Humanrail
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.
Available Tools
| Tool | Description |
|---|---|
create_task | Route a task to a human worker for review/judgment |
get_task | Check the status and result of a task |
wait_for_task | Poll until a task completes (blocking) |
cancel_task | Cancel a pending task |
list_tasks | List tasks with filters (status, type, date range) |
get_usage | View usage stats and billing summary |
health_check | Check if the HumanRail API is reachable |
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 1-10document_review— Extract/verify information from a documentsentiment_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
| Variable | Required | Default | Description |
|---|---|---|---|
HUMANRAIL_API_KEY | Yes | — | Your API key (ek_live_... or ek_test_...) |
HUMANRAIL_BASE_URL | No | https://api.humanrail.dev/v1 | API base URL |
How It Works
AI Agent → create_task() → HumanRail API → Worker Pool
↓
AI Agent ← get_task() ← Verified Result ← Verification Pipeline
- Create: Agent sends task with context and output schema
- Route: HumanRail's routing engine assigns the best-matched worker
- Execute: Worker reviews the context and submits their judgment
- Verify: 6-stage verification pipeline validates the result
- Pay: Worker is paid via Lightning Network (instant)
- Return: Verified result is available via
get_taskorwait_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.
Server Config
{
"mcpServers": {
"humanrail": {
"command": "uvx",
"args": [
"humanrail-mcp-server"
],
"env": {
"HUMANRAIL_API_KEY": "your-api-key"
}
}
}
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