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Bibliographies - Unit-Label Consistency Check
x402
Bibliographies - Unit-Label Consistency Check is a paid API for AI agents from www.mahastrategies.com, paid per call via x402, $0.012339/call, status unknown (last checked 2026-10-20).
Validates that declared aggregate count types match their unit labels within a single supplied bibliography, source, work, study, or revision record.
Price / call$0.012339
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 structured analysis report flagging any discrepancies between declared aggregate count kinds and their unit labels within the supplied record, along with details of the measurements inspected. Note: this is a supplied-record analysis, not independent certification.
Example use cases
Research manuscript pre-submission audit
“Before I submit this research paper, can you run a unit-label consistency check on my bibliography? I have 12 measurements from a public dataset, all non-personal metadata, and I need to catch any mismatches between aggregate count types and their unit labels.”
Synthetic study data QA check
“I've got a synthetic study record with 8 measurements and I need to verify that the declared aggregate count kinds all match their unit labels before we hand it off — nonpersonal metadata only, please run the unit-label consistency check.”
Single revision record validation
“Can you inspect this single revision record's unit-label consistency for me? It's public data, nonpersonal metadata, with 20 measurements — I want to know if any aggregate count declarations don't line up with their unit labels.”
Technical details
Endpoint
POSThttps://www.mahastrategies.com/api/v1/library/bibliographies/unit-label-consistency?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you need a fast, per-record unit-label consistency check on a single bibliography, study, or revision artifact — particularly for pre-submission QA, research handoff validation, or data pipeline quality gates where the data is either public or synthetic and contains no personal metadata. Not suited for independent certification or cross-record bulk audits.
Side effects
- measurements array empty or exceeds 32-item limit — validation error returned
- dataClass value not in allowed enum (public, synthetic) — schema rejection
- nonpersonalMetadata not set to true — request rejected
- Malformed measurement objects cause parsing failure
- Service unavailable returns HTTP 5xx with no report
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| dataClass | string | |
| measurements | array | |
| nonpersonalMetadata | boolean |
Tags
x402
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