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LLM Response Citation Coverage Analyzer
x402
LLM Response Citation Coverage Analyzer is a paid API for AI agents from signalharness.ai, paid per call via x402, $0.015/call, status unknown (last checked 2026-09-15).
Analyzes an LLM-generated response to measure how well its claims are supported by citations or referenced sources.
Price / call$0.015
Success—
StatusUnknown
How this service works
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Output
Returns a JSON object containing a citation coverage analysis of the submitted LLM response, including a coverage breakdown, any warnings about unverified or unsupported claims, an evidence scope label indicating the data came from the caller, and a status field. The result is wrapped in a signed receipt with payment confirmation on Base USDC.
Example use cases
Auditing AI research report citations
“I have an AI-generated research summary and I need to know what percentage of its claims are actually supported by cited sources — can you run a citation coverage analysis on this response JSON and flag any unsupported assertions?”
QA check before publishing AI content
“Before we publish this LLM-written article, check the citation coverage on this response JSON to make sure all factual claims are properly referenced — I don't want to put out anything that isn't backed up.”
Validating chatbot answer grounding
“Here's the JSON output from our RAG chatbot — can you analyze the citation coverage to verify that the answer is actually grounded in the retrieved sources and not just hallucinated?”
Technical details
Endpoint
POSThttps://signalharness.ai/api/agent/services/llm_response_citation_coverage/invokeWhen to prefer
Choose this endpoint when you need a lightweight, pay-per-call check on whether an LLM-generated response adequately cites its sources — especially in RAG pipelines, AI content workflows, or agent quality-assurance loops where hallucination and grounding are concerns. Prefer this over manual review when you need automated, programmatic citation coverage scoring at $0.015 per call via x402/USDC micropayment.
Side effects
- Malformed or non-JSON request_json input causes validation error
- request_json exceeds 65,536 character limit returns rejection
- Empty or trivially short input (fewer than 2 characters) is rejected
- LLM response contains no citations at all, yielding zero-coverage result with warnings
- Caller-supplied data flagged as unverifiable, triggering caller_supplied_data warning in result
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": "llm_response_citation_coverage",
"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": "llm_response_citation_coverage",
"executionId": "example-execution",
"startedAtMs": 0,
"amountAtomic": "15000",
"resultSha256": "b5015898b24d0a54e906d8efaf74affd12af3e5bbb3fa76810dc45f9b1d5ba56",
"serviceVersion": "1.0.0",
"settlementReference": "0x0000000000000000000000000000000000000000000000000000000000000000"
},
"artifacts": [],
"requestId": "example-request",
"executionId": "example-execution"
}
}Tags
x402
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