# Data Reconciliation — String Normalisation + Exact Row Deduplication

> Data Reconciliation — String Normalisation + Exact Row Deduplication 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).

Normalises strings and deduplicates exact rows across supplied data reconciliation records comparing source and target totals, returning 2 analytical reports for up to 200 rows.

## Facts

- Endpoint: POST https://market.datapackvibe.com/x402/assistant-ib26-analytics-11-020?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-exact-row-deduplication-a158badb
- Structured record (JSON): https://api.zero.xyz/v1/capabilities/cap_FEiMs_F0Z-zNMtQ2uqNuJ

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-exact-row-deduplication-a158badb -d '<json body>'
```

Example prompt: Can you reconcile these rows for me — I have source and target totals for groups A and B across dates in October 2026, up to 200 rows, and I need string normalisation plus exact row deduplication across both datasets? Here are the rows: row-1 on 2026-10-01, group-a, source 10, target 1; row-2 on 2026-10-02, group-b, source 20, target 2.

## When to prefer this

Choose this endpoint when you need to clean and deduplicate a batch of up to 200 reconciliation records comparing source and target numeric totals, especially when string normalisation is also required. It is best suited for offline, supplied-data analysis with no need for live lookups, predictions, or external data enrichment. Prefer it over general-purpose deduplication tools when your data is structured as date-grouped, keyed rows with source and target totals, such as financial ledger reconciliation or ETL validation workflows.

## Known failure modes

- More than 200 rows submitted — request rejected due to maxItems constraint
- Missing required fields (row_id, dataset_group, date, source_total, target_total) — validation error
- Date field does not match YYYY-MM-DD pattern — schema validation failure
- source_total or target_total outside ±1,000,000,000,000 range — validation error
- Empty rows array (minItems: 1 not met) — validation error
- row_id exceeds 80 characters — validation error

## How this service works

Data reconciliation — String normalisation + Exact row deduplication: String normalisation + Exact row deduplication 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 2 independent analytical reports: (1) a string normalisation report showing how string fields were standardised across the submitted rows, and (2) an exact row deduplication report identifying which rows are exact duplicates within the supplied dataset. Reports are computed locally with no external lookups or predictions, covering up to 200 rows of source_total / target_total reconciliation 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-exact-row-deduplication-a158badb/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)