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Edition Records - Unit-Label Consistency Check
x402
Edition Records - Unit-Label Consistency Check is a paid API for AI agents from www.mahastrategies.com, paid per call via x402, $0.012341/call, status unknown (last checked 2026-10-16).
Validates that declared aggregate count kinds in an edition record are consistent with their associated unit labels within a single source, work, study, or revision.
Price / call$0.012341
Success—
StatusUnknown
How this service works
Check declared aggregate count kinds against unit labels in one exact source/work/study/revision Supplied-record analysis, not independent certification. Full contract at the free describe URL.
Output
Returns a consistency analysis report indicating whether declared aggregate count kinds align with unit labels across the supplied measurements in the edition record, including details of any mismatches or inconsistencies found. This is a supplied-record analysis, not an independent certification.
Example use cases
Research dataset unit audit
“I have a study revision with 12 measurements and I need to verify that all the declared aggregate count kinds match their unit labels correctly — it's public data, non-personal metadata only. Can you run the unit-label consistency check and tell me what's inconsistent?”
Synthetic data quality gate
“Before we publish this synthetic edition, can you check the unit-label consistency across these 8 measurements to make sure the count kinds line up with their declared units? Mark it as synthetic and non-personal.”
Library edition pre-submission check
“I'm about to submit this library edition record with 20 measurements — can you validate that none of the aggregate count types conflict with their unit labels so I can catch any declaration errors before it goes in? Data class is public, all non-personal metadata.”
Technical details
Endpoint
POSThttps://www.mahastrategies.com/api/v1/library/edition-records/unit-label-consistency?utm_source=zero.xyzWhen to prefer
Use this endpoint when you need to validate that declared aggregate count kinds are internally consistent with unit labels within a single, well-defined edition record (source, work, study, or revision). It is best suited for pre-submission quality checks on library or research edition metadata with up to 32 measurements. It performs supplied-record analysis rather than independent certification, so choose it when you own or control the record being validated and want fast, automated consistency checking rather than authoritative audit.
Side effects
- Measurements array empty or exceeds 32-item limit — validation error returned
- nonpersonalMetadata not set to true — request rejected
- Invalid dataClass value outside 'public' or 'synthetic' enum — schema validation failure
- Malformed measurement entries — partial or failed analysis
- Service unavailable — no consistency result returned
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| dataClass | string | |
| measurements | array | |
| nonpersonalMetadata | boolean |
Tags
x402
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