# Numeric Precision Comparisons — String Normalisation + IQR Outlier Flags

> Numeric Precision Comparisons — String Normalisation + IQR Outlier Flags 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).

Performs string normalisation and IQR-based outlier flagging on up to 200 rows of supplied numeric precision comparison records (source_value vs transformed_value), producing 2 independent analytical reports locally.

## Facts

- Endpoint: POST https://market.datapackvibe.com/x402/assistant-ib26-analytics-20-032?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/numeric-precision-comparisons-string-normalisation-iqr-outlier-flags-6563352e
- Structured record (JSON): https://api.zero.xyz/v1/capabilities/cap_A3lRHY40T4DguiDnaQQNn

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 numeric-precision-comparisons-string-normalisation-iqr-outlier-flags-6563352e -d '<json body>'
```

Example prompt: Can you run IQR outlier flagging and string normalisation on these 50 rows of numeric precision comparisons — each row has a row_id, dataset_group, date, source_value, and transformed_value — and give me both analytical reports? I want to analyse the source_value field.

## When to prefer this

Choose this endpoint when you need lightweight, local statistical analysis of numeric precision comparison pairs (source vs transformed values) without any external data dependencies. It is ideal for ETL validation, data quality auditing, and anomaly detection on small-to-medium datasets (up to 200 rows) where you want both IQR outlier flagging and string normalisation in a single low-cost call. Prefer alternatives if you need real-time lookups, predictive modelling, professional assessments, or datasets larger than 200 rows.

## Known failure modes

- More than 200 rows submitted — request rejected due to maxItems constraint
- Missing required fields (row_id, dataset_group, date, source_value, or transformed_value) — validation error
- Numeric values outside allowed range (-1e12 to 1e12) — schema rejection
- Invalid date format (not YYYY-MM-DD) — pattern validation failure
- Empty rows array — minItems violation
- Invalid field selector (not 'source_value' or 'transformed_value') — enum error

## How this service works

Numeric precision comparisons — String normalisation + IQR outlier flags: String normalisation + IQR outlier flags for supplied numeric precision comparisons records (source_value, transformed_value). 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 per call: one covering string normalisation of the selected field across the supplied rows, and one providing IQR-based outlier flags identifying which rows fall outside the interquartile range. Reports are derived entirely from the supplied data using local analytical engines with no external lookups.

## Request schema (JSON Schema)

```json
{
 "type": "object",
 "properties": {
  "rows": {
   "type": "array",
   "items": {
    "type": "object",
    "required": [
     "row_id",
     "dataset_group",
     "date",
     "source_value",
     "transformed_value"
    ],
    "properties": {
     "date": {
      "type": "string",
      "default": "",
      "pattern": "^\\d{4}-\\d{2}-\\d{2}$",
      "maxLength": 10
     },
     "row_id": {
      "type": "string",
      "default": "",
      "maxLength": 80,
      "minLength": 1
     },
     "source_value": {
      "type": [
       "number",
       "null"
      ],
      "maximum": 1000000000000,
      "minimum": -1000000000000
     },
     "dataset_group": {
      "type": "string",
      "default": "",
      "maxLength": 80
     },
     "transformed_value": {
      "type": [
       "number",
       "null"
      ],
      "maximum": 1000000000000,
      "minimum": -1000000000000
     }
    },
    "additionalProperties": false
   },
   "default": [
    {
     "date": "2026-10-01",
     "row_id": "row-1",
     "source_value": 10,
     "dataset_group": "group-a",
     "transformed_value": 1
    },
    {
     "date": "2026-10-02",
     "row_id": "row-2",
     "source_value": 20,
     "dataset_group": "group-b",
     "transformed_value": 2
    },
    {
     "date": "2026-10-03",
     "row_id": "row-3",
     "source_value": 15,
     "dataset_group": "group-a",
     "transformed_value": 3
    },
    {
     "date": "2026-10-04",
     "row_id": "row-4",
     "source_value": 30,
     "dataset_group": "group-b",
     "transformed_value": 4
    }
   ],
   "maxItems": 200,
   "minItems": 1
  },
  "field": {
   "enum": [
    "source_value",
    "transformed_value"
   ],
   "type": "string",
   "default": "source_value"
  }
 }
}
```

## More

- Live health (JSON, refreshed every minute): https://www.zero.xyz/c/numeric-precision-comparisons-string-normalisation-iqr-outlier-flags-6563352e/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)