What Is MCP? The Model Context Protocol Explained in Plain English

You have probably heard the term "MCP" floating around AI discussions lately. Maybe you saw it in a product announcement, a tech blog, or a friend's excited text about how "Claude can now read my files." If you tried looking it up, you likely hit a wall of jargon - JSON-RPC, client-server architecture, transport layers.

Let me cut through all that. MCP, or the Model Context Protocol, is one of the most important developments in how we use AI tools, and the core idea is surprisingly simple. Here is what it is, why you should care, and how to start using it today.

The Problem: AI Tools Live in a Bubble

When you use an AI chatbot like ChatGPT, Claude, or Gemini, you are talking to a model that only knows two things: what it learned during training and what you type into the chat box. That is it.

Want the AI to check your calendar? It cannot. Want it to look up a customer record in your database? It has no way to do that. Want it to read a file on your computer? Nope.

To get AI tools connected to your real-world systems - your files, your apps, your databases - developers have traditionally had to build custom integrations for every single combination. Need Claude to talk to your Slack? That is one custom integration. Need it to read your Google Drive? Another integration. Need it to query your database? Another one. Switch from Claude to a different AI tool? Start over and build them all again.

This "build everything from scratch every time" approach was expensive, slow, and meant most people could not connect AI to the tools they actually use.

MCP: The USB-C of AI

MCP fixes this by creating a universal standard. Think of it like USB-C.

Before USB-C, every device had its own charging cable. Your phone used one plug, your tablet used another, your laptop used a third. Your desk drawer was a tangle of incompatible cords.

USB-C solved that by creating one connector that works with everything. You buy one cable, and it charges your phone, your laptop, your headphones, and your tablet.

MCP does the same thing for AI. Instead of building a unique integration for every AI-tool-to-external-service combination, developers build one connector - called an MCP server - and it works with any AI tool that supports the standard. Build a Google Calendar connector once, and it works with Claude, ChatGPT, Cursor, and every other MCP-compatible application.

The protocol was created by Anthropic (the company behind Claude) in late 2024, and has since been adopted by OpenAI, Google, Microsoft, and the broader AI industry. In December 2025, Anthropic donated MCP to the Agentic AI Foundation under the Linux Foundation, making it a true open standard governed by the community.

How It Works (Without the Jargon)

MCP has three simple roles:

The host. This is the AI application you are using - Claude Desktop, ChatGPT, Cursor, or any other MCP-compatible tool. It is the thing you talk to.
The server. This is a small program that connects to a specific external service. There is an MCP server for your filesystem, one for GitHub, one for Slack, one for Google Drive, and thousands more. Each server exposes a set of "tools" - specific actions the AI can take, like "read a file" or "send a message."
The protocol. This is the standardized language that hosts and servers use to communicate. It means any host can talk to any server without custom code.

When you ask Claude something like "What meetings do I have tomorrow?", here is what happens behind the scenes:

Claude sees that a Google Calendar MCP server is connected
It calls the server's "list events" tool with tomorrow's date
The server queries your Google Calendar and returns the results
Claude reads the results and gives you a natural language answer

The key point: Claude does not need to know anything about the Google Calendar API. The MCP server handles all of that. Claude just knows it has a tool called "list events" and how to use it.

What Can You Actually Do With MCP?

The ecosystem has grown fast. As of early 2026, there are thousands of MCP servers available - the PulseMCP directory alone lists over 8,600. Here are some of the most popular and practical ones:

Filesystem. Let your AI read, write, and organize files on your computer. Ask Claude to "find all PDFs on my desktop" or "create a meeting notes file in my Documents folder." The AI requests your permission before each action.

GitHub. Manage repositories, pull requests, issues, and code reviews directly through conversation. Ask your AI to "show me open pull requests on the main branch" or "create an issue for the login bug."

Slack. Search your message history, summarize channel discussions, and draft responses. Your chat history becomes a searchable knowledge base your AI can tap into.

Google Drive. Search, read, and organize documents stored in your Drive. Great for quickly finding information buried in shared team folders.

Databases. Connect to PostgreSQL, MySQL, SQLite, and other databases. Ask your AI to run queries, explore schemas, or analyze data using natural language instead of SQL.

Web search. Connect to Brave Search or other search engines so your AI can look up current information on the web while helping you.

For a deeper dive into the technical architecture and a full list of supported frameworks, check out our MCP tools overview.

Setting It Up: Your First MCP Server in 5 Minutes

Let me walk you through connecting your first MCP server to Claude Desktop. We will use the Filesystem server because it is the simplest and does not require any API keys.

What You Need

Claude Desktop installed on your Mac or Windows computer (download it from claude.ai/download)
Node.js installed (download from nodejs.org - choose the LTS version)

To check if Node.js is already installed, open a terminal (Mac) or command prompt (Windows) and type:

node --version

If you see a version number, you are good to go.

Step 1: Open the Configuration File

In Claude Desktop, click the Claude menu in your system's menu bar and select "Settings." Go to the "Developer" tab and click "Edit Config." This opens a file called claude_desktop_config.json.

