If you’ve watched any of our breakdowns lately, you’ve heard the term MCP. It comes up constantly, usually without anyone stopping to define it. So here it is in one line: MCP is a standard way to connect an AI model to the tools, data, and systems it needs to actually do work.
MCP stands for Model Context Protocol. It was introduced by Anthropic in November 2024, and it has quietly become the way the entire industry wires AI into the real world. If you’re trying to make sense of modern AI systems, this is one of the few acronyms worth knowing.
Why MCP exists
A language model on its own is a closed box. It can reason, but it can’t see your CRM, read your files, or pull a record from your database. To make it useful, you have to connect it to your systems. And until recently, every one of those connections was a custom job.
That gets expensive fast. Anthropic put it plainly at launch: even the most capable models were walled off from the data that would make them useful, because every new source needed its own bespoke integration. Picture ten AI applications that each need to talk to a hundred tools. Done the old way, that’s up to a thousand separate integrations to build and maintain — every model-to-tool pairing reinvented from scratch.
That’s the problem MCP was built to kill.
A universal connector for AI
The cleanest way to picture MCP is USB-C for AI. Before USB-C, every device had its own cable and you kept a drawer full of adapters. One standard connector replaced all of it. MCP does the same thing for software: instead of a different custom integration for every model-and-tool combination, you build to one protocol.
Build an MCP server for your database once, and any model that speaks MCP can query it. Swap the model later and the connection still works. The integration is no longer welded to a single vendor.
How MCP works
MCP uses a simple client-server setup with three pieces:
- The host is the AI application you’re using — a chat assistant, a coding tool, an agent you’ve deployed.
- The client lives inside the host and speaks the protocol.
- The server is a lightweight connector that exposes one system: your Google Drive, a Postgres database, a GitHub repo, a Slack workspace.
When the AI needs something from that system, the request travels host to client to server and back, in a format every MCP server understands. Anthropic shipped pre-built servers for common systems like Google Drive, Slack, GitHub, and Postgres on day one, and the public catalog has since passed 10,000. When someone says they’re “adding an MCP,” this is what they mean: standing up one of these servers so the AI can reach a system it couldn’t before.
Why a shared standard matters
A standard is only useful if people actually agree to use it, and MCP cleared that bar in record time. OpenAI adopted it in early 2025, Google and Microsoft followed, and by the end of the year Anthropic had handed MCP to the Linux Foundation so no single company controls it. Competitors don’t agree on much. They agreed on this.
For the kind of systems we build, that matters more than it sounds. When we do AI engineering for a client, the hard part is rarely the model — it’s connecting that model to the dozen systems the business already runs on. MCP turns those connections into reusable parts instead of brittle one-offs. The claims system we built for a dental group lives or dies on clean access to payer portals and practice software, and standard connectors are what keep a system like that maintainable a year later instead of breaking the first time a tool updates.
The bottom line
MCP is the plumbing, not the magic. The model is still the engine, but an engine wired to nothing doesn’t move anything — and MCP is fast becoming the standard wiring. It’s still young, not yet two years old, and the details will keep shifting. The core idea won’t: connect AI to your systems once, through a standard, and stop rebuilding the same integration over and over.