# Numeric Precision Comparisons — String Normalisation & Category Frequencies

> Numeric Precision Comparisons — String Normalisation & Category Frequencies 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).

Analyzes up to 200 rows of numeric source/transformed value pairs, producing two local reports: string normalisation analysis and category frequency counts grouped by dataset_group.

## Facts

- Endpoint: POST https://market.datapackvibe.com/x402/assistant-ib26-analytics-20-024?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-category-frequencies-49cb10f0
- Structured record (JSON): https://api.zero.xyz/v1/capabilities/cap_T07QDMtkWr3pUfvqB-jOv

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-category-frequencies-49cb10f0 -d '<json body>'
```

Example prompt: I have up to 200 rows of paired numeric readings — each with a row ID, a dataset group, a date, an original source value, and a transformed value. Can you run string normalisation and category frequency analysis on them so I can see how the numbers differ and how often each group appears?

## When to prefer this

Choose this endpoint when you have a batch of paired numeric records (source vs transformed values) grouped by category and want two quick offline reports — string normalisation and frequency counts — without needing live lookups, external data enrichment, or predictions. It is ideal for data quality audits, ETL validation, and category distribution checks on datasets up to 200 rows at very low cost ($0.01 USDC for both reports).

## Known failure modes

- Submitting more than 200 rows returns an error or truncation
- Missing required fields (row_id, dataset_group, date, source_value, transformed_value) cause validation failure
- Date fields not matching YYYY-MM-DD pattern are rejected
- Numeric values outside ±1,000,000,000,000 are rejected
- Empty rows array (minItems: 1 violated) returns an error

## How this service works

Numeric precision comparisons — String normalisation + Category frequencies: String normalisation + Category frequencies 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

Two analytical reports generated locally from supplied data: (1) a string normalisation report covering the numeric precision comparison pairs, and (2) category frequency counts showing how many rows belong to each dataset_group. No external lookups or live data are involved.

## 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
  }
 }
}
```

## More

- Live health (JSON, refreshed every minute): https://www.zero.xyz/c/numeric-precision-comparisons-string-normalisation-category-frequencies-49cb10f0/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)