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Glossary Records - Unit-Label Consistency Checker
x402
Glossary Records - Unit-Label Consistency Checker is a paid API for AI agents from www.mahastrategies.com, paid per call via x402, $0.012342/call, status unknown (last checked 2026-10-16).
Validates that declared aggregate count kinds match their unit labels within a single specified source, work, study, or revision record.
Price / call$0.012342
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 an analysis report indicating whether declared aggregate count kinds are consistent with unit labels across the supplied measurements within the single specified record. Highlights specific mismatches or conflicts found. Note: this is a supplied-record analysis, not independent certification.
Example use cases
Scientific study unit audit
“I have 12 synthetic measurements from our latest study revision and I need to verify that all declared aggregate count kinds are consistent with their unit labels — can you run a unit-label consistency check on them with nonpersonalMetadata confirmed?”
Public dataset glossary validation
“We're publishing a public dataset and need to confirm there are no unit-label conflicts across 20 measurement entries before we submit — please check aggregate count kinds against unit labels and flag any mismatches.”
Single-revision record QA check
“Before we archive this revision, can you audit the 8 synthetic measurements in our glossary record to make sure every count kind declaration matches its unit label? Mark it as nonpersonalMetadata true.”
Technical details
Endpoint
POSThttps://www.mahastrategies.com/api/v1/library/glossary-records/unit-label-consistency?utm_source=zero.xyzWhen to prefer
Use this endpoint when you need to audit unit-label consistency within a single, well-defined glossary record, study, or revision — specifically checking that aggregate count kind declarations match their associated unit labels. Prefer this over generic data validation tools when working with structured scientific or measurement glossary records that require count-kind/unit-label alignment checks. Not suitable for cross-record comparisons, ownership verification, or independent certification.
Side effects
- measurements array is empty or exceeds 32 items — validation error returned
- dataClass value not in allowed enum (public, synthetic) — request rejected
- nonpersonalMetadata not set to true — request may be refused
- malformed measurement entries — parsing error or partial results
- service unavailable — HTTP 402/5xx with no analysis returned
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| dataClass | string | |
| measurements | array | |
| nonpersonalMetadata | boolean |
Tags
x402
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