What Is MCP and Why It Matters for Developers

The problem MCP solves

AI assistants are powerful, but they're trapped. They can generate code, answer questions, and reason through problems — but they can't do anything. They can't query your database, deploy your app, or check your monitoring dashboard.

Every AI provider solved this differently. OpenAI has function calling. Google has extensions. Anthropic had tool use. Each implementation was bespoke, tightly coupled to a single model provider, and impossible to reuse across clients.

The Model Context Protocol (MCP) changes that. It's an open standard — originally developed by Anthropic and now adopted across the industry — that defines a universal interface between AI assistants and external tools.

How MCP works

MCP follows a client-server architecture. The MCP client is the AI assistant (Claude Desktop, Cursor, VS Code, or any compatible app). The MCP server is a lightweight process that exposes tools the AI can call.

An MCP server declares its capabilities — a list of tools with names, descriptions, and input schemas. When the AI decides it needs to use a tool, the client sends a structured request to the server, which executes the action and returns the result.

The key insight: the protocol is model-agnostic and client-agnostic. An MCP server you build today works with Claude Desktop, Cursor, Windsurf, and any future client that implements the protocol. Write once, run everywhere.

What MCP servers look like

An MCP server is just a program — typically a Node.js or Python script — that speaks the MCP protocol over stdio or HTTP. A minimal server might look like this:

import { Server } from '@modelcontextprotocol/sdk/server';

const server = new Server({
  name: 'my-tool',
  version: '1.0.0',
});

server.setRequestHandler('tools/list', async () => ({
  tools: [{
    name: 'search_docs',
    description: 'Search documentation',
    inputSchema: {
      type: 'object',
      properties: {
        query: { type: 'string' },
      },
    },
  }],
}));

server.setRequestHandler('tools/call', async (req) => {
  const results = await searchDocs(req.params.arguments.query);
  return { content: [{ type: 'text', text: results }] };
});

That's a working MCP server. Any compatible AI assistant can discover and call search_docs without any custom integration.

Why this matters for developers

MCP creates a new distribution channel for developer tools. Instead of building a CLI, a web UI, and an API, you can build an MCP server and instantly be accessible from every AI coding assistant.

• Database tools — Let the AI query, migrate, and inspect your database directly.
• Monitoring — Give the AI access to logs, metrics, and alerts so it can debug production issues.
• DevOps — Deploy, scale, and manage infrastructure through natural language.
• APIs as tools — Wrap any REST API as an MCP server and let AI assistants use it natively.

The developer who used to search Stack Overflow, then copy a curl command, then parse the output — now just asks the AI, and the MCP server handles the rest.

The ecosystem is real

MCP isn't theoretical. Major clients already support it: Claude Desktop, Cursor, VS Code (via GitHub Copilot), Windsurf, and others. Thousands of MCP servers exist on npm and GitHub, covering everything from Slack to Kubernetes to Figma.

This is the early web all over again. The protocol is established, the clients are shipping, and the ecosystem is wide open for developers who build useful tools.

The missing piece: payments

There's one major gap. MCP servers are free by default. There's no built-in way to charge for access, gate features by plan, or track usage. If you want to build a business around your MCP server, you're on your own — wiring up Stripe, building a license key system, managing tiers.

That's exactly what Vend solves. Two lines of code to add license keys, tier gating, and Stripe billing to any MCP server. But that's a story for another post.

MCP is the interface layer between AI and the real world. If you build tools for developers, it's worth understanding now — before the ecosystem matures and the early-mover window closes.