# Field Survey Coverage Analyzer — Record Quality Audit, String Normalisation & Adjacent Row Changes

> Field Survey Coverage Analyzer — Record Quality Audit, String Normalisation & Adjacent Row Changes is a paid API for AI agents from market.datapackvibe.com, paid per call via x402, $0.01/call, status unknown (last checked 2026-11-02).

Runs three local analytical reports (record quality audit, string normalisation, and adjacent row changes) on up to 200 field survey coverage records containing surveyed_m2 and target_m2 values.

## Facts

- Endpoint: POST https://market.datapackvibe.com/x402/assistant-ib26-weather-22-200?utm_source=zero.xyz
- Price: $0.01/call
- Payment: x402
- Status: unknown
- Last checked: 2026-11-02
- Activations on Zero: 0
- Tags: x402
- Canonical page: https://www.zero.xyz/c/field-survey-coverage-analyzer-record-quality-audit-string-fcbdd78b
- Structured record (JSON): https://api.zero.xyz/v1/capabilities/cap_79EhObusJTaDZ62BFIHzk

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-analyzer-record-quality-audit-string-fcbdd78b -d '<json body>'
```

Example prompt: Can you run a record quality audit, string normalisation, and adjacent row changes analysis on my field survey coverage data? I have 4 rows — row-1 on 2026-10-01 with station_group 'group-a', surveyed_m2 of 10 and target_m2 of 1; row-2 on 2026-10-02 with 'group-b', 20 and 2; row-3 on 2026-10-03 with 'group-a', 15 and 3; row-4 on 2026-10-04 with 'group-b', 30 and 4 — and focus the analysis on the surveyed_m2 field.

## When to prefer this

Choose this endpoint when you have field survey coverage data (surveyed_m2, target_m2) in up to 200 rows and need three simultaneous local analytical reports — quality audit, string normalisation, and adjacent row change detection — without any external data lookups, predictions, or professional assessments. It is cost-effective at $0.01 USDC for all three reports and is ideal for pre-submission data validation, cleaning pipelines, or QA workflows on ecological, environmental, or geospatial survey datasets.

## Known failure modes

- Exceeding 200 rows returns a validation error
- Missing required fields (row_id, station_group, date, surveyed_m2, target_m2) causes rejection
- Invalid date format (not YYYY-MM-DD) triggers schema validation failure
- Null values in surveyed_m2 or target_m2 may limit numeric analysis in affected reports
- Payment failure or insufficient USDC balance prevents endpoint execution

## How this service works

Field survey coverage — Record quality audit + String normalisation + Adjacent row changes: Record quality audit + String normalisation + Adjacent row changes for supplied field survey coverage records (surveyed_m2, target_m2). Up to 200 rows; 3 independent reports using shared local analytical engines. No live lookup, prediction, professional assessment or external action. 0.01 USDC includes 3 supplied-data reports for up to 200 rows.

## Output

Three independent analytical reports covering: (1) a record quality audit identifying malformed, missing, or suspicious values across the survey rows; (2) a string normalisation report highlighting inconsistencies or casing issues in station_group and other string fields; and (3) an adjacent row changes report showing value differences between consecutive rows for the selected field (surveyed_m2 or target_m2). All analysis is performed 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-analyzer-record-quality-audit-string-fcbdd78b/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)