I Built an MCP Server That Gives AI Agents Human Judgment

The Problem AI agents are incredible at 90% of tasks. But that last 10% — content moderation, refund decisions, subjective quality assessments, edge cases requiring human context — that's where they fall apart. I kept running into this building automation systems: the AI would hit a decision it couldn't confidently make, and the whole pipeline would stall. So I built HumanRail — an API that routes tasks requiring human judgment to a vetted worker pool, verifies the result, pays the worker via Lightning Network, and returns structured output. Think "Stripe for human judgment." Why MCP? MCP (Model Context Protocol) is exploding. It's the standard for connecting AI models to external tools. Claude, ChatGPT, Copilot — they all support it. There are already 18,000+ MCP servers listed across registries. But here's what's missing: a way for AI agents to escalate to humans when they're stuck. That's the gap HumanRail fills. I built an MCP server so any AI agent can: Route a task to a human wor