HumanRail MCP Server

Route tasks requiring human judgment to a vetted worker pool — directly from any AI agent.

When your AI agent hits something it can't handle — content moderation, refund decisions, subjective quality assessments, data verification — HumanRail routes it to a human worker, verifies the result, pays the worker via Lightning Network, and returns structured output.

Think "Stripe for human judgment."

Quick Start

Install

pip install humanrail-mcp-server

Or run directly:

uvx humanrail-mcp-server

Configure

Add to your Claude Code config (~/.claude.json):

{
  "mcpServers": {
    "humanrail": {
      "command": "uvx",
      "args": ["humanrail-mcp-server"],
      "env": {
        "HUMANRAIL_API_KEY": "ek_live_your_key_here"
      }
    }
  }
}

Or for Claude Desktop (claude_desktop_config.json):

{
  "mcpServers": {
    "humanrail": {
      "command": "uvx",
      "args": ["humanrail-mcp-server"],
      "env": {
        "HUMANRAIL_API_KEY": "ek_live_your_key_here"
      }
    }
  }
}

Get an API Key

Sign up at humanrail.dev to get your API key.

Related MCP server: ReverseCentaur

Available Tools

Example Usage

Once connected, Claude can use HumanRail naturally:

User: "Review this customer's refund request — order #12345, they say the item arrived damaged."

Claude: I'll route this to a human reviewer for a refund eligibility decision. (calls create_task with task_type="refund_eligibility")

The human reviewer has verified: Refund approved. The item shows visible damage in the photos and the customer's account is in good standing.

Task Types

You can create any task type. Common examples:

• content_moderation — Is this content appropriate?

• refund_eligibility — Should we approve this refund?

• data_verification — Is this information accurate?

• quality_assessment — Rate this output 1-10

• document_review — Extract/verify information from a document

• sentiment_analysis — What's the tone/intent of this message?

Output Schema

Define exactly what you need back using JSON Schema:

# Boolean decision
{"type": "object", "required": ["approved"], "properties": {"approved": {"type": "boolean"}}}

# Rating with explanation
{"type": "object", "required": ["score", "reason"],
 "properties": {"score": {"type": "integer", "minimum": 1, "maximum": 10},
                "reason": {"type": "string"}}}

Environment Variables

How It Works

AI Agent → create_task() → HumanRail API → Worker Pool
                                              ↓
AI Agent ← get_task()  ← Verified Result ← Verification Pipeline

1. Create: Agent sends task with context and output schema

2. Route: HumanRail's routing engine assigns the best-matched worker

3. Execute: Worker reviews the context and submits their judgment

4. Verify: 6-stage verification pipeline validates the result

5. Pay: Worker is paid via Lightning Network (instant)

6. Return: Verified result is available via get_task or wait_for_task

Pricing

Pay per task. No subscriptions. Workers are paid from your task budget.

• Low risk: $0.10–$0.50 per task

• Medium risk: $0.25–$1.00 per task

• High/Critical: $1.00–$5.00 per task

Pricing depends on task complexity, SLA requirements, and risk tier.

License

MIT — see LICENSE.