How to Create an MCP Server for Your Application

Why MCP?

The Model Context Protocol (MCP) is a step forward in how we interact with LLMs. Instead of copy-pasting or building custom API wrappers for every task, MCP provides a standard way for a model to discover and use "tools" exposed by your server.

The Problem It Solves

If you have a private database or internal CLI, traditionally you'd have to: build a custom API, implement auth, write complex prompts to explain the API to the AI, and wire everything by hand.

With MCP, you build a "server" that exposes those functions as standard tools. The AI (Claude or your own agent) just "connects" and knows how to call them.

Building Your First MCP Server

We'll use Node.js and the official MCP SDK.

1. Initialize the project

mkdir my-mcp-server
cd my-mcp-server
npm init -y
npm install @modelcontextprotocol/sdk

2. Define your tools

An MCP server is basically a list of functions. Create a tool that gets the weather (simulated):

const { McpServer } = require("@modelcontextprotocol/sdk/server");

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

server.tool("getWeather", {
  city: { type: "string" }
}, async ({ city }) => {
  return {
    content: [{ type: "text", text: `The weather in ${city} is sunny and 75°F.` }]
  };
});

server.connect();

3. Connect with the agent

When the server is running, point your MCP-compatible client at its endpoint. The agent will see getWeather as a tool it can use whenever the user mentions weather.

Conclusion

MCP reduces friction. By standardizing the interface between models and tools, we open the door for AI agents to actually "operate" on our infrastructure, not just talk about it.