# Time Series Completeness — Record Quality Audit + Numeric Row Ordering

> Time Series Completeness — Record Quality Audit + Numeric Row Ordering is a paid API for AI agents from market.datapackvibe.com, paid per call via x402, $0.01/call, status unknown (last checked 2026-10-28).

Audits record quality and orders rows numerically for time series completeness data (observed_points vs expected_points), producing two independent analytical reports for up to 200 rows.

## Facts

- Endpoint: POST https://market.datapackvibe.com/x402/assistant-ib26-analytics-16-007?utm_source=zero.xyz
- Price: $0.01/call
- Payment: x402
- Status: unknown
- Last checked: 2026-10-28
- Activations on Zero: 0
- Tags: x402
- Canonical page: https://www.zero.xyz/c/time-series-completeness-record-quality-audit-numeric-row-ordering-6e49dcd9
- Structured record (JSON): https://api.zero.xyz/v1/capabilities/cap_qPB-7zr1dgEopzeOx49ya

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 time-series-completeness-record-quality-audit-numeric-row-ordering-6e49dcd9 -d '<json body>'
```

Example prompt: Can you audit the quality of these time series completeness records and also rank the rows numerically by observed_points? I have 4 rows: row-1 for 2026-10-01 in group-a with 10 observed and 1 expected, row-2 for 2026-10-02 in group-b with 20 observed and 2 expected, row-3 for 2026-10-03 in group-a with 15 observed and 3 expected, and row-4 for 2026-10-04 in group-b with 30 observed and 4 expected.

## When to prefer this

Choose this endpoint when you need both a record quality audit AND numeric row ordering in a single call for time series completeness data with observed/expected point counts. It is ideal for data pipeline monitoring workflows where you want to validate completeness records and rank them by volume in one step, without needing live lookups, predictions, or external data enrichment. Prefer alternatives if you only need a single report type or are working with non-completeness time series data.

## Known failure modes

- More than 200 rows submitted — request exceeds maximum row limit
- Missing required fields (row_id, date, observed_points, expected_points) cause validation rejection
- Invalid date format (not YYYY-MM-DD) triggers schema error
- Null values for both observed_points and expected_points may reduce audit usefulness
- Payment not included or insufficient USDC causes 402 rejection

## How this service works

Time series completeness — Record quality audit + Numeric row ordering: Record quality audit + Numeric row ordering for supplied time series completeness records (observed_points, expected_points). 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: (1) a record quality audit flagging issues in the submitted time series completeness rows (e.g. nulls, anomalies, malformed entries), and (2) a numeric row ordering ranking the rows by the selected field (observed_points or expected_points). Both reports are computed locally on 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",
     "observed_points",
     "expected_points"
    ],
    "properties": {
     "date": {
      "type": "string",
      "default": "",
      "pattern": "^\\d{4}-\\d{2}-\\d{2}$",
      "maxLength": 10
     },
     "row_id": {
      "type": "string",
      "default": "",
      "maxLength": 80,
      "minLength": 1
     },
     "dataset_group": {
      "type": "string",
      "default": "",
      "maxLength": 80
     },
     "expected_points": {
      "type": [
       "number",
       "null"
      ],
      "maximum": 1000000000000,
      "minimum": -1000000000000
     },
     "observed_points": {
      "type": [
       "number",
       "null"
      ],
      "maximum": 1000000000000,
      "minimum": -1000000000000
     }
    },
    "additionalProperties": false
   },
   "default": [
    {
     "date": "2026-10-01",
     "row_id": "row-1",
     "dataset_group": "group-a",
     "expected_points": 1,
     "observed_points": 10
    },
    {
     "date": "2026-10-02",
     "row_id": "row-2",
     "dataset_group": "group-b",
     "expected_points": 2,
     "observed_points": 20
    },
    {
     "date": "2026-10-03",
     "row_id": "row-3",
     "dataset_group": "group-a",
     "expected_points": 3,
     "observed_points": 15
    },
    {
     "date": "2026-10-04",
     "row_id": "row-4",
     "dataset_group": "group-b",
     "expected_points": 4,
     "observed_points": 30
    }
   ],
   "maxItems": 200,
   "minItems": 1
  },
  "field": {
   "enum": [
    "observed_points",
    "expected_points"
   ],
   "type": "string",
   "default": "observed_points"
  }
 }
}
```

## More

- Live health (JSON, refreshed every minute): https://www.zero.xyz/c/time-series-completeness-record-quality-audit-numeric-row-ordering-6e49dcd9/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)