MCP: The USB-C for AI Explained in 5 Minutes

Want to build AI agents that can actually do things? Meet MCP, the USB-C for AI applications. Whether you're using Claude, GPT, or any other AI model, MCP lets them securely interact with your tools and data while keeping you in control. In this article, we'll take MCP for a spin, showing you how to give your AI real-world capabilities, from searching across your entire computer instantly to building powerful automated workflows. And the best part? It's all standardized and secure.

How MCP Works: A Quick Breakdown

Think of the Model Context Protocol (MCP) as a universal connector for AI applications. At its core, we have what we call the Host. This could be Claude Desktop, your favorite IDE, or any AI tool. The host is where you interact with the AI.

Inside the host, MCP clients create direct connections to different servers. Each connection is independent, letting the host talk to multiple data sources at once. These servers are lightweight programs that each handle specific tasks. For example, one server might connect to your local files, another could link to your database, and a third might connect to web services.

This is where MCP itself operates. In the transport layer, the Model Context Protocol standardizes all client-server communication, defining exactly how the host requests resources, executes tools, and interacts with each server—regardless of what that server provides.

A Flexible and Powerful Ecosystem

The beauty of this design is its flexibility. Just like USB lets you plug in any compatible device, MCP lets AI applications securely connect to any data source or tool that speaks the protocol. This modular approach means you can easily add new capabilities to your AI workflows just by connecting new servers—no need to rebuild your entire application.

The MCP ecosystem includes powerful implementations across various domains, from enterprise solutions like Cloudflare and Neon to community-developed servers for Docker and Spotify. MCP's versatility enables seamless integration across data, development, and productivity tools.

Getting Started: Installing MCP Servers

Before we dive into integrations, let's quickly cover how to install MCP servers. The process is straightforward, with different options depending on the programming language used by the MCP servers.

• For TypeScript-based servers: We'll use npx, which makes installation as simple as running a single command.
• For Python-based servers: You have two excellent options: uvx (recommended for simplicity) or the traditional pip installer.

These package managers handle all dependencies automatically, making setup a breeze.

Practical Integrations: Supercharging Your AI

Now that we have our installation basics covered, let's move on to actually integrating these servers.

Web Search with Brave

ChatGPT now has the ability to do web search, and Claude can do it too with the help of MCP. Let's equip Claude with powerful search capabilities using the Brave Search MCP Server. This integration will allow us to perform web searches directly within our AI conversations.

First, you'll need an API key from Brave. The free tier provides plenty of searches to get started. Now, add this configuration to Claude Desktop:

{
  "servers": [
    {
      "name": "Brave Search",
      "command": "npx -p @mcp/brave-search-server brave-search-server",
      "env": {
        "BRAVE_SEARCH_API_KEY": "YOUR_API_KEY"
      }
    }
  ]
}

You'll need to restart Claude Desktop for these changes to take effect. And just like that, you've transformed Claude into a powerful search assistant capable of finding and analyzing information from across the web using Brave's privacy-focused search engine.

Local File Operations

Next, let's integrate the Filesystem MCP Server, which gives us powerful local file operation capabilities. The filesystem server needs to know which directories it can access. We'll set this up in our configuration to ensure secure operations.

{
  "servers": [
    {
      "name": "File System",
      "command": "npx -p @mcp/filesystem-server filesystem-server",
      "args": ["--root", "/path/to/safe/directory"]
    }
  ]
}

With this setup, you can now perform file operations like reading, writing, moving files, and searching through directories—all directly through Claude's interface, but only within your specified safe directories. This integration transforms Claude into a powerful file management assistant.

Lightning-Fast Computer Search

Let's take this up a notch by installing an MCP server that can search for all files on your computer. The Everything Search MCP Server is an open-source tool that provides fast file searching across different operating systems.

• Windows: It uses voidtools' Everything Search Engine.
• macOS: It leverages the built-in mdfind command.
• Linux: It uses the locate command.

For this demonstration, we'll use Windows with Everything Search. First, install Everything by voidtools and download the required SDK files containing the DLL for the MCP server. The server is available on the Python Package Index and can be installed via uvx.

With this powerful search integration, you can instantly find and organize files across your entire system.

Creating a Powerful Workflow

Now that we have both the file system and Everything Search servers set up, let's see how they can work together to create a powerful file organization assistant.

First, use Everything Search to get a complete overview of your files and identify any duplicates. By combining Everything Search's lightning-fast file discovery with the file system server's organization capabilities, Claude becomes an efficient file management assistant that can clean up and organize your files in seconds.

The Model Context Protocol opens up a world of possibilities for AI integrations. Whether you're streamlining your workflow or building innovative tools, MCP provides the foundation you need.