# Record Routing Analytics: String Normalisation + Date by Category Pivot

> Record Routing Analytics: 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 2026-10-24).

Performs string normalisation and date-by-category pivot analysis on supplied record routing data (accepted_records, rejected_records) for up to 200 rows, returning two independent analytical reports.

## Facts

- Endpoint: POST https://market.datapackvibe.com/x402/assistant-ib26-analytics-19-035?utm_source=zero.xyz
- Price: $0.01/call
- Payment: x402
- Status: unknown
- Last checked: 2026-10-24
- Activations on Zero: 0
- Tags: x402
- Canonical page: https://www.zero.xyz/c/record-routing-analytics-string-normalisation-date-by-category-pivot-9132cd00
- Structured record (JSON): https://api.zero.xyz/v1/capabilities/cap_WJY6Ihkl8waxL95zgHiYR

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 record-routing-analytics-string-normalisation-date-by-category-pivot-9132cd00 -d '<json body>'
```

Example prompt: Can you run string normalisation and a date-by-category pivot on my record routing data? I have rows with dataset_group, date, accepted_records, and rejected_records — up to 50 rows — and I want to analyze the accepted_records field.

## When to prefer this

Choose this endpoint when you have up to 200 rows of record routing data with accepted/rejected record counts grouped by category and date, and you need both string normalisation and a date-by-category pivot in a single low-cost call ($0.01 USDC). It is ideal for lightweight, local analytical processing of ETL or data pipeline quality metrics without requiring external data sources, live lookups, or prediction services.

## Known failure modes

- Input array exceeds 200 rows — request will be rejected or truncated
- Missing required fields (row_id, date, accepted_records, rejected_records) — validation error
- Date field not in YYYY-MM-DD format — schema validation failure
- Numeric values outside allowed range (-1e12 to 1e12) — validation error
- Empty rows array — minimum 1 item required
- Invalid field selector not in enum [accepted_records, rejected_records] — validation error

## How this service works

Record routing — String normalisation + Date by category pivot: String normalisation + Date by category pivot for supplied record routing records (accepted_records, rejected_records). 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 cleaning and standardising text fields in the supplied routing records, and (2) a date-by-category pivot table summarising the selected metric (accepted_records or rejected_records) broken down by date and dataset_group — all computed locally from 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",
     "accepted_records",
     "rejected_records"
    ],
    "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
     },
     "accepted_records": {
      "type": [
       "number",
       "null"
      ],
      "maximum": 1000000000000,
      "minimum": -1000000000000
     },
     "rejected_records": {
      "type": [
       "number",
       "null"
      ],
      "maximum": 1000000000000,
      "minimum": -1000000000000
     }
    },
    "additionalProperties": false
   },
   "default": [
    {
     "date": "2026-10-01",
     "row_id": "row-1",
     "dataset_group": "group-a",
     "accepted_records": 10,
     "rejected_records": 1
    },
    {
     "date": "2026-10-02",
     "row_id": "row-2",
     "dataset_group": "group-b",
     "accepted_records": 20,
     "rejected_records": 2
    },
    {
     "date": "2026-10-03",
     "row_id": "row-3",
     "dataset_group": "group-a",
     "accepted_records": 15,
     "rejected_records": 3
    },
    {
     "date": "2026-10-04",
     "row_id": "row-4",
     "dataset_group": "group-b",
     "accepted_records": 30,
     "rejected_records": 4
    }
   ],
   "maxItems": 200,
   "minItems": 1
  },
  "field": {
   "enum": [
    "accepted_records",
    "rejected_records"
   ],
   "type": "string",
   "default": "accepted_records"
  }
 }
}
```

## More

- Live health (JSON, refreshed every minute): https://www.zero.xyz/c/record-routing-analytics-string-normalisation-date-by-category-pivot-9132cd00/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)