Model Context Protocol Explained in Plain Language: What It is, Why It Matters, and How It Changes AI Use.

In a recent conversation, the term Model Context Protocol came up—and right away, we could all agree on one thing: it sounds complex. Honestly, it feels that way too.

In this blog post, I’m breaking down what Model Context Protocol (MCP) actually is—without the jargon. Whether you’re someone using generative AI tools like Claude, Gemini or ChatGPT in your browser, or you’re starting to explore multi-agent systems, this post is meant to help you understand how MCP fits in. You’ll also learn why MCP matters, and how it can shape your next steps—especially if you’re trying to decide whether it’s time to scale your work beyond AI tools built for one-at-a-time chats. (you type something in like a prompt, it responds, and that’s it)

When I first started learning about Model Context Protocol—commonly known as MCP—I went searching online for clarity. What I found instead was a sea of technical jargon, dense documentation, and explanations that seemed written for engineers who had already built a dozen of these systems.

I even picked up a few books on the subject. And while they offered depth, they didn’t offer accessibility. That’s why I wanted to write this blog post.

This blog post isn’t meant to show you how to build a MCP system from scratch—though by the end, you might feel closer to doing that—but to make the concept approachable. So you can make more strategic, informed decisions about the tools and platforms you're considering. So when you hear that an AI system uses MCP, you’ll know what that means, why it matters, and whether it fits the direction you’re heading.

To understand Model Context Protocol (MCP), I think about a friend of mine who's really good at assembling furniture. When she brings home a new set from IKEA, she carefully lays out all the screws, bolts, wheels, and instruction sheets before getting started. Everything has a place, and that organization makes the whole process smoother and faster.

I, on the other hand, usually avoid assembling furniture if I can—I’d rather hire someone who’s good at it, because I know it’s not my strength. But if I have to do it myself, I end up dumping everything from the box onto the floor and sorting as I go. That approach works, but it’s slower, and can be frustrating—especially when screws get kicked under the couch. I forget which part goes where. Or, sometimes the manual reads more like a book of riddles than a guide.

That’s why when I first learned about Model Context Protocol—or MCP—it immediately reminded me of my friend’s method of assembling furniture. Developed by Anthropic, an AI safety and research company known for creating the Claude family of language models, MCP is a way to organize and present all the pieces an AI system might need to do its job—documents, tools, prompts, files, and even memory.

The idea is simple: instead of making your AI start from scratch every time you open a new session, MCP gives it a consistent, organized workspace to pull from. Think about when you open an AI tool in your browser and it doesn’t remember anything you said before—you have to re-upload a document, re-explain your goal, or copy-paste the same prompt or instructions again. With MCP, that repetition goes away—empowering you to work without wasting time or losing momentum.

The diagram above offers a helpful way to see how Model Context Protocol actually works behind the scenes (diagram published by Everything Wrong with MCP by sshh.io). The user interacts with an AI assistant—like ChatGPT, Claude, or Cursor—in turn, that assistant uses MCP to connect with different third-party tools behind the scenes. These tools (known as MCP "servers") might include:

• Google Drive MCP (for pulling files)

• Perplexity MCP (for live search)

• Alexa MCP (for IoT or smart device control)

In a real-world example, you could ask Claude: “Check my research notes in Google Drive, search Perplexity for missing sources, and then turn my lamp green using Alexa when you’re done.”

The assistant isn’t able to do this on its own—it’s using MCP to smoothly coordinate these tasks. But it’s more than just smart coordination. MCP gives you persistent, organized access to the tools and resources that define the system’s context and output.

That means you don’t have to keep re-entering files, typing prompts or repeating instructions, or resetting the environment.

These tools and documents become part of the AI assistant’s ongoing memory—always available, always in reach. And because you can expand what’s connected to the MCP, you’re not limited to one-off queries. You can add more tools, build toward longer workflows, deeper analysis, and have efficient results—without losing momentum.

