
Key Takeaways
MCP is turning into universal infrastructure, and when it does, the questions worth asking move one layer down, to the data and controls the connector reaches.
- MCP is becoming a standard feature, so "having MCP" will stop being a differentiator within months.
- A protocol is a port. What matters is the data, the model, and the controls on the other end of it.
- The durable differences are the depth of the record the AI reads, whether you can run your own model on your own data, how far back the history goes, and whether every answer is checkable.
- A vendor whose value is a closed model or a proprietary sensor slice structurally can’t offer your own model on a vendor-neutral record.
- Evaluate the connector now, but choose the platform for the layer that outlasts the connector.
Next Steps
If this is the layer you want to evaluate, two pieces go deeper on the parts that matter most.
- For the protocol basics before you judge what sits behind it, "What Is MCP, and Why Is Everyone Suddenly Talking About It?" explains how an AI connects to your systems in plain language.
- For the retention argument in full, "Your AI is only as good as how far back it can see" covers why the depth of your data decides how useful any connected AI turns out to be.
- See it in action: request a live walkthrough of Scrutinizer, and we’ll point an AI at a real flow record and answer a question against traffic from months back, with the records behind every answer.
Frequently Asked Questions
Almost certainly. MCP is an open standard that every major AI platform now supports, and it was placed under neutral foundation governance in late 2025. Standards like that tend toward universal, which is why treating "we have MCP" as a differentiator has a short shelf life.
Look past the connector to what it exposes. Ask what data the AI can actually reach, whether you can run your own model on your own infrastructure, how far back the history goes, and whether every answer traces to evidence you can check. Those are the differences that survive once MCP is everywhere.
It means pointing the AI your team already runs at your data, on infrastructure you control, instead of sending your data to the vendor’s model on the vendor’s cloud. It matters most in regulated, sovereign, or air-gapped environments, and to any team that doesn’t want network metadata leaving the building. A vendor whose value is their closed model usually can’t offer it.
Yes, and that’s the whole point. The AI can only reason over what the connection can retrieve. If the underlying record is a thin slice of one vendor’s telemetry, or only reaches back a few weeks, the answers are limited no matter how capable the model is. Depth and completeness of the data set the ceiling.
You can when the platform supports your own model on your own infrastructure. Plixer lets you run the AI self-hosted, in a sovereign cloud, or fully air-gapped, pointed at your own flow data, so the analysis happens where your constraints require it to happen.
Other vendors’ servers generally expose a slice of their own telemetry. Plixer’s exposes the vendor-neutral record of every conversation that crossed the network you already built, exported by your existing routers, switches, and firewalls. Same protocol, a broader and deeper record for the AI to read, kept as far back as you choose.
No. If a platform already ships an MCP server, connecting the AI you use is closer to entering a URL and a token than building an integration. The evaluation work is not technical setup, it’s asking the four questions above about the data and controls behind the connection.
