# Data Catalog Coverage — String Normalisation + Date by Category Pivot

> Data Catalog Coverage — String Normalisation + 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 2026-10-23).

Runs two analytical reports on data catalog coverage records: string normalisation of field values and a date-by-category pivot, for up to 200 rows.

## Facts

- Endpoint: POST https://market.datapackvibe.com/x402/assistant-ib26-analytics-21-035?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/data-catalog-coverage-string-normalisation-date-by-category-pivot-f68f7f17
- Structured record (JSON): https://api.zero.xyz/v1/capabilities/cap_JLGWw9cwa-Q4GBTqThdtV

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-catalog-coverage-string-normalisation-date-by-category-pivot-f68f7f17 -d '<json body>'
```

Example prompt: Can you run string normalisation and a date-by-category pivot on my data catalog coverage records? I have rows with dataset_group, date, documented_fields, and total_fields — up to about 50 rows — and I want the analysis focused on the documented_fields column.

## When to prefer this

Choose this endpoint when you need two bundled analytical operations — string normalisation and a date-by-category pivot — applied to data catalog coverage records (documented_fields, total_fields) in a single low-cost call. It is ideal for data governance or catalog quality workflows where you supply your own rows and need fast, local computation without external data lookups, predictions, or professional assessments. Prefer it over general analytics APIs when your domain is specifically data catalog coverage metrics grouped by dataset_group and date.

## Known failure modes

- Fewer than 1 row or more than 200 rows supplied — request rejected
- Missing required fields (row_id, dataset_group, date, documented_fields, total_fields) in any row — validation error
- Invalid date format (must match YYYY-MM-DD) — schema validation failure
- Numeric values outside allowed range (±1,000,000,000,000) — rejection
- Invalid field enum value (must be 'documented_fields' or 'total_fields') — schema error
- Payment of 0.01 USDC not completed — request blocked

## How this service works

Data catalog coverage — String normalisation + Date by category pivot: String normalisation + Date by category pivot for supplied data catalog coverage records (documented_fields, total_fields). 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 string normalisation report that cleans and standardises text values in the specified field across the supplied catalog coverage rows, and (2) a date-by-category pivot report that aggregates the chosen numeric measure (documented_fields or total_fields) across dataset_group categories and dates. Both reports are computed locally against 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",
     "documented_fields",
     "total_fields"
    ],
    "properties": {
     "date": {
      "type": "string",
      "default": "",
      "pattern": "^\\d{4}-\\d{2}-\\d{2}$",
      "maxLength": 10
     },
     "row_id": {
      "type": "string",
      "default": "",
      "maxLength": 80,
      "minLength": 1
     },
     "total_fields": {
      "type": [
       "number",
       "null"
      ],
      "maximum": 1000000000000,
      "minimum": -1000000000000
     },
     "dataset_group": {
      "type": "string",
      "default": "",
      "maxLength": 80
     },
     "documented_fields": {
      "type": [
       "number",
       "null"
      ],
      "maximum": 1000000000000,
      "minimum": -1000000000000
     }
    },
    "additionalProperties": false
   },
   "default": [
    {
     "date": "2026-10-01",
     "row_id": "row-1",
     "total_fields": 1,
     "dataset_group": "group-a",
     "documented_fields": 10
    },
    {
     "date": "2026-10-02",
     "row_id": "row-2",
     "total_fields": 2,
     "dataset_group": "group-b",
     "documented_fields": 20
    },
    {
     "date": "2026-10-03",
     "row_id": "row-3",
     "total_fields": 3,
     "dataset_group": "group-a",
     "documented_fields": 15
    },
    {
     "date": "2026-10-04",
     "row_id": "row-4",
     "total_fields": 4,
     "dataset_group": "group-b",
     "documented_fields": 30
    }
   ],
   "maxItems": 200,
   "minItems": 1
  },
  "field": {
   "enum": [
    "documented_fields",
    "total_fields"
   ],
   "type": "string",
   "default": "documented_fields"
  }
 }
}
```

## More

- Live health (JSON, refreshed every minute): https://www.zero.xyz/c/data-catalog-coverage-string-normalisation-date-by-category-pivot-f68f7f17/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)