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SignalHarness Retrieval Failure Diagnosis Report
x402
SignalHarness Retrieval Failure Diagnosis 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 caller-supplied retrieval context data and returns a structured diagnostic report identifying failure modes and warnings in a retrieval pipeline.
Price / call$0.03
Success—
StatusUnknown
How this service works
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Output
Returns a JSON object containing an analysis_json field with structured diagnostic findings, a warnings array flagging data quality concerns, an evidence_scope field indicating the data provenance (caller_supplied_data), and a full execution receipt with payment details, latency, result hash, and service version information.
Example use cases
Debugging empty RAG retrieval results
“My RAG system keeps returning empty results for certain queries — can you run a retrieval failure diagnosis on this pipeline snapshot and tell me what failure modes are present: {"query": "product pricing tiers", "retrieved": [], "top_k": 5, "similarity_threshold": 0.85}?”
Identifying vector search quality issues
“Our semantic search is returning completely irrelevant documents — please diagnose this retrieval failure and give me a structured analysis: {"query": "customer churn prediction", "results": [{"score": 0.42, "doc": "office supply inventory"}], "expected_domain": "analytics"}”
Auditing agent memory retrieval failures
“My AI agent's memory retrieval keeps missing relevant past context — can you run a failure diagnosis report on this retrieval event to identify what's broken: {"agent_id": "agent-42", "retrieval_attempt": "find prior user preferences", "matches": 0, "index": "episodic_memory"}?”
Technical details
Endpoint
POSThttps://signalharness.ai/api/agent/services/retrieval_failure_diagnosis_report/invokeWhen to prefer
Choose this endpoint when you need a structured, auditable diagnostic report on retrieval pipeline failures and want a pay-per-call model with cryptographic receipts. Prefer this over ad-hoc debugging when you need evidence-scoped analysis with traceable execution provenance on the Base network via USDC micropayments.
Side effects
- Malformed request_json input returns validation error
- Empty or too-short JSON string (below minLength:2) rejected
- Oversized payload exceeding 65536 characters returns error
- Payment failure via x402 protocol blocks execution
- Service may return warnings if caller-supplied data cannot be fully validated
- Schema-valid but semantically ambiguous data may produce low-confidence diagnosis
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": "retrieval_failure_diagnosis_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": "retrieval_failure_diagnosis_report",
"executionId": "example-execution",
"startedAtMs": 0,
"amountAtomic": "30000",
"resultSha256": "c0fbea0da94bcaeae2a97998762dcc41125e230896c017f359594c7bd44905f5",
"serviceVersion": "1.0.0",
"settlementReference": "0x0000000000000000000000000000000000000000000000000000000000000000"
},
"artifacts": [],
"requestId": "example-request",
"executionId": "example-execution"
}
}Tags
x402
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