What is MCP (Model Context Protocol)? A simple guide

What in the world is MCP?

Is it just another buzzword? Some fancy acronym tossed around the developer community to make things sound smarter than they are?

Thankfully, no.

And it’s definitely worth your attention.

If you’ve ever tried to build an LLM-powered app that actually does something useful — like pull data from Stripe, update a Google Sheet, or send an email — you’ve probably run into a wall when integrating APIs.

Currently, integrating APIs directly with LLMs requires a lot of technical knowledge about what a specific API can (and can’t) do. And if you just feed API docs to an LLM with the hopes of figuring it out, it often leads to an LLM hallucinating.

On top of that, every new API integration requires you to go through the same process over and over again — making it hard to ship new integrations fast.

The whole process is messy and slow.

Now imagine if your favorite LLM can easily talk to your favorite tools — without you needing to hard-code a new integration each time. That’s what MCP (Model Context Protocol) is here to solve.

In this article, we’ll break down exactly what MCP is (in simple terms), why it’s important to be aware of it, and how it works.

Let’s get into it.

What is Model Context Protocol (MCP)?

MCP (Model Context Protocol) is a new open standard created by the AI giant Anthropic that makes it easier for large language models (LLMs) and software applications to work together — unlocking new ways to develop AI tools and workflows.

At its core, an MCP helps developers build AI workflows and agents that leverage LLMs. Think of it this way: you probably have a handful of tools you use every day at work — Slack, an email client, Google Sheets, Notion, etc. You also probably use LLMs like ChatGPT, Claude, Gemini, or Grok.

MCP acts as a bridge between these tools and your favorite LLM, allowing you to create automated workflows where an LLM can help execute tasks across your tech stack.

Why have an MCP server?

Okay, you might be asking yourself, why MCP? Why now? Why not continue to build LLM-powered applications in a way that directly calls APIs?

If it ain’t broke, don’t fix it… right?

Here’s the thing: an MCP makes it 10X easier for developers to build powerful, extensible AI apps without spending tons of time wrestling with API docs, integrations, or unreliable workflows.

Let me explain.

Today, if you want to build an LLM-powered app that interacts with tools and software — like Stripe, Notion, Gmail, etc — you have to integrate their API directly with your app.

This means you have to read through hundreds of pages of docs, figure out which endpoints to use, what authentication is needed, and how to structure your requests. And then, you pray that nothing breaks and the LLM can make sense of it all.

Okay, but why not just feed an API doc directly to an LLM and tell it to just “figure it out” on its own?

Well, it sounds great in theory. But in practice, it’s almost impossible to get reliable results. Sometimes the docs are too complex, and the LLM might hallucinate and not make the right calls. The LLM might not know what tools to call, when to call them, and how to handle any edge cases.

It’s you, as a developer, who needs a deep understanding of how the integration works so you can guide the LLM through every single step.

But what if the creators of those tools (i.e. developers at Stripe) could package up exactly what an LLM needs to know into a standardized format? This is where MCP comes into play.

Instead of forcing LLMs to sift through pages of a doc, MCP filters the key functions of a tool into a clean protocol that LLMs can understand and interact with. This creates lots of benefits.

Why MCP is great for everyone

Having an MCP benefits everyone involved — the developer of an AI app, the creators of a tool, and the open-source community.

• For AI-app developers: You skip the headache of knowing what API calls to make to a tool and instead plug into an MCP server that can interact with an LLM to do all the heavy lifting. The advantage is more than just the increased speed at which you can build. It makes your app more flexible for future tool integrations and add-ons.
• For tool creators (software): Giving developers an MCP server makes your platform easier to integrate with. This makes it easier to unlock more platform usage, create more engagement, and build a stronger ecosystem around your product.
• For the open-source community: Anyone can create and host their own MCP server to make it easier for LLMs to integrate with popular software tools. This creates a shared infrastructure that helps everyone build faster.

Overall, MCP creates a new standard for how LLMs interact and talk with software tools. It reduces dev work for engineers, helps AI-powered apps unlock extensibility, and empowers tool creators to grow the usage of their platforms.

How does MCP work?

Now that we know why MCP is a big deal, and why everyone is raving about it online, let’s go over how it actually works.

At a high level, MCP creates a “middleman” between an LLM and a software tool. This makes it easy for LLMs to make the right calls to an API.

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Here’s how it works:

1. Hosted MCP servers

First off, MCP is a hosted service. An MCP server is simply a translator between your LLM-powered app and a tool’s API (like Stripe or Notion). Instead of your app doing a direct call to an API, it “talks” to the MCP server instead. The MCP server then figures out what call to make to the API to get your users’ desired outcome.

For the MCP server, you can either:

• Host your own MCP server
• Or, use one hosted by someone else (this would be either the tool creator or an open-source community)

Note: There is a cost to hosting and running an MCP server. This cost goes to either the tool that has its own MCP server, an independent developer, or a contributor in an open-source community.

For a developer building an LLM app, you most likely won’t have to pay to host an MCP server if it’s hosted by someone else. However, if you want to host your own MCP, for privacy or custom tooling reasons, you will have to pay to run that server.

2. Creating a standardized protocol for tools

Each software tool can publish its own MCP-compliant interface — a simple version of the functionality created for LLMs to understand and interact with. This reduces the complexity of apps integrating with their tool and helps create consistency across different LLM apps.

However, the best case is that a tool’s own developers create and maintain their own official MCP. If the tool company does not want to do that, then an open-source community can create and share MCP servers for that given tool. Think of this as a public directory for popular platforms.

3. No more build-time integrations

Today, when you’re building an LLM app, you need to integrate APIs at build time. This means you need to hard-code each individual connection from your app's functionality to a tool’s API.

With MCP, you can make your app extensible at runtime.

You won’t have to rebuild a whole integration from reading a tool’s API docs, writing new code to integrate that API into your app, and testing/debugging/deploying your updated app.

With MCP, you can skip all that. Just connect to an MCP server for the tool, and your app can use it instantly, without any rebuilding or redeployment.

4. A new flow for building fast

Now, there’s a new flow for how an LLM talks to a tool’s API. The flow goes like this:

1. The LLM sends a request to an MCP server.
2. The MCP server interprets the request and interacts with the tool’s API.
3. The response from the tool is sent back through the MCP server.
4. The MCP server responds back to the LLM.

This new paradigm decouples your LLM app from a tool’s API — making development faster, smoother, and more reliable.

Here's an example of using guMCP in Cursor to interact with Airtable:

Conclusion

MCP is more than just a buzzword. This new protocol is changing how we think about connecting AI to real-world applications. By creating a standard for how LLMs interact with APIs, MCP opens a new paradigm for how we build LLM-powered apps.

MCP gives us the ability to make our apps more extensible, reduce development headaches, and create more powerful AI workflows that our users crave.

As more creators and companies adopt MCP, we’ll likely see an explosion of AI-powered applications are are easier to build, maintain and expand. And because it’s open source, the developer community plays a huge role in shaping what this future looks like — everything from maintaining MCP servers to improving docs and standards.

If you’re a developer, founder, or creator working with LLMs, now is the best time to explore MCP and figure out how it can improve your stack. Whether you want to host your own server, contribute to an ecosystem, or use existing MCPs, there are many different ways to get involved.

If you want to learn more about how to get started, check out the official MCP documentation, browse GitHub repositories, or join our guMCP community forum.

Here’s to creating a new paradigm for how to build for AI.

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