The file lives at:

Mac: ~/Library/Application Support/Claude/claude_desktop_config.json
Windows: %APPDATA%\Claude\claude_desktop_config.json

Step 2: Add the Filesystem Server

Replace the contents of the file with this (or add to it if you already have other servers):

On Mac:

{
  "mcpServers": {
    "filesystem": {
      "command": "npx",
      "args": [
        "-y",
        "@modelcontextprotocol/server-filesystem",
        "/Users/YOUR_USERNAME/Desktop",
        "/Users/YOUR_USERNAME/Documents"
      ]
    }
  }
}

On Windows:

{
  "mcpServers": {
    "filesystem": {
      "command": "npx",
      "args": [
        "-y",
        "@modelcontextprotocol/server-filesystem",
        "C:\\Users\\YOUR_USERNAME\\Desktop",
        "C:\\Users\\YOUR_USERNAME\\Documents"
      ]
    }
  }
}

Replace YOUR_USERNAME with your actual computer username. The paths you list are the only directories the AI will be able to access - it cannot see anything else on your computer.

Step 3: Restart Claude Desktop

Completely quit Claude Desktop (do not just close the window - right-click the dock/taskbar icon and quit). Then reopen it.

If everything worked, you will see a small tools icon in the bottom-right corner of the chat input. Click it to see the tools your filesystem server provides: reading files, creating directories, searching for files, and more.

Step 4: Try It Out

Ask Claude something like:

"What files are on my Desktop?"
"Can you create a new file called shopping-list.txt on my Desktop with a few grocery items?"
"Search my Documents folder for any files related to budgets"

Claude will ask for your permission before performing each file operation. You stay in control.

Adding More Servers

Once you have the hang of it, adding more servers follows the same pattern. Each server is just another entry in your config file. Here is an example with a filesystem server and a Brave Search server running side by side:

{
  "mcpServers": {
    "filesystem": {
      "command": "npx",
      "args": [
        "-y",
        "@modelcontextprotocol/server-filesystem",
        "/Users/YOUR_USERNAME/Desktop"
      ]
    },
    "brave-search": {
      "command": "npx",
      "args": [
        "-y",
        "@modelcontextprotocol/server-brave-search"
      ],
      "env": {
        "BRAVE_API_KEY": "your-api-key-here"
      }
    }
  }
}

Some servers, like Brave Search, need API keys. Others, like the filesystem server, just need a list of directories. The documentation for each server tells you exactly what it needs.

You can browse available servers at the official MCP servers repository on GitHub, or use directories like PulseMCP which catalog community-built servers.

Staying Safe With MCP

MCP servers run on your computer and can potentially access sensitive data, so a few basic precautions go a long way:

Only install servers you trust. Stick to official MCP servers from the modelcontextprotocol GitHub organization or well-known, actively maintained community projects. Avoid random servers from unknown sources.

Give minimal access. When configuring the filesystem server, only grant access to the specific folders you need. Do not point it at your entire home directory.

Review permissions. Claude Desktop asks for your approval before the AI takes any action through an MCP server. Read these requests carefully before clicking "Allow."

Keep things updated. MCP servers are software, and like all software, they receive security patches. Run updates regularly.

The MCP specification itself includes security best practices around input validation, least-privilege access, and encrypted connections. The protocol is designed with safety in mind, but responsible use still starts with you.

MCP and AI Agents: Why This Matters for the Future

If you have been following AI developments, you have probably heard about AI agents - AI systems that can take actions on their own rather than just answering questions. MCP is a foundational piece of the agent puzzle.

An AI agent that can only generate text is limited. An agent that can read your files, check your calendar, query your database, search the web, and send messages on your behalf? That is a genuinely useful digital coworker. MCP is the layer that makes these connections possible in a standardized, secure way.

This is why every major AI company has adopted MCP. It is not just about connecting one chatbot to one file system. It is about building an ecosystem where AI agents can plug into any tool, any service, and any data source - using one universal standard. If you are interested in building agents yourself, our guide on building your first AI agent walks through the process step by step.

Key Terms Glossary

MCP (Model Context Protocol): An open standard for connecting AI applications to external tools, data, and services.
MCP Server: A small program that exposes specific tools or data sources to AI applications using the MCP standard.
MCP Host: The AI application (like Claude Desktop or ChatGPT) that connects to MCP servers and uses their tools.
MCP Client: The component inside a host that manages the connection to a specific server.
Tool: A specific action an MCP server exposes, like "read file," "search messages," or "run query."
stdio: A transport method where the MCP host and server communicate through standard input/output on your local machine. Simple and fast.
JSON-RPC: The message format MCP uses - a structured way of sending requests and receiving responses between host and server.

The Bottom Line

MCP is not complicated. It is a universal plug that lets your AI tools connect to the rest of your digital life. Instead of AI living in a chat bubble, cut off from your files, your apps, and your data, MCP opens the door to AI that can actually do things in your world - with your permission, every step of the way.

The ecosystem is growing fast, adoption is industry-wide, and getting started takes about five minutes. Whether you just want Claude to read files on your desktop or you are planning to build sophisticated AI agents, MCP is the foundation everything is being built on. And now you know what it is.

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