What is an MCP Server, MCP Client, and MCP Host?

Model Context Protocol (MCP), though confusingly named, is an open standard that helps AI systems easily and securely connect to external data and tools. Imagine it as the universal translator that lets AI models talk to the rest of the digital world without endless fiddling. In the words of Anthropic (the creator of the MCP):

“MCP standardizes how AI models connect to external tools and data. It’s open-source and designed to be used by anyone.”

Platforms like Claude, Cursor, and Windsurf are already using it, which shows its chops.

The basics of MCP

Here’s the headache MCP solves: AI assistants (or coding bots, or task automators) often need fresh data or superpowers—think real-time calendar checks, running code, searching massive docs. But until now, every single tool needed a unique, painstaking integration. Picture dozens of spaghetti cables where every single app plays by its own rules. As Anthropic jokingly puts it, “Just as USB-C provides a standardized way to connect your devices to various peripherals and accessories, MCP provides a standardized way to connect AI models to different data sources and tools.” If you have one of those drawers in your home with at least 15 different types of cables, you know why that matters.

MCP keeps things model-agnostic and open-source—which means it works with any AI model or platform and is open for anyone to use or improve. MCP isn’t just for chatbots or fluffy assistants; you’ll see it powering coding copilots, document search, workflow automation, and more. If an AI tool needs to run real code, query live data, or fetch information, MCP is usually what’s making that handshake run smoothly.

There are three different components in MCP:

• The Host: This is where the AI “lives”—like the operating base.
• The Client: The helpful connector or interpreter that sits between the AI and the outside world.
• The Server: The external tool or service that provides the data, search results, or computing magic.

Under the hood, MCP uses something called JSON-RPC to handle all the message traffic. But don’t worry: Most developers won’t ever touch those bits directly because there are handy SDKs and plug-and-play integrations.

One of the slickest parts about MCP is that servers run in their own space, away from the brainy AI models. The host gets to decide which tools are allowed to plug in and when—so it’s not a digital free-for-all.

At the end of the day, MCP is quickly becoming a foundational way for AI agents to get out of their sandboxes and interact with the world: scalably, securely, and without a tangle of custom wires. USB-C for AI, but with zero risk of plugging it in upside down.

Server vs Client vs Host

At its core, MCP works through a relationship that has three main players: the host, the client, and the server. Sounds like a crowded dinner table, right? Let’s break down what each person is actually doing.

We already gave the tech-y explanation, but here's a slightly tastier analogy. Imagine:

• The host is a tourist, trying to order food in a foreign country.
• The client is the translator, bridging the language gap.
• The server is the restaurant server, only speaking the local language.

So, the tourist tries to order a meal. They speak to the translator, who then passes the request to the server. The server can suggest menu options or bring the food, but waits for the translator (client) to do the asking. The translator (client) makes sure both the tourist (host) and server understand each other.

One part that gets confusing is that these roles are logically separate, but that doesn’t mean they always run on different machines. For example, in something like Claude Desktop, you might have a file server running locally right on your own computer. But in other cases—like if you use a tool such as Asana, server could live somewhere entirely remote, maybe even in the cloud. Over time, more servers are expected to move into the cloud, so this split becomes even more common.

A common mistake is thinking the “client” is doing the job of both host and client, or that “client-side” always means both together. But if you’ve found your way to this post, you probably want the distinction: the host is the AI agent itself, the client manages connections, and the server is the source of truth or tools.

In terms of communication flow:

• The host starts things off, making a request.
• The client is the messenger, sending and receiving on the host’s behalf.
• The server only replies and never initiates anything on its own.

And a single client always connects to just one server, but a host can wrangle many clients and servers all at once, in case your AI agent is the social butterfly type. For example, tools like Cursor will have dozens of clients running that connect to things like Github, CI/CD tools, internal docs, Jira tickets, etc.

So, next time you’re thinking about MCP, just picture a hungry tourist, a multitasking translator, and a helpful waiter. Suddenly, those technical docs might seem a little bit tastier.

The server is not really (always) a server

Here’s where things get a bit quirky — when people hear “MCP server,” they might think they need to fire up a massive cloud machine or start wrangling Kubernetes. Great news: you can leave your AWS console closed for now.

Despite the name, an MCP server is usually nothing like a traditional web server (usually). Today, most MCP servers are just simple apps running locally. Picture a tiny ExpressJS or FastAPI server, maybe just a few hundred lines of code, happily chugging away on the end user's laptop or desktop.

Some companies are starting to deploy their own hosted MCP servers in the cloud. Anthropic, for example, rolled out a cloud-hosted MCP connector API to make it even easier for folks to use MCP without running their own server at all, and dozens of companies like Asana, Atlassian, Linear, and Zapier have already hopped on.

For most developers though, especially if you’re just experimenting or building tools for yourself, setting up a heavy cloud server is overkill. Just spin up a local server and you can always switch to deploying it later if you need to.

A simple example

Let’s say you want to schedule a meeting.

So, you ask your helpful AI assistant: “Can you schedule a meeting for tomorrow at 2 PM with Jordan?”

Here’s how the magic happens behind the scenes, step by step.

What does the MCP Host do?

First, the host kicks things off.

Remember, the host is the main app the user is interacting with, let's say it's Claude Desktop. In this scenario, the Claude Desktop realizes, “Hey, I need to use Google Calendar to make this event.” But it doesn’t do the job itself.

Instead, it would tell the MCP Client to use the createEvent tool on the Google Calendar MCP server. The host would also pass the relevant info: 2 PM tomorrow, invite Jordan.

What does the MCP Client do?

Now it’s the client’s turn.

The client is already connected to the Google Calendar MCP server and knows about a bunch of tools it can use, like listEvents, searchEvents, and createEvent. It's already shared those with the host previously, which is why the host knows to use the specific createEvent tool.

So when the host tells the client to call that specific tool, it gets to work. It bundles up the details and packages them into a request into JSON-RPC and sends this request to the server and waits for an answer.

Once it receives a response in JSON-RPC, whether it's an error or a success, it'll convert that back to a format the host can understand and relay to the user.

What does the MCP Server do?

Here’s where the server finally gets involved.

The MCP Server receives the request from the client and springs into action. It talks directly to the Google Calendar API, making the actual request to create the meeting.

Once Google Calendar processes the request, the server gets the outcome (success, error, or maybe a “did you mean 2 AM?”). It sends this response back through the client, which then relays it to the host — and finally, you get to see whether your meeting with Jordan is set.

So, next time your AI assistant makes scheduling look easy, you’ll know there’s a host, a client, and a server all working together behind the scenes.

Conclusion

In a nutshell, MCP strips away the wiring-diagram headaches that used to come with plugging AI into the real world. By cleanly separating the host (the thinker), the client (the translator), and the server (the do-er), it lets you mix-and-match tools without rewriting half your codebase or babysitting fragile APIs.

Whether those servers live on your laptop or in someone else’s cloud, the flow stays the same—and that reliability is what finally makes “agents that act” feel doable, not dreamy.

So if you’re tinkering with AI assistants, coding copilots, or any automation that needs live data, start by wiring things the MCP way.

You’ll spend less time gluing endpoints together and more time building the features people actually care about. Think of MCP as the USB-C port for your model: one clean plug-in, and suddenly the whole ecosystem of tools is at your fingertips.