# Field Survey Coverage String Normalisation + Date by Category Pivot

> Field Survey Coverage String Normalisation + Date by Category Pivot is a paid API for AI agents from market.datapackvibe.com, paid per call via x402, $0.01/call, status unknown (last checked 2026-10-25).

Normalises station group strings and produces a date-by-category pivot table from field survey coverage records (surveyed_m2, target_m2) — two analytical reports for up to 200 rows.

## Facts

- Endpoint: POST https://market.datapackvibe.com/x402/assistant-ib26-weather-22-035?utm_source=zero.xyz
- Price: $0.01/call
- Payment: x402
- Status: unknown
- Last checked: 2026-10-25
- Activations on Zero: 0
- Tags: x402
- Canonical page: https://www.zero.xyz/c/field-survey-coverage-string-normalisation-date-by-category-pivot-8219478e
- Structured record (JSON): https://api.zero.xyz/v1/capabilities/cap_HwLZ4_sgJvqmrrFQP421d

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 field-survey-coverage-string-normalisation-date-by-category-pivot-8219478e -d '<json body>'
```

Example prompt: I have 4 rows of field survey coverage data with station groups like 'group-a' and 'group-b', dates from 2026-10-01 to 2026-10-04, and surveyed_m2 values — can you run string normalisation and a date-by-category pivot on the surveyed_m2 field for me?

## When to prefer this

Choose this endpoint when you have field survey coverage records with station groups and need both string normalisation of category labels and a date-by-category pivot in a single call. It is ideal for up to 200 rows of survey data where you need two bundled analytical outputs at low cost ($0.01 USDC), without requiring live data lookups or external integrations.

## Known failure modes

- Rows exceeding 200 items will be rejected (maxItems constraint)
- Missing required fields (row_id, station_group, date, surveyed_m2, target_m2) will cause validation errors
- Dates not matching YYYY-MM-DD pattern will fail schema validation
- Numeric values outside ±1,000,000,000,000 range will be rejected
- Invalid field enum value (must be 'surveyed_m2' or 'target_m2') will cause an error
- Payment of 0.01 USDC must be included via x402 protocol or the request will be rejected

## How this service works

Field survey coverage — String normalisation + Date by category pivot: String normalisation + Date by category pivot for supplied field survey coverage records (surveyed_m2, target_m2). 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-normalised version of the station_group field across all submitted rows, and (2) a date-by-category pivot table aggregating the selected metric (surveyed_m2 or target_m2) across dates and station groups — all computed locally with no external lookups.

## Request schema (JSON Schema)

```json
{
 "type": "object",
 "properties": {
  "rows": {
   "type": "array",
   "items": {
    "type": "object",
    "required": [
     "row_id",
     "station_group",
     "date",
     "surveyed_m2",
     "target_m2"
    ],
    "properties": {
     "date": {
      "type": "string",
      "default": "",
      "pattern": "^\\d{4}-\\d{2}-\\d{2}$",
      "maxLength": 10
     },
     "row_id": {
      "type": "string",
      "default": "",
      "maxLength": 80,
      "minLength": 1
     },
     "target_m2": {
      "type": [
       "number",
       "null"
      ],
      "maximum": 1000000000000,
      "minimum": -1000000000000
     },
     "surveyed_m2": {
      "type": [
       "number",
       "null"
      ],
      "maximum": 1000000000000,
      "minimum": -1000000000000
     },
     "station_group": {
      "type": "string",
      "default": "",
      "maxLength": 80
     }
    },
    "additionalProperties": false
   },
   "default": [
    {
     "date": "2026-10-01",
     "row_id": "row-1",
     "target_m2": 1,
     "surveyed_m2": 10,
     "station_group": "group-a"
    },
    {
     "date": "2026-10-02",
     "row_id": "row-2",
     "target_m2": 2,
     "surveyed_m2": 20,
     "station_group": "group-b"
    },
    {
     "date": "2026-10-03",
     "row_id": "row-3",
     "target_m2": 3,
     "surveyed_m2": 15,
     "station_group": "group-a"
    },
    {
     "date": "2026-10-04",
     "row_id": "row-4",
     "target_m2": 4,
     "surveyed_m2": 30,
     "station_group": "group-b"
    }
   ],
   "maxItems": 200,
   "minItems": 1
  },
  "field": {
   "enum": [
    "surveyed_m2",
    "target_m2"
   ],
   "type": "string",
   "default": "surveyed_m2"
  }
 }
}
```

## More

- Live health (JSON, refreshed every minute): https://www.zero.xyz/c/field-survey-coverage-string-normalisation-date-by-category-pivot-8219478e/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)