Quick Tech Trends: What Is MCP (Model Context Protocol)?

The quiet standard that let AI models stop being clever chatbots and start actually doing things across your tools.

New here? Quick Tech Trends is my Thursday series where I take one tech term, old or new, and explain it in plain words. No jargon walls, no filler. This week: What is MCP — Model Context Protocol?

Every AI tool you have used probably talks to your other tools in some way.

It reads your calendar, checks a database, searches the web, updates a spreadsheet. Someone had to build a custom connector for every single one of those combinations, until fairly recently. MCP is the thing that made that stop being necessary, and in about a year it went from an internal experiment at one company to something OpenAI, Google, and Microsoft all now speak the same language of.

What MCP actually is?

MCP stands for Model Context Protocol. It is an open standard that gives an AI model a single, consistent way to connect to external tools, files, and data sources, instead of a different custom wiring job for every combination.

The comparison everyone reaches for, because it is genuinely the right one, is USB-C. Before USB-C, every device needed its own specific charger and cable. USB-C did not make devices smarter, it just gave everything a shared plug. MCP does the same job for AI. It does not make a model smarter, it gives it a shared plug into your calendar, your codebase, your database, your Slack, whatever a developer has wired up.

Concretely, an MCP server exposes a tool (say, “search my company’s documents” or “create a calendar event”), and any MCP-compatible AI model can discover it and use it, without a bespoke integration built just for that one model and that one tool.

Why it matters in AI?

Before MCP, the industry had what engineers called the N times M problem. With N different AI models and M different tools, you needed roughly N times M custom integrations, one for every pairing. Add a new model, redo the wiring. Add a new tool, redo it again for every model that wanted to use it.

MCP flattens that into N plus M. Build one MCP server for your tool, and it works with any MCP-compatible model. Build one MCP client into your model, and it works with any MCP server anyone has published. That is not a small efficiency gain, it is the difference between a project that scales and one that drowns in maintenance the moment you add a third tool or a second model.

This is also the plumbing underneath the shift I wrote about a few weeks back, single-turn chat becoming agentic AI. An agent that can actually act needs tools to act with. MCP is quietly becoming the standard way it reaches them.

How this came to picture?

Anthropic open-sourced MCP on November 25, 2024. The idea was straightforward: stop rebuilding the same brittle, bespoke plumbing between every model and every tool, and give the industry one shared protocol instead.

What happened next is the more interesting part. Adoption was fast by any standard I have seen in this industry. OpenAI added native MCP support to its own agent tooling within months. Google folded MCP into its Gemini agent stack later the same year. By early 2026, the ecosystem had crossed roughly 97 million monthly SDK downloads and more than 10,000 public MCP servers covering everything from GitHub and Stripe to Slack and Postgres.

Then came the move that mattered most for its long-term credibility. In December 2025, Anthropic handed MCP over to a newly formed Agentic AI Foundation under the Linux Foundation, co-founded with Block and OpenAI, and backed by Google, Microsoft, AWS, and Cloudflare. A protocol invented by one company now belongs to no one company. That is usually what it takes for a standard to actually stick.

Where it gets interesting?

A shared standard this new brings a shared standard’s usual headaches.

Security is the biggest one. An MCP server is a door into a real system, your database, your email, your files. A model that can call any MCP tool it finds needs real permission boundaries, or you have built a very polite, very fast way to leak or corrupt data. This is not theoretical, it is the first question any serious engineering team asks before wiring an agent into production tools.

Quality is uneven too. Anyone can publish an MCP server, and the community-built ones vary wildly, some well maintained, some abandoned after a weekend project. The official, vendor-built servers from companies like GitHub or Stripe tend to be dependable. The long tail is genuinely a “check before you trust it” situation.

Here is my take. MCP is not exciting on its own, which is exactly the point of good infrastructure. Nobody gets excited about USB-C either. What it does is remove a mountain of duplicate plumbing so that the actual interesting work, what an AI agent should do with the tools in front of it, can finally get built without everyone reinventing the connector first.

The protocol is boring by design. What it unlocked is not.

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Quick Tech Trends: What Is MCP (Model Context Protocol)? was originally published in Bootcamp on Medium, where people are continuing the conversation by highlighting and responding to this story.