# Data Freshness — String Normalisation + Exact Row Deduplication

> Data Freshness — String Normalisation + Exact Row Deduplication is a paid API for AI agents from market.datapackvibe.com, paid per call via x402, $0.01/call, status unknown (last checked 2026-10-18).

Normalises strings and deduplicates rows in supplied data freshness records (age_minutes, record_count) using local analytical engines, producing 2 independent reports for up to 200 rows.

## Facts

- Endpoint: POST https://market.datapackvibe.com/x402/assistant-ib26-analytics-06-020?utm_source=zero.xyz
- Price: $0.01/call
- Payment: x402
- Status: unknown
- Last checked: 2026-10-18
- Activations on Zero: 0
- Tags: x402
- Canonical page: https://www.zero.xyz/c/data-freshness-string-normalisation-exact-row-deduplication-d7a0e03d
- Structured record (JSON): https://api.zero.xyz/v1/capabilities/cap__QKcm1s7sfDrc464hfF90

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-exact-row-deduplication-d7a0e03d -d '<json body>'
```

Example prompt: Can you clean up my data freshness records by normalising string fields and removing exact duplicate rows? I have up to 50 rows with fields like row_id, dataset_group, date, age_minutes, and record_count — I need both the string normalisation and deduplication reports.

## When to prefer this

Choose this endpoint when you need lightweight, local string normalisation and exact row deduplication on data freshness records (age_minutes, record_count) without any external lookups, predictions, or live data access. It is ideal for pre-ingestion data cleaning, auditing ETL pipeline freshness logs, or standardising dataset group labels at low cost ($0.01 USDC for 2 reports, up to 200 rows). Not suitable for fuzzy deduplication, ML-based anomaly detection, or live data freshness monitoring.

## Known failure modes

- More than 200 rows submitted — request rejected due to maxItems constraint
- Missing required fields (row_id, dataset_group, date, age_minutes, record_count) — validation error
- Invalid date format (not YYYY-MM-DD) — schema validation failure
- Payment not included or insufficient (requires 0.01 USDC via x402) — payment error
- Null values for age_minutes or record_count may affect deduplication logic

## How this service works

Data freshness — String normalisation + Exact row deduplication: String normalisation + Exact row deduplication 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: one covering string normalisation results (standardised string fields across the input rows) and one covering exact row deduplication (identifying and removing duplicate rows), applied to up to 200 supplied data freshness records containing age_minutes and record_count fields.

## 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
  }
 }
}
```

## More

- Live health (JSON, refreshed every minute): https://www.zero.xyz/c/data-freshness-string-normalisation-exact-row-deduplication-d7a0e03d/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)