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SignalHarness AI Answer Citation Integrity Report
x402
SignalHarness AI Answer Citation Integrity Report is a paid API for AI agents from signalharness.ai, paid per call via x402, $0.03/call, status unknown (last checked 2026-09-15).
Analyzes AI-generated answers for citation integrity issues, flagging unsupported claims, hallucinated sources, and reference quality problems
Price / call$0.03
Success—
StatusUnknown
How this service works
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Output
Returns a JSON analysis report containing integrity warnings, an evidence scope classification (caller_supplied_data), and a structured analysis JSON identifying citation issues, unsupported claims, or reference quality problems. Also includes a signed receipt with payment confirmation, latency, and a SHA-256 result hash for auditability.
Example use cases
Fact-checking AI research summaries
“I have an AI-generated literature review with citations — can you check whether the citations actually back up the claims made, and give me a report of any integrity issues?”
Detecting hallucinated sources in chatbot output
“My chatbot just produced an answer about climate policy with several cited papers. Can you audit it for hallucinated or fabricated references and tell me which claims lack real support?”
QA pipeline for AI content publishing
“Before we publish this AI-written article, run a citation integrity check on it and flag any unsupported assertions or suspicious source references so we can review them.”
Technical details
Endpoint
POSThttps://signalharness.ai/api/agent/services/ai_answer_citation_integrity_report/invokeWhen to prefer
Choose this endpoint when you need automated citation integrity checking specifically for AI-generated answers or documents, especially in QA pipelines for AI content, research validation workflows, or agent output auditing. Prefer this over general fact-checking APIs when the concern is citation-claim alignment rather than real-world fact verification. At $0.03 USDC per call it is cost-effective for per-document auditing.
Side effects
- Malformed or non-JSON request_json input returns a validation error
- Input exceeding 65536 character limit is rejected
- Citations that cannot be parsed from the input may be silently skipped
- Result is based solely on caller-supplied data — no external URL fetching or live source verification
- Ambiguous or poorly structured answers may yield low-confidence or generic warnings
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| request_json | string |
Output
Schema · Response
{
"type": "json",
"example": {
"replay": false,
"result": {
"warnings": [
"Verify the caller-supplied data before relying on this result."
],
"service_id": "ai_answer_citation_integrity_report",
"analysis_json": "{\"example\":\"schema-valid caller-supplied data\"}",
"evidence_scope": "caller_supplied_data"
},
"status": "succeeded",
"receipt": {
"asset": "0x833589fCD6eDb6E08f4c7C32D4f71b54bdA02913",
"usage": [],
"status": "succeeded",
"network": "eip155:8453",
"artifacts": [],
"endedAtMs": 0,
"latencyMs": 0,
"paymentId": "example-payment",
"receiptId": "example-receipt",
"requestId": "example-request",
"serviceId": "ai_answer_citation_integrity_report",
"executionId": "example-execution",
"startedAtMs": 0,
"amountAtomic": "30000",
"resultSha256": "c2a97ddfcd2e67a7e40f989ff8420e73f700a4f1ca9c29e4a89c059ad1c7e067",
"serviceVersion": "1.0.0",
"settlementReference": "0x0000000000000000000000000000000000000000000000000000000000000000"
},
"artifacts": [],
"requestId": "example-request",
"executionId": "example-execution"
}
}Tags
x402
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