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Document Readability Inputs — Record Quality Audit + String Normalisation
x402
Document Readability Inputs — Record Quality Audit + String Normalisation is a paid API for AI agents from market.datapackvibe.com, paid per call via x402, $0.01/call, status unknown (last checked 2026-10-26).
Audits data quality and normalises strings for up to 200 rows of document readability data (sentence_count, word_count), producing two independent analytical reports.
Price / call$0.01
Success—
StatusUnknown
How this service works
Document readability inputs — Record quality audit + String normalisation: Record quality audit + String normalisation for supplied document readability inputs records (sentence_count, word_count). Up to 200 rows; 2 independent reports using shared local analytical engines. No live lookup, prediction, professional assessment or external action. 0.01 USDC includes 2 supplied-data reports for up to 200 rows.
Output
Two independent reports covering: (1) a record quality audit flagging issues such as missing values, out-of-range numbers, malformed dates, and duplicate row IDs across the supplied readability rows; and (2) a string normalisation report showing standardised forms of string fields like document_group. All analysis is performed locally with no external lookups or predictions.
Example use cases
Pre-pipeline readability data validation
“Before I feed my document stats into the NLP pipeline, can you audit the quality of these 50 readability rows — check for bad values in sentence_count and word_count and normalise the document_group strings? Groups are things like 'Group A', 'group-a', 'GROUP A' and I want them cleaned up.”
Research corpus quality check
“I've got 120 rows of readability measurements from our research corpus spanning 2025-01-01 to 2025-06-30 across three document groups. Can you run a quality audit to find any missing or out-of-range sentence_count or word_count values, and normalise the group name strings?”
Content team writing metrics audit
“Our content team logged word counts and sentence counts for 80 articles across two groups — 'blog-posts' and 'white-papers' — between 2026-03-01 and 2026-03-31. Can you audit those records for quality issues and normalise the string fields so we know what to fix before reporting?”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-ib26-writing-07-001?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you need a combined record quality audit and string normalisation pass on document readability data (sentence_count, word_count) in a single cheap call, without needing live lookups, predictions, or external integrations. It is well-suited for pre-processing steps before NLP pipelines, research data cleaning, or content analytics workflows where you want to validate data integrity and standardise group labels across up to 200 rows at minimal cost.
Side effects
- Empty rows array or fewer than 1 row — validation error
- More than 200 rows submitted — rejected with max items error
- Missing required fields (row_id, document_group, date, sentence_count, word_count) on any row — schema validation failure
- Malformed date format (not YYYY-MM-DD) — pattern validation error
- row_id exceeding 80 characters — field length error
- Payment not provided or insufficient USDC — x402 payment required response
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| rows | array |
Tags
x402
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