From REST API to MCP Server: How I Gave AI Agents Native Access to Korean Web Data

I spent February building 13 Korean web scrapers on Apify. REST endpoints, pay-per-event pricing, the usual. In March, I added one more layer: an MCP server that wraps the whole portfolio. Here's what changed — and what didn't. The Problem with REST for AI Agents When a developer calls my Apify scraper, the flow is: Send HTTP request with query params Wait for run to complete Parse JSON response Use the data When an AI agent (Claude, Cursor, etc.) needs Korean data, that same flow requires: The developer to write tool-calling code The AI to understand the API schema Session management for async runs Error handling for Apify's run lifecycle It works. But it's friction. What MCP Changes MCP (Model Context Protocol) is Anthropic's standard for connecting AI agents to external tools. Instead of an HTTP endpoint, you define a tool with a name, description, and input schema. { "name": "search_naver_places", "description": "Search Korean businesses and places on Naver Maps", "inputSchema": {