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

This guide will walk you through building a fully functional Model Context Protocol (MCP) server in just 10 minutes. You’ll learn how MCP works, why it's the new standard for AI tools, and how to build your own server using Node.js or Python.

🚀 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.