If you've integrated a third-party AI API into a content pipeline before, you already know the pattern. Auth headers. Rate limit handling. A conditional branch to decide whether to call a second endpoint based on the first response. Retry logic for the inevitable timeout. None of that is hard, individually, but it adds up to real code you own, test, and maintain indefinitely, for a task that's conceptually simple: check this text, fix it if it needs fixing.
MCP changes where that logic lives. Here's the actual before-and-after, not the marketing version.
The traditional workflow
Say you're building a content pipeline that drafts text, checks it for AI-detectability, and humanizes the flagged sections before publishing. Calling a humanizer's REST API directly, the shape of that integration typically looks something like this:
import requests
