# Join Coverage Analytics — Daily Sum Series + Adjacent Row Changes

> Join Coverage Analytics — Daily Sum Series + Adjacent Row Changes 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 two analytical reports (daily sum series and adjacent row changes) over supplied join coverage data (matched_rows and source_rows) for up to 200 rows.

## Facts

- Endpoint: POST https://market.datapackvibe.com/x402/assistant-ib26-analytics-09-137?utm_source=zero.xyz
- Price: $0.01/call
- Payment: x402
- Status: unknown
- Last checked: 2026-10-23
- Activations on Zero: 0
- Tags: x402
- Canonical page: https://www.zero.xyz/c/join-coverage-analytics-daily-sum-series-adjacent-row-changes-c2eaf780
- Structured record (JSON): https://api.zero.xyz/v1/capabilities/cap_2L87jDc6BQVb2tNCZ1vBq

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-daily-sum-series-adjacent-row-changes-c2eaf780 -d '<json body>'
```

Example prompt: I have a list of daily join coverage records with matched_rows and source_rows for two dataset groups — can you run the daily sum series and adjacent row changes reports on the matched_rows field for these rows: 2026-10-01 group-a matched=10 source=1, 2026-10-02 group-b matched=20 source=2, 2026-10-03 group-a matched=15 source=3?

## When to prefer this

Choose this endpoint when you have pre-assembled join coverage row data (matched_rows and source_rows) and need both a daily aggregation time series and adjacent-row change detection in a single paid call. It is ideal for data pipeline audits, ETL quality monitoring, and batch analytics on join results where you supply all data locally and need no external lookups or predictions. The bundled two-report model makes it cost-efficient compared to calling separate analytics services.

## Known failure modes

- Fewer than 1 row supplied — minimum 1 row required
- More than 200 rows supplied — exceeds maxItems limit
- Invalid date format (must match YYYY-MM-DD pattern)
- Missing required fields (row_id, dataset_group, date, matched_rows, source_rows)
- Invalid field enum value — must be 'matched_rows' or 'source_rows'
- Numeric values outside allowed range (-1,000,000,000,000 to 1,000,000,000,000)
- Payment failure — 0.01 USDC required per call via x402 protocol

## How this service works

Join coverage — Daily sum series + Adjacent row changes: Daily sum series + Adjacent row changes 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

Two analytical reports: (1) a daily sum series showing aggregated values of the selected field (matched_rows or source_rows) grouped by date, and (2) an adjacent row changes report showing differences between consecutive rows for the selected field. Both reports are derived entirely from the supplied input 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-daily-sum-series-adjacent-row-changes-c2eaf780/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)