How to Create an MCP Server for Your Application

Why MCP?

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

The Problem it Solves

Imagine you have a private database or a custom internal CLI. Traditionally, to give an AI access to these, you'd need to:

1. Build a custom API.
2. Implement auth.
3. Write complex prompts to tell the AI how to use the API.
4. Manually bridge the two.

With MCP, you build a "server" that exposes these functions as standard tools. The AI (like Claude or a custom agent) simply "plugs in" and knows exactly 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 essentially a list of functions. Let's create a tool that fetches 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. Connecting to the Agent

Once your server is running, you can point your MCP-compatible client to its entry point. The agent will immediately see getWeather as a tool it can use whenever a user mentions weather.

Conclusion

MCP is about reducing friction. By standardizing the interface between models and tools, we open up a world where AI agents can truly "operate" within our infrastructure rather than just talking about it.

At Solardevs, we are implementing MCP layers for our clients to make their legacy systems AI-ready in days, not months.