# Data Reconciliation: String Normalisation + Numeric Distribution Analysis

> Data Reconciliation: String Normalisation + Numeric Distribution Analysis is a paid API for AI agents from market.datapackvibe.com, paid per call via x402, $0.01/call, status unknown (last checked 2026-10-19).

Runs two local analytical reports — string normalisation and numeric distribution — on supplied source/target reconciliation row data (up to 200 rows) for $0.01 USDC.

## Facts

- Endpoint: POST https://market.datapackvibe.com/x402/assistant-ib26-analytics-11-022?utm_source=zero.xyz
- Price: $0.01/call
- Payment: x402
- Status: unknown
- Last checked: 2026-10-19
- Activations on Zero: 0
- Tags: x402
- Canonical page: https://www.zero.xyz/c/data-reconciliation-string-normalisation-numeric-distribution-analysis-fc0e7ce5
- Structured record (JSON): https://api.zero.xyz/v1/capabilities/cap_hChDyNfONq7fCcGaj8-lA

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-numeric-distribution-analysis-fc0e7ce5 -d '<json body>'
```

Example prompt: Can you run a string normalisation and numeric distribution analysis on my reconciliation data? I have 4 rows: row-1 on 2026-10-01 in group-a with source_total 10 and target_total 1, row-2 on 2026-10-02 in group-b with source_total 20 and target_total 2, row-3 on 2026-10-03 in group-a with source_total 15 and target_total 3, row-4 on 2026-10-04 in group-b with source_total 30 and target_total 4 — analyze the source_total field.

## When to prefer this

Choose this endpoint when you need lightweight, low-cost (0.01 USDC) local analytical reports — specifically string normalisation and numeric distribution — on a batch of up to 200 data reconciliation rows with source and target totals. Ideal when no external data lookup, prediction, or professional assessment is needed and you want two bundled reports in a single call. Not suited for real-time lookups, machine learning predictions, or datasets exceeding 200 rows.

## Known failure modes

- Fewer than 1 or more than 200 rows submitted — rejected with validation error
- Row missing required fields (row_id, dataset_group, date, source_total, target_total) — row-level validation failure
- Date field not matching YYYY-MM-DD pattern — schema validation error
- Numeric values outside ±1,000,000,000,000 range — validation error
- Invalid field enum value (not source_total or target_total) — schema rejection
- Payment of 0.01 USDC not completed — 402 payment required response

## How this service works

Data reconciliation — String normalisation + Numeric distribution: String normalisation + Numeric distribution 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

Two independent analytical reports: (1) a string normalisation report covering the text/categorical fields in the supplied rows, and (2) a numeric distribution report summarising the statistical spread of the selected field (source_total or target_total) across up to 200 reconciliation rows. Reports are generated locally 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",
     "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
  },
  "field": {
   "enum": [
    "source_total",
    "target_total"
   ],
   "type": "string",
   "default": "source_total"
  }
 }
}
```

## More

- Live health (JSON, refreshed every minute): https://www.zero.xyz/c/data-reconciliation-string-normalisation-numeric-distribution-analysis-fc0e7ce5/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)