# Data Reconciliation: String Normalisation + Explicit Missing-Value Fill

> Data Reconciliation: String Normalisation + Explicit Missing-Value Fill 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 string fields and fills missing values (via zero, mean, or median) in data reconciliation records comparing source_total vs target_total, returning two analytical reports for up to 200 rows.

## Facts

- Endpoint: POST https://market.datapackvibe.com/x402/assistant-ib26-analytics-11-037?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-explicit-missing-value-fill-c559a2f6
- Structured record (JSON): https://api.zero.xyz/v1/capabilities/cap_agD07-bC-NBCrmV8vMOW9

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-explicit-missing-value-fill-c559a2f6 -d '<json body>'
```

Example prompt: I have a batch of reconciliation rows with some null source_total and target_total values — can you fill the missing source_total values using the median and also normalise all the string fields? Here are my rows: [{"row_id":"r1","dataset_group":"sales","date":"2025-01-15","source_total":null,"target_total":500},{"row_id":"r2","dataset_group":"sales","date":"2025-01-16","source_total":300,"target_total":null}].

## When to prefer this

Choose this endpoint when you have up to 200 data reconciliation rows with null or inconsistent source_total/target_total values and need both string normalisation and missing-value imputation in a single call. Ideal for ETL pipelines, financial close processes, or audit workflows where you need lightweight, deterministic local cleaning without any external data lookups or ML predictions. Not suitable for datasets larger than 200 rows, live data enrichment, or prediction tasks.

## Known failure modes

- Exceeding 200 rows returns a validation error
- Missing required fields (row_id, dataset_group, date, source_total, target_total) causes rejection
- Invalid date format (not YYYY-MM-DD) fails schema validation
- Numeric values outside ±1,000,000,000,000 are rejected
- Invalid method enum value (not zero/mean/median) causes an error
- Invalid field enum value (not source_total/target_total) causes an error

## How this service works

Data reconciliation — String normalisation + Explicit missing-value fill: String normalisation + Explicit missing-value fill 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 independent analytical reports covering (1) string-normalised versions of the input reconciliation records and (2) the specified numeric field (source_total or target_total) with missing values explicitly filled using the chosen method (zero, mean, or median), applied locally without any 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"
  },
  "method": {
   "enum": [
    "zero",
    "mean",
    "median"
   ],
   "type": "string",
   "default": "median"
  }
 }
}
```

## More

- Live health (JSON, refreshed every minute): https://www.zero.xyz/c/data-reconciliation-string-normalisation-explicit-missing-value-fill-c559a2f6/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)