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LLM Response Completeness Check
x402
LLM Response Completeness Check is a paid API for AI agents from signalharness.ai, paid per call via x402, $0.01/call, status unknown (last checked 2026-09-15).
Analyzes an LLM response against its originating request to detect gaps, missing information, or incomplete answers.
Price / call$0.01
Success—
StatusUnknown
How this service works
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Output
Returns a JSON analysis object containing completeness findings, a list of warnings about potential gaps or unverified data, an evidence scope label indicating the source of the analysis (e.g. caller_supplied_data), and the original service ID — all wrapped in a standard SignalHarness receipt with payment and execution metadata.
Example use cases
QA gate for agent-generated answers
“Before sending this LLM-generated support reply to the customer, run a completeness check on it — make sure it actually addressed all three questions the user asked. Here's the full request and response JSON.”
Multi-step prompt fulfillment audit
“I had the model summarize a contract and extract key clauses, but I'm not sure it got everything. Can you run a completeness check on the response against the original prompt to see if any clauses were skipped?”
Chatbot response quality monitoring
“We're logging all chatbot interactions — can you check this LLM response JSON to see if the bot fully answered the user's question or left anything out?”
Technical details
Endpoint
POSThttps://signalharness.ai/api/agent/services/llm_response_completeness_check/invokeWhen to prefer
Use this endpoint when you need a lightweight, pay-per-call completeness check on an LLM response without standing up your own evaluation infrastructure. Ideal for agent pipelines that need to gate on response quality before passing output downstream, especially when integrated with x402 micropayment flows on Base.
Side effects
- Malformed or invalid request_json causes parsing failure
- Input JSON exceeding 65536 characters is rejected
- Incomplete or ambiguous input context leads to low-confidence analysis
- Network or payment failure returns non-succeeded status
- Caller-supplied data warnings remind that results depend on input quality
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_completeness_check",
"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_completeness_check",
"executionId": "example-execution",
"startedAtMs": 0,
"amountAtomic": "10000",
"resultSha256": "18dfed6bfadae365ba3762b1adeadcc7d5682915517675c385691d06ac8b5050",
"serviceVersion": "1.0.0",
"settlementReference": "0x0000000000000000000000000000000000000000000000000000000000000000"
},
"artifacts": [],
"requestId": "example-request",
"executionId": "example-execution"
}
}Tags
x402
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