What Is MCP (Model Context Protocol) — and Why Enterprises Care

MCP is having its moment, and most of what’s written about it is aimed at developers — how to build a server, how to wire a client. Useful if you’re coding. Less useful if you’re the person deciding whether MCP should shape your enterprise AI strategy. So here’s the version for buyers: what it is, why it matters, and what to actually ask about it.

What is MCP?

MCP — the Model Context Protocol — is an open standard for connecting AI models to tools and data through one consistent interface. It was introduced by Anthropic and has been adopted broadly across the industry. The one-line version: it’s a universal way to plug AI into the systems it needs, so the connection doesn’t have to be custom-built every single time.

The problem MCP solves

Before a standard, connecting AI to tools was an N×M problem. Every AI application had to build a bespoke integration to every tool it wanted to use — ten AI apps and twenty systems meant two hundred custom integrations, each one to build and maintain. That doesn’t scale, and it’s a big reason enterprise AI stays stuck in demos: the plumbing is enormous.

MCP turns N×M into N+M. Each tool implements MCP once, each AI app implements MCP once, and they interoperate. The integration cost stops multiplying and starts adding.

How it works

MCP uses a client–server model. A system exposes its capabilities through an MCP server —tools (actions the AI can take, like creating a ticket) and resources (data the AI can read). The AI application acts as an MCP client that discovers and calls them over the standard protocol. Because both sides speak the same language, new connections don’t require new integration code on both ends.

Why enterprise buyers should care

Three reasons this belongs in a buying decision, not just an engineering one. It cuts integration cost and fragility, which is often the real blocker to getting AI into production. It reduces lock-in, because tools built to MCP work across compliant AI applications and models, so you’re not welded to one vendor’s stack. And it gives you portability as the model landscape shifts — a real advantage when you’d otherwise have to rebuild integrations every time you change models.

The part the developer guides skip: governance

Here’s what a buyer has to hold that a tutorial won’t tell you. MCP is a connection standard, not a permission system. It defines how AI reaches a tool, not whether a given user should be allowed to, or what gets logged when it does. And the moment MCP exposes tools that act on your systems — not just read from them — that gap becomes the whole ballgame.

So MCP is the “where,” and governance is the “how safely.” An MCP hub is only enterprise-grade if every call through it passes a policy and audit layer: authentication, per-user and per-tool permissions, checks on sensitive actions, and a record of what happened. That’s the difference between a powerful connector and an ungoverned door into your systems.

What to ask vendors about MCP

• Do you support MCP, as a client, a server, or both?
• Is access through MCP governed — authenticated, permissioned per user and per tool?
• Is every MCP call audited, so you can show what the AI did?
• Do actions (not just reads) go through policy checks and approvals?
• Does MCP support let me keep model and tool choices portable, or does it re-lock me in?

Get good answers to those and MCP becomes what it should be: the standard, governed way AI reaches the systems your business runs on. That’s exactly the job of a governed connection fabric.

Frequently asked questions