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Reading Plans - Unit Label Consistency Check
x402
Reading Plans - Unit Label Consistency Check is a paid API for AI agents from www.mahastrategies.com, paid per call via x402, $0.01234/call, status unknown (last checked 2026-10-16).
Validates that declared aggregate count kinds in a reading plan record match their corresponding unit labels within a single source, work, study, or revision entry.
Price / call$0.01234
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 the declared aggregate count kinds in the supplied reading plan record are consistent with the unit labels, highlighting any mismatches or discrepancies found across the provided measurements. This is a supplied-record analysis, not independent certification.
Example use cases
Study plan unit audit
“I have a study plan record with 8 measurements and I need to verify that all the declared aggregate count kinds are consistent with the unit labels — it's public data, can you run a consistency check on it?”
Revision record quality control
“Before we finalize this revision, can you check these 12 measurements in our reading plan to make sure none of the unit labels conflict with the declared count kinds? The data is synthetic.”
Source record validation pipeline
“We're processing a batch of reading plan entries and I need to validate that the unit labels match the aggregate count declarations across these 20 measurements — mark it as non-personal metadata and use the public data class.”
Technical details
Endpoint
POSThttps://www.mahastrategies.com/api/v1/library/reading-plans/unit-label-consistency?utm_source=zero.xyzWhen to prefer
Use this endpoint when you need to perform unit label consistency checks on a single reading plan, study, work, or revision record and want to validate that declared aggregate count kinds align with their unit labels. Best suited for data quality control pipelines where you supply the record for analysis rather than needing independent third-party certification.
Side effects
- Measurements array outside 1–32 item bounds returns validation error
- dataClass not in ['public','synthetic'] enum causes rejection
- nonpersonalMetadata not set to true (const) causes request failure
- Malformed measurement objects may cause parsing errors
- Missing required fields result in 400-level errors
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| dataClass | string | |
| measurements | array | |
| nonpersonalMetadata | boolean |
Tags
x402
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