# Data Reconciliation: String Normalisation + Equal-Width Histogram

> Data Reconciliation: String Normalisation + Equal-Width Histogram 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 on reconciliation data: string normalisation of row fields and an equal-width histogram over a chosen numeric column (source_total or target_total), for up to 200 rows.

## Facts

- Endpoint: POST https://market.datapackvibe.com/x402/assistant-ib26-analytics-11-031?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-equal-width-histogram-61163a60
- Structured record (JSON): https://api.zero.xyz/v1/capabilities/cap_Z4dLQRAesxOzaseDEjkBy

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-equal-width-histogram-61163a60 -d '<json body>'
```

Example prompt: I have 50 reconciliation rows with source_total and target_total values across two dataset groups from October 2026 — can you normalize the string fields and generate a 10-bin equal-width histogram over the source_total column?

## When to prefer this

Choose this endpoint when you need fast, local analytical processing of reconciliation data combining string normalization with histogram analysis in a single call. Ideal when you have up to 200 rows of source/target reconciliation records and want both data quality cleanup and a frequency distribution summary without any external lookups, live predictions, or professional assessments. Good value when you need two distinct reports for a flat $0.01 USDC fee.

## Known failure modes

- Rows array empty or missing — validation error returned
- More than 200 rows submitted — rejected with maxItems error
- Invalid date format (not YYYY-MM-DD) — schema validation failure
- bin count outside 1–30 range — rejected with min/max constraint error
- source_total or target_total values outside ±1,000,000,000,000 — rejected
- field value not one of 'source_total' or 'target_total' — enum validation error
- Missing required row fields (row_id, dataset_group, date) — validation failure

## How this service works

Data reconciliation — String normalisation + Equal-width histogram: String normalisation + Equal-width histogram 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: (1) a string normalization report cleaning row_id, dataset_group, and date fields across all submitted rows, and (2) an equal-width histogram report showing bin edges, counts, and frequency distribution for the selected numeric column (source_total or target_total), across up to 200 rows. No external lookups or predictions are made.

## Request schema (JSON Schema)

```json
{
 "type": "object",
 "properties": {
  "bins": {
   "type": "integer",
   "default": 5,
   "maximum": 30,
   "minimum": 1
  },
  "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-equal-width-histogram-61163a60/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)