# Data Freshness — String Normalisation + Numeric Row Ordering

> Data Freshness — String Normalisation + 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 2026-10-22).

Normalises string fields and orders rows numerically for supplied data freshness records (age_minutes, record_count) across up to 200 rows, returning 2 independent analytical reports.

## Facts

- Endpoint: POST https://market.datapackvibe.com/x402/assistant-ib26-analytics-06-025?utm_source=zero.xyz
- Price: $0.01/call
- Payment: x402
- Status: unknown
- Last checked: 2026-10-22
- Activations on Zero: 0
- Tags: x402
- Canonical page: https://www.zero.xyz/c/data-freshness-string-normalisation-numeric-row-ordering-8785ca5a
- Structured record (JSON): https://api.zero.xyz/v1/capabilities/cap_dzT4ab4571ZhlXtDnJsqL

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 data-freshness-string-normalisation-numeric-row-ordering-8785ca5a -d '<json body>'
```

Example prompt: Can you normalise the string fields and sort the rows numerically by age_minutes for these data freshness records? I have 4 rows: row-1 for group-a on 2026-10-01 with age_minutes 10 and record_count 1, row-2 for group-b on 2026-10-02 with age_minutes 20 and record_count 2, row-3 for group-a on 2026-10-03 with age_minutes 15 and record_count 3, and row-4 for group-b on 2026-10-04 with age_minutes 30 and record_count 4.

## When to prefer this

Choose this endpoint when you have up to 200 rows of data freshness records and need both string normalisation of categorical/group fields and numeric ordering of quantitative fields (age_minutes or record_count) in a single call returning two reports. It is best when no live data lookup, external enrichment, or prediction is needed — only local analytical processing of supplied data.

## Known failure modes

- More than 200 rows supplied — exceeds maxItems limit, request rejected
- Missing required fields (row_id, dataset_group, date, age_minutes, record_count) — validation error
- Invalid date format not matching YYYY-MM-DD pattern — schema validation failure
- Invalid field enum value not matching age_minutes or record_count — request rejected
- Empty rows array (minItems 1 not met) — validation error

## How this service works

Data freshness — String normalisation + Numeric row ordering: String normalisation + Numeric row ordering for supplied data freshness records (age_minutes, record_count). 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 independent analytical reports covering: (1) normalised string values for the dataset_group and related string fields, and (2) rows ordered numerically by the chosen field (age_minutes or record_count). Results are based entirely on the supplied rows with no external lookups or predictions.

## Request schema (JSON Schema)

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

## More

- Live health (JSON, refreshed every minute): https://www.zero.xyz/c/data-freshness-string-normalisation-numeric-row-ordering-8785ca5a/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)