For a long time, these kinds of limitations—like having to start fresh every time you open your browser, or losing access to your files, memory, or tools the moment you log out—pushed many teams to consider multi-agent systems. Before MCP, AI agents were often designed with embedded logic that allowed them to manage long-term context, make decisions based on stored memory, and even collaborate with other agents to carry out complex tasks at scale—unlike browser-based generative AI tools, which typically start fresh each session.

But what's interesting now is that thanks to emerging infrastructure like Model Context Protocol, you don’t necessarily need to jump straight to building a full multi-agent system. MCP brings many of the same benefits—context continuity, tool access, memory reuse—into environments that are still powered by a single model. It lets everyday tools like ChatGPT or Claude act more like intelligent collaborators by giving them structured access to everything they need. In other words, what once required a team of agents can now be achieved through a well-organized system.

That structure matters exponentially more when you're working with multi-agent systems. The more agents you have, the more tools, tasks, and responsibilities are being juggled.

In multi-agent systems, you might have one agent that needs access to memory logs to track changes across the system. Another agent might be analyzing documents—whether that's patient records, legal filings, or financial reports. A third agent might be coordinating with external APIs or databases. If these agents can't smoothly share tools and resources, their systems break down quickly.

In the context of a multi-agent system, Model Context Protocol (MCP) acts like a central switchboard—connecting agents to the tools they need to do their jobs. In the diagram above, you see a “Source Agent” on the left using MCP to reach out to tools like a browser, APIs, a vector database, and a local file system. Even if you’re not familiar with what each of these tools does, just know they’re examples of the kinds of resources agents might need to perform tasks, gather information, or share updates in a larger system.

Instead of manually wiring or teaching each agent how to use every tool, MCP provides a shared map. All agents follow the same directions to find what they need—whether it’s a file, an API, or a memory store. You don’t have to set up separate instructions for every agent and every tool. They all work from the same layout, which saves time, reduces confusion, and keeps tools and resources accessible—without agents bumping into each other, duplicating tasks, or losing track of what’s already been done.

As pictured in the image above, MCP sits in the middle, handling the back-and-forth between agents and tools. It keeps the system clean, coordinated, and ready to scale. (Image Source: Model Context Protocol Crash Course, Part 2 – DailyDoseofDS)

Structure and organization matters in AI systems—but only if you know where to put things. That’s often the challenge with generative AI tools like Claude, ChatGPT, or Gemini, and even more so with agents running in the background. You may have files, prompts, tools, or even shared memory—but where do they actually go? Where do you “store” something so that the system can find and reuse it later?

That’s where Model Context Protocol comes in. It doesn’t just enforce structure for structure’s sake—it gives your AI environment a central place for everything, and a way to access it when needed. Whether it’s an agent fetching a document or a model remembering what’s already been done, MCP helps keep the whole system aware, efficient, and aligned.

It’s what I like to call the “house rules” for AI systems. A shared etiquette that tells every agent, “Here’s how we work together. Here’s where things are. Here’s what’s been done. Here’s what’s off-limits.” When agents follow the same rules in the same space, they can coordinate more intelligently and securely—without stepping on each other’s toes.

Is It Time to Build a Multi-Agent System?

If you’ve been thinking about building a multi-agent system, it’s worth stepping back to ask: Do you really need to?

A lot of people move in that direction because they’ve hit the limits of personal AI tools—things like having to start from scratch every time, constant editing and tweaking of AI outputs, not being able to share work easily, or needing something that can run in the background without constant input.

But now, with tools that use Model Context Protocol (MCP), some of those gaps are starting to close. You can get more structure, more continuity, and even some collaboration features for teams—without having to build an entire system from the ground up.

If you're trying to decide whether it's time to scale up, I can help you think it through. The goal isn’t just to use the newest tools—it’s to make sure your systems are aligned, efficient, and worth the effort. Sometimes that means building a multi-agent system. Other times, it means learning how to better use what’s already available.

Thinking about implementing AI or multi-agent systems? I’d love to help or answer any questions you have. I also offer workshops and strategy support—learn more on my website!