# Join Coverage Analytics: Category Frequencies + Date-by-Category Pivot

> Join Coverage Analytics: Category Frequencies + 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 2026-10-19).

Generates two analytical reports for join coverage data — category frequency distribution and a date-by-category pivot — for up to 200 supplied rows of matched_rows/source_rows metrics.

## Facts

- Endpoint: POST https://market.datapackvibe.com/x402/assistant-ib26-analytics-09-110?utm_source=zero.xyz
- Price: $0.01/call
- Payment: x402
- Status: unknown
- Last checked: 2026-10-19
- Activations on Zero: 0
- Tags: x402
- Canonical page: https://www.zero.xyz/c/join-coverage-analytics-category-frequencies-date-by-category-pivot-2b25fe7c
- Structured record (JSON): https://api.zero.xyz/v1/capabilities/cap_vnpWngv4Rjfdd3ZJZi1gN

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-category-frequencies-date-by-category-pivot-2b25fe7c -d '<json body>'
```

Example prompt: Can you run a join coverage analysis on my data — I have 4 rows with dataset groups group-a and group-b, dates from 2026-10-01 to 2026-10-04, and matched_rows values of 10, 20, 15, 30 with source_rows of 1, 2, 3, 4 — give me the category frequencies and a date-by-category pivot for the matched_rows field?

## When to prefer this

Choose this endpoint when you need quick dual-report analytics (frequency + pivot) on join coverage data without setting up a local analytics environment. Ideal for data engineers auditing ETL pipeline join rates across multiple dataset groups and dates, especially when you want both a categorical frequency breakdown and a time-series pivot in a single API call for up to 200 rows at very low cost.

## Known failure modes

- Fewer than 1 row or more than 200 rows supplied — rejected by minItems/maxItems validation
- Invalid date format (not YYYY-MM-DD) causes schema validation failure
- Numeric values outside ±1,000,000,000,000 range rejected
- Missing required fields (row_id, dataset_group, date, matched_rows, source_rows) cause validation errors
- Invalid field enum value (not 'matched_rows' or 'source_rows') rejected
- Payment failure or insufficient USDC balance prevents call execution

## How this service works

Join coverage — Category frequencies + Date by category pivot: Category frequencies + 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 category frequency table showing how often each dataset_group appears and its aggregate statistics for the selected field (matched_rows or source_rows), and (2) a date-by-category pivot table cross-tabulating dates against dataset groups for the chosen numeric metric — all computed locally from the supplied rows 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-category-frequencies-date-by-category-pivot-2b25fe7c/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)