Build Your Own MCP Server in 10 Minutes — The Only Guide You Need

🚀 Why MCP Is the Future of AI Tools

MCP (Model Context Protocol) is an open standard that lets AI models interact with external tools, apps, and data sources in a structured, secure way. Think of it as the upgrade to traditional function calling — more reliable, more flexible, and universal across LLMs.

🧩 What You'll Build

By the end of this guide, you'll have an MCP server that supports:

• Tool registration
• Schema-based actions
• Secure AI function access
• JSON-based tool results

Step 1: Create Your Project

{mkdir mcp- server
cd mcp - server
npm init - y}
        

Step 2: Install MCP Server Packages (Node.js)

{npm install @modelcontextprotocol / sdk}
        

Step 3: Create a Simple MCP Server

{// index.js
import { Server } from "@modelcontextprotocol/sdk";

    const server = new Server({
      name: "demo-mcp-server",
      version: "1.0.0",
    });

    server.tool("get_time", {
      description: "Returns current server time",
      run: async () => {
        return { time: new Date().toISOString() };
      },
    });

    server.start();
    console.log("MCP Server running..."); }
        

Python Version (Optional)

{from mcp import MCPServer
import datetime

server = MCPServer(name = "demo-mcp", version = "1.0.0")

@server.tool()
def get_time():
return { "time": datetime.datetime.utcnow().isoformat() }

server.start()
print("MCP Server running...")}
        

Step 4: Connect Your MCP Server to an AI Model

In your AI client setup:

{client.mcp.connect({
  url: "http://localhost:3000",
}); }
        

🎉 You're Done!

You now have a fully functional MCP server ready to integrate into any AI agent, workflow, or tool ecosystem. Expand it with more tools, secure it, and deploy anywhere.