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Join Coverage Analytics — String Normalisation + Base-100 Series
x402
Join Coverage Analytics — String Normalisation + Base-100 Series is a paid API for AI agents from market.datapackvibe.com, paid per call via x402, $0.01/call, status unknown (last checked 2026-10-23).
Computes string normalisation and Base-100 index series for join coverage metrics (matched_rows, source_rows) across up to 200 supplied data rows, returning two independent analytical reports.
Price / call$0.01
Success—
StatusUnknown
How this service works
Join coverage — String normalisation + Base-100 series: String normalisation + Base-100 series for supplied join coverage records (matched_rows, source_rows). 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
Returns two independent analytical reports per call: (1) string-normalised values for the selected field (matched_rows or source_rows) across all supplied rows, and (2) a Base-100 index series anchored at the specified base_index row. Reports are computed locally from supplied data only — no external lookups or predictions are performed.
Example use cases
ETL pipeline join quality audit
“I have daily join coverage records for two dataset groups across the past week — matched_rows and source_rows for each day. Can you run string normalisation and a Base-100 index series on the matched_rows field, starting from base index 0, so I can see how join quality has trended?”
Cross-group coverage benchmarking
“I want to benchmark join coverage across my group-a and group-b datasets. I have about 50 rows with date, matched_rows, and source_rows. Run the string normalisation and Base-100 series on source_rows with base index 0 and give me the two reports.”
Data pipeline regression detection
“After a schema change last month, I suspect our join coverage dropped. I have 120 rows of matched_rows and source_rows data by date and dataset group. Can you compute the string normalisation and Base-100 indexed series on matched_rows starting at index 0 so I can spot where the regression happened?”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-ib26-analytics-09-030?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you have tabular join coverage data (matched_rows, source_rows) keyed by date and dataset group and need both string normalisation and a Base-100 time-indexed series in a single low-cost call. It is ideal for data engineers auditing ETL pipeline join quality, comparing coverage across groups, or detecting regressions over time — especially when you want offline, supplied-data-only computation with no external dependencies. At $0.01 USDC for two reports on up to 200 rows, it is cost-effective for batch analytical passes over moderate-sized datasets.
Side effects
- Rows array empty or missing — minimum 1 row required
- More than 200 rows submitted — maxItems limit exceeded
- Invalid date format (must match YYYY-MM-DD pattern)
- row_id missing or empty string — required field
- matched_rows or source_rows out of allowed numeric range (±1,000,000,000,000)
- base_index out of range (0–199) or exceeds actual row count
- field enum value not one of matched_rows or source_rows — validation error
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| rows | array | |
| field | string | |
| base_index | integer |
Tags
x402
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