🔌 The 'USB-C of AI': Make Your Own MCP

If you've been building AI-integrated apps recently, you know the pain: every single LLM, agent, and coding assistant needs a custom integration to read your database, check your GitHub repo, or pull Jira tickets. It's an endless cycle of writing custom API glue code.

Enter the Model Context Protocol (MCP).

Originally open-sourced by Anthropic, MCP is rapidly becoming the universal standard for how AI agents talk to data sources. It is quite literally the USB-C of the AI world.

Here is why MCP is completely changing the modern developer stack—and how you can start using it to turbocharge your workflow today.

In simple terms, MCP is an open standard that standardizes how AI models access external context. Instead of building a custom plugin for Claude, a different one for Cursor, and another for your custom Python agent, you build one MCP Server.

Any MCP-compatible client (like Claude Desktop, Cursor, or your own app) can instantly connect to that server and understand what tools and data are available.

When an AI connects to an MCP server, it gets access to three core primitives:

Resources are like file systems for AI. They allow the LLM to read data without modifying it.

Tools are functions the LLM can call to actually do things. The server defines the required arguments, and the client prompts the user for permission before executing.

execute_sql_query

, create_github_issue

, or restart_docker_container

.Pre-defined prompt templates that help users get the most out of the connected data.

You don't need to be building an AI startup to benefit from MCP. You can use it today to make your local dev environment incredibly powerful.

Imagine this workflow:

You are debugging an issue in Cursor. Instead of copying and pasting logs from your terminal, you spin up a local Postgres MCP Server and a Datadog MCP Server.

You simply ask your AI:

"Look at the recent 500 errors in Datadog, query the users table in my local Postgres to see if their accounts are active, and find the bug in my codebase."

Because the AI is connected to those MCP servers, it can autonomously fetch the logs, run the SQL query, and fix the code in one seamless interaction.

Building a server is surprisingly easy. You can write them in TypeScript or Python. Here is the conceptual skeleton of exposing a simple database tool in TypeScript:

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

// 1. Initialize the server
const server = new McpServer({
  name: "Local-DB-Server",
  version: "1.0.0"
});

// 2. Add a Tool for the AI to use
server.tool(
  "query_users",
  "Run a search query against the local users database",
  { searchTerm: z.string() },
  async ({ searchTerm }) => {
    // Run your actual DB logic here
    const results = await mockDbSearch(searchTerm);
    return {
      content: [{ type: "text", text: JSON.stringify(results) }]
    };
  }
);

// 3. Start listening over standard I/O
const transport = new StdioServerTransport();
await server.connect(transport);