You'll start hearing this term everywhere: MCP.
Model Context Protocol. Sounds like another acronym to add to the pile. It's not.
The Problem Nobody Talks About Enough
AI models are impressive in isolation. But isolated is exactly what they are.
Out of the box, a model can't read your files, access your tools, update your systems, or trigger workflows. It just… sits there, waiting for you to paste things into it.
So we build integrations. Custom connectors, custom logic, custom handling — one for every tool we want the AI to work with. Google Drive, Notion, Slack, your internal APIs. Each one its own snowflake.
Every new tool means more code. It doesn't scale. And it puts the integration burden entirely on the developer, not on the AI.
What MCP Actually Is
MCP (Model Context Protocol) is a standard way for AI models to interact with external systems.
That's the whole thing. A common interface — so instead of teaching an AI "here's how Google Drive works" and then "here's how Notion works" and then "here's how your internal API works," you teach it once: here's how to use MCP.
The systems that want to be accessible build MCP servers. The AI uses an MCP client. They speak the same language, regardless of what's on either end.
What an AI Can Actually Do With It
Through MCP, a model can:
- Read data — fetch documents, pull context, retrieve memory
- Write data — update records, create notes, persist state
- Trigger actions — run workflows, call functions, kick off processes
Same structure every time. The AI doesn't need to know the internals of every system — just how to talk MCP.
Why This Is a Bigger Deal Than It Sounds
The shift here is real: from tool-specific integrations to AI-native infrastructure.
Before MCP, every AI integration was a custom job. After MCP, systems expose a standard interface and any MCP-compatible model or agent can work with them. That's the unlock for agents that actually do things in the real world — not just generate text, but read, act, and respond across systems.
If APIs made software talk to software, MCP makes AI talk to everything.
This Is the Foundation
MCP is becoming the substrate for AI agents, multi-tool workflows, and real-world AI applications. It's not a feature — it's infrastructure.
I wrote about the distinction between APIs and MCP in more depth in the next post. If you're already wondering "wait, how is this different from just calling an API?" — that one's for you.
The standard layer that lets AI interact with the real world. That's MCP.
— Cheers, NP