Overview

Have you ever thought: ChatGPT, Claude and other AIs are smart, but they can only chat in a dialog box — they can’t help you check your calendar, manage files, or operate databases because they’re “locked in a cage” with no access to the outside world.

MCP (Model Context Protocol) is here to break down that wall.

Simply put, MCP is an open standard protocol that enables AI applications to connect to external systems — files, databases, APIs, various tools — just like giving AI a universal plug (USB-C port). Plug in anything and it works.

One sentence to understand MCP: It’s the “USB-C port” for the AI world — a unified standard, plug and play.

Why Use MCP?

Before MCP

Before MCP existed, if you wanted to connect AI to external tools (like checking the weather, reading calendars, operating databases), each AI application and each tool required custom integration development.

This was like the early days of phone charging ports — one for Android, one for Apple, one for Type-C. Every time you switched devices, you needed a new cable.

The problems were obvious:

• High development costs: Every integration started from scratch
• Not universal: Tools written for Claude couldn’t be used by ChatGPT
• Difficult to maintain: When a tool was upgraded, all integrations had to be changed

After MCP

MCP defines a unified standard:

• Tool providers develop an MCP Server following the MCP standard
• AI applications act as MCP Clients to connect to it
• As long as both sides follow the MCP protocol, plug and play, no redundant development

This is like how USB-C unified charging ports — one cable to rule them all.

What Can MCP Do? Use Cases at a Glance

1. Personal AI Assistant

Let AI help you manage your daily life:

• Connect Google Calendar to check schedules and create meeting reminders
• Connect Notion to organize notes and manage to-do lists
• Connect Gmail to read and reply to emails
• Connect Slack to summarize chat history

Example: You tell the AI “check what meetings I have tomorrow and summarize last week’s project progress notes” — the AI can automatically check your calendar, go through Notion, and give you a summary.

2. Programming & Development

Let AI coding assistants actually “get their hands dirty”:

• Connect to the local file system via MCP to read and write project files
• Connect to databases to directly query and modify data
• Connect to GitHub to automatically create Issues and submit PRs
• Connect to Figma to generate code directly from design mockups

Example: Claude Code connects to Figma via MCP, obtains the design mockup, and generates a complete web application.

3. Enterprise Applications

Make a big impact in the corporate world:

• Connect to multiple databases so employees can query business data in natural language
• Connect to internal systems (CRM, ERP) for AI to automatically compile reports
• Connect to knowledge bases so AI can answer questions based on company documentation

4. Creative & Design

• Connect to Blender to let AI generate 3D models
• Connect to 3D printers to print designs directly
• Connect to video editing tools to automate the editing workflow

MCP Core Architecture

MCP’s architecture is very simple, with only three roles:

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┌──────────────┐         ┌──────────────┐         ┌──────────────┐
│  AI App      │ ◄─────► │  MCP Protocol │ ◄─────► │ MCP Server   │
│ (MCP Client) │         │ (Transport)   │         │ (External    │
└──────────────┘         └──────────────┘         │  Tool)       │
                                                   └──────────────┘

What Can an MCP Server Provide?

An MCP Server can expose three types of capabilities to AI:

How to Use MCP?

Method 1: Use Existing MCP Servers (Recommended for Beginners)

This is the simplest approach — others have already written the MCP Server, you just need to “install and connect”.

Step 1: Find the MCP Server You Need

The MCP ecosystem already has a large number of Servers available. Common sources:

• Official repository: github.com/modelcontextprotocol/servers
• MCP marketplaces: mcp.so, smithery.ai
• GitHub search: Search for mcp-server

Popular MCP Servers:

Step 2: Configure MCP Server in Your AI Application

Using Claude Desktop as an example, find the configuration file:

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

Add MCP Server configuration (using filesystem as an example):

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{
  "mcpServers": {
    "filesystem": {
      "command": "npx",
      "args": [
        "-y",
        "@modelcontextprotocol/server-filesystem",
        "/your/work/directory/path"
      ]
    }
  }
}

After saving, restart Claude Desktop, and the AI will have file management capabilities.

Step 3: Start Using

After restarting, simply ask the AI to use these tools in conversation:

• “Create a notes folder in my work directory”
• “Read the README.md content and give me a summary”
• “Search for recent project Issues”

The AI will automatically call the corresponding MCP Server to complete the task.

Method 2: Use MCP in Cursor / VS Code

If you use Cursor or VS Code for programming development, the configuration is similar.

Create .cursor/mcp.json (Cursor) or .vscode/mcp.json (VS Code) in your project root:

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{
  "mcpServers": {
    "fetch": {
      "command": "npx",
      "args": ["-y", "@modelcontextprotocol/server-fetch"]
    },
    "sqlite": {
      "command": "npx",
      "args": ["-y", "@modelcontextprotocol/server-sqlite", "./data.db"]
    }
  }
}

After configuration, you can use these tools in AI conversations.

Method 3: Develop Your Own MCP Server (Advanced)

If you want to connect AI to your own systems (like a company’s internal API), you can write your own MCP Server.

MCP supports multiple programming languages with official SDKs:

Minimal example (TypeScript):

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import { McpServer } from "@modelcontextprotocol/sdk/server/mcp.js";
import { StdioServerTransport } from "@modelcontextprotocol/sdk/server/stdio.js";
import { z } from "zod";

const server = new McpServer({
  name: "my-server",
  version: "1.0.0"
});

// Define a "check weather" tool
server.tool(
  "get_weather",
  { city: z.string().describe("City name") },
  async ({ city }) => {
    // Your business logic here
    const weather = `${city} is sunny today, 25°C`;
    return {
      content: [{ type: "text", text: weather }]
    };
  }
);

// Start the service
const transport = new StdioServerTransport();
await server.connect(transport);

Once written, configure it in your AI application and start using it.

Which AI Applications Support MCP?

MCP has become an industry standard, with mainstream AI applications offering support:

Configure once, use everywhere — an MCP Server configured in Claude will work in other applications too.

FAQ

Q: What’s the difference between MCP and APIs?

An API is a “private protocol” between two parties, with each API having different calling methods. MCP is a unified standard — all tools that follow MCP can be called by AI in the same way. Think of MCP as “a standard API layer specifically for AI.”

Q: Do I need programming skills to use MCP?

No! If you’re just using existing MCP Servers, you only need to know how to copy and paste configuration files. Programming is only needed when developing your own MCP Server.

Q: Is MCP secure?

MCP Servers run locally by default, and data is not uploaded to the cloud. However, when using third-party MCP Servers, it’s recommended to check their source code and permissions to ensure security.

Q: What environment does an MCP Server need to run?

Most MCP Servers run on Node.js, so you need to install Node.js first. Python-based Servers require a Python environment.

Summary

The essence of MCP is: creating a unified “plug standard” for the AI world, allowing tools and AI to connect freely.

Further reading:

• MCP Official Documentation
• MCP Server Repository
• MCP Marketplace