# Data Reconciliation: String Normalisation + Category Frequencies

> Data Reconciliation: String Normalisation + Category Frequencies is a paid API for AI agents from market.datapackvibe.com, paid per call via x402, $0.01/call, status unknown (last checked 2026-10-15).

Analyzes up to 200 data reconciliation rows (source_total vs target_total) by producing two reports: string normalization of text fields and category frequency distributions across dataset groups.

## Facts

- Endpoint: POST https://market.datapackvibe.com/x402/assistant-ib26-analytics-11-024?utm_source=zero.xyz
- Price: $0.01/call
- Payment: x402
- Status: unknown
- Last checked: 2026-10-15
- Activations on Zero: 0
- Tags: x402
- Canonical page: https://www.zero.xyz/c/data-reconciliation-string-normalisation-category-frequencies-46dbf75b
- Structured record (JSON): https://api.zero.xyz/v1/capabilities/cap_tp2UQm8OqzbElaweSd-7S

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-reconciliation-string-normalisation-category-frequencies-46dbf75b -d '<json body>'
```

Example prompt: Can you run string normalization and category frequency analysis on my reconciliation data? I have rows with row_id, date (like 2026-10-01), dataset_group (e.g. 'group-a' or 'group-b'), source_total, and target_total — about 50 rows total — and I need both reports in one call.

## When to prefer this

Choose this endpoint when you need both string normalization and category frequency analysis on reconciliation data (source vs target totals) in a single API call for up to 200 rows. It is ideal for data quality workflows that require text field cleanup and group distribution summaries together without any external data lookups or predictions. If you only need one report type or have more than 200 rows, consider alternative endpoints.

## Known failure modes

- Submitting more than 200 rows returns a validation error
- Missing required fields (row_id, date, source_total, target_total, dataset_group) cause row-level rejection
- Dates not matching YYYY-MM-DD pattern fail schema validation
- Numeric values outside ±1,000,000,000,000 are rejected
- Empty rows array returns an error

## How this service works

Data reconciliation — String normalisation + Category frequencies: String normalisation + Category frequencies for supplied data reconciliation records (source_total, target_total). 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 analytical reports for the submitted rows: (1) a string normalization report that standardizes and cleans text fields such as row_id and dataset_group, and (2) a category frequency report showing the count and distribution of each distinct dataset_group value across all submitted rows. No external lookups or predictions are made — all analysis is performed locally on the supplied data.

## Request schema (JSON Schema)

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

## More

- Live health (JSON, refreshed every minute): https://www.zero.xyz/c/data-reconciliation-string-normalisation-category-frequencies-46dbf75b/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)