The problem it solves
When an AI needs to do something real — book, charge, update an order — someone has to decide what it can touch and what it can't. Without that filter, "giving the AI access" usually means giving it access to everything, with no distinction between reading a piece of data and executing an irreversible action.
What an MCP server is, without the jargon
It's an intermediate layer between the AI model and your real systems. The model never touches your database directly: it requests a specific action, that server validates it against a set of rules, and only then executes it. It's the same principle as an ATM: you request an operation, and the bank decides whether to approve it.
What your company gains from this
Every action is logged — what was requested, with what data, with what result. If something goes wrong, there's a real record to review, not just a chat transcript to take on faith. And if you switch AI providers tomorrow, the control layer doesn't change with it.
What to ask any AI provider
Does the model execute actions directly on my systems, or does it go through a layer that validates them? Can I see a log of every action executed? What happens if the model requests something outside what's allowed? If none of these questions has a clear answer, there's probably no real control behind it.
When you don't need quite as much
For a purely informational use case — answering questions about your catalog, for example — the risk is lower. The control layer really matters as soon as the agent can modify something real: an appointment, an order, a charge. That's where this kind of infrastructure stops being a technical detail and becomes a business decision.