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SignalHarness RAG Context Coverage Report
x402
SignalHarness RAG Context Coverage Report is a paid API for AI agents from signalharness.ai, paid per call via x402, $0.025/call, status unknown (last checked 2026-09-13).
Analyzes a caller-supplied JSON payload to generate a coverage report assessing how well RAG (Retrieval-Augmented Generation) context is represented across a given dataset or query set.
Price / call$0.025
Success—
StatusUnknown
How this service works
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Output
Returns a JSON object containing an analysis_json field with the coverage assessment, a list of warnings about data reliability, an evidence_scope label indicating the data was caller-supplied, and a full execution receipt including payment details, latency, and a SHA-256 hash of the result for verification.
Example use cases
Audit RAG pipeline before production launch
“Before I ship my new RAG-powered support bot, can you run a context coverage report on this JSON of test queries and retrieved chunks to see if there are any major gaps or topics that aren't being retrieved well?”
Diagnose poor answer quality in chatbot
“My chatbot is giving weak answers on certain topics — can you analyze this RAG context coverage report JSON I've put together from last week's queries and tell me which areas have insufficient retrieval coverage?”
Benchmark knowledge base completeness
“I just updated my knowledge base and want to verify it covers all the topics in our FAQ. Here's the JSON with the FAQ questions and the retrieved context snippets — can you generate a coverage report so I can see what's missing?”
Technical details
Endpoint
POSThttps://signalharness.ai/api/agent/services/rag_context_coverage_report/invokeWhen to prefer
Choose this endpoint when you need a structured, automated audit of RAG retrieval coverage and want a machine-readable report rather than a manual review. It is especially useful when you have a well-formed JSON payload of queries and retrieved context ready to submit and need a fast, per-call diagnostic without committing to a recurring service. Prefer this over general LLM-based analysis when you want a consistent, receipted, verifiable output with a SHA-256 result hash.
Side effects
- Invalid or malformed request_json causes a parsing error
- request_json shorter than 2 characters or exceeding 65536 characters triggers a validation error
- Caller-supplied data that doesn't conform to expected RAG schema may produce low-confidence or empty analysis
- Payment failure on the x402 protocol results in no execution
- Network or service timeout yields no 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": "rag_context_coverage_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": "rag_context_coverage_report",
"executionId": "example-execution",
"startedAtMs": 0,
"amountAtomic": "25000",
"resultSha256": "00e45edd7a4b1e3381d57f8fd589a60967262984626fe627bbcfe494749a3395",
"serviceVersion": "1.0.0",
"settlementReference": "0x0000000000000000000000000000000000000000000000000000000000000000"
},
"artifacts": [],
"requestId": "example-request",
"executionId": "example-execution"
}
}Tags
x402
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