# Join Coverage Analytics: Numeric Row Ordering + Date-by-Category Pivot

> Join Coverage Analytics: Numeric Row Ordering + Date-by-Category Pivot is a paid API for AI agents from market.datapackvibe.com, paid per call via x402, $0.01/call, status unknown (last checked 2026-10-15).

Generates two analytical reports on join coverage data — numeric row ordering and a date-by-category pivot — for up to 200 supplied records of matched_rows and source_rows.

## Facts

- Endpoint: POST https://market.datapackvibe.com/x402/assistant-ib26-analytics-09-122?utm_source=zero.xyz
- Price: $0.01/call
- Payment: x402
- Status: unknown
- Last checked: 2026-10-15
- Activations on Zero: 0
- Tags: x402
- Canonical page: https://www.zero.xyz/c/join-coverage-analytics-numeric-row-ordering-date-by-category-pivot-758de57b
- Structured record (JSON): https://api.zero.xyz/v1/capabilities/cap_6E-HC5ZcDZ9NWOjLWvxrK

Status and success rate cover calls made through Zero and Zero's own probes. Third-party monitors may report differently.

## How to call it through Zero

Zero handles the 402 payment challenge and records the run. With the Zero CLI installed (`npm i -g @zeroxyz/cli`):

```sh
zero fetch --capability join-coverage-analytics-numeric-row-ordering-date-by-category-pivot-758de57b -d '<json body>'
```

Example prompt: I have join coverage records for two dataset groups — group-a and group-b — with matched_rows and source_rows across October 2026. Can you run numeric row ordering and a date-by-category pivot on matched_rows for these 4 rows and give me both reports?

## When to prefer this

Choose this endpoint when you have tabular join coverage data (matched_rows, source_rows) with date and category labels and need both a numeric ordering view and a date-by-category pivot in a single low-cost call. Ideal for data engineers auditing ETL pipelines, monitoring join completeness over time, or preparing coverage summaries by dataset group. Not suitable for live data lookups, predictive analytics, or datasets exceeding 200 rows.

## Known failure modes

- Missing required fields (row_id, dataset_group, date, matched_rows, source_rows) cause validation errors
- Date values not matching YYYY-MM-DD pattern are rejected
- Submitting more than 200 rows exceeds the maxItems limit
- Empty rows array (minItems: 1 violated) returns an error
- Invalid field enum value (not matched_rows or source_rows) causes rejection
- Null numeric values may affect ordering and pivot completeness

## How this service works

Join coverage — Numeric row ordering + Date by category pivot: Numeric row ordering + Date by category pivot 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 analytical reports: (1) a numeric row ordering of the supplied join coverage records, and (2) a date-by-category pivot table showing the selected metric (matched_rows or source_rows) broken down by date and dataset_group. Both reports are computed locally from the supplied data with no external lookups.

## Request schema (JSON Schema)

```json
{
 "type": "object",
 "properties": {
  "rows": {
   "type": "array",
   "items": {
    "type": "object",
    "required": [
     "row_id",
     "dataset_group",
     "date",
     "matched_rows",
     "source_rows"
    ],
    "properties": {
     "date": {
      "type": "string",
      "default": "",
      "pattern": "^\\d{4}-\\d{2}-\\d{2}$",
      "maxLength": 10
     },
     "row_id": {
      "type": "string",
      "default": "",
      "maxLength": 80,
      "minLength": 1
     },
     "source_rows": {
      "type": [
       "number",
       "null"
      ],
      "maximum": 1000000000000,
      "minimum": -1000000000000
     },
     "matched_rows": {
      "type": [
       "number",
       "null"
      ],
      "maximum": 1000000000000,
      "minimum": -1000000000000
     },
     "dataset_group": {
      "type": "string",
      "default": "",
      "maxLength": 80
     }
    },
    "additionalProperties": false
   },
   "default": [
    {
     "date": "2026-10-01",
     "row_id": "row-1",
     "source_rows": 1,
     "matched_rows": 10,
     "dataset_group": "group-a"
    },
    {
     "date": "2026-10-02",
     "row_id": "row-2",
     "source_rows": 2,
     "matched_rows": 20,
     "dataset_group": "group-b"
    },
    {
     "date": "2026-10-03",
     "row_id": "row-3",
     "source_rows": 3,
     "matched_rows": 15,
     "dataset_group": "group-a"
    },
    {
     "date": "2026-10-04",
     "row_id": "row-4",
     "source_rows": 4,
     "matched_rows": 30,
     "dataset_group": "group-b"
    }
   ],
   "maxItems": 200,
   "minItems": 1
  },
  "field": {
   "enum": [
    "matched_rows",
    "source_rows"
   ],
   "type": "string",
   "default": "matched_rows"
  }
 }
}
```

## More

- Live health (JSON, refreshed every minute): https://www.zero.xyz/c/join-coverage-analytics-numeric-row-ordering-date-by-category-pivot-758de57b/health.json
- [Zero catalog index](https://www.zero.xyz/llms.txt)
- [Other services from market.datapackvibe.com](https://www.zero.xyz/host/market.datapackvibe.com/llms.txt)