# Structured Data Observations — Numeric Interval Filter + Adjacent Row Changes

> Structured Data Observations — Numeric Interval Filter + 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 2026-10-25).

Filters structured observation rows by a numeric interval on a chosen field and detects adjacent row changes, returning two analytical reports for up to 200 rows.

## Facts

- Endpoint: POST https://market.datapackvibe.com/x402/assistant-ib26-web-08-127?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/structured-data-observations-numeric-interval-filter-adjacent-row-fbfff5ff
- Structured record (JSON): https://api.zero.xyz/v1/capabilities/cap_SYK8lJXTYhtjzAsEM8dow

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 structured-data-observations-numeric-interval-filter-adjacent-row-fbfff5ff -d '<json body>'
```

Example prompt: I have a list of structured observation records with site_group, date, valid_items, and total_items — can you filter rows where valid_items is between 10 and 150 and also flag any adjacent rows where that value changed? I've got about 80 rows total.

## When to prefer this

Choose this endpoint when you have structured observation data (with site_group, date, valid_items, total_items fields) and need both a numeric range filter AND adjacent-row change detection in a single low-cost call. It is ideal for lightweight data QA, pre-report validation, or sequential anomaly detection on datasets up to 200 rows, without needing external data sources or live lookups.

## Known failure modes

- Empty or missing rows array returns an error
- More than 200 rows submitted exceeds the per-call limit
- Invalid date format (not YYYY-MM-DD) causes schema validation failure
- Minimum value greater than maximum value may produce an empty interval result
- Null numeric values in valid_items or total_items may be excluded or cause unexpected behavior in comparisons
- Unsupported field enum value returns a validation error

## How this service works

Structured data observations — Numeric interval filter + Adjacent row changes: Numeric interval filter + Adjacent row changes for supplied structured data observations records (valid_items, total_items). 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 reports: (1) rows from the input dataset whose chosen field (valid_items or total_items) falls within the specified [minimum, maximum] interval; (2) a list of adjacent row pairs where the chosen field value changed from one row to the next. Both reports operate on the same supplied dataset of up to 200 rows with no external lookups.

## Request schema (JSON Schema)

```json
{
 "type": "object",
 "properties": {
  "rows": {
   "type": "array",
   "items": {
    "type": "object",
    "required": [
     "row_id",
     "site_group",
     "date",
     "valid_items",
     "total_items"
    ],
    "properties": {
     "date": {
      "type": "string",
      "default": "",
      "pattern": "^\\d{4}-\\d{2}-\\d{2}$",
      "maxLength": 10
     },
     "row_id": {
      "type": "string",
      "default": "",
      "maxLength": 80,
      "minLength": 1
     },
     "site_group": {
      "type": "string",
      "default": "",
      "maxLength": 80
     },
     "total_items": {
      "type": [
       "number",
       "null"
      ],
      "maximum": 1000000000000,
      "minimum": -1000000000000
     },
     "valid_items": {
      "type": [
       "number",
       "null"
      ],
      "maximum": 1000000000000,
      "minimum": -1000000000000
     }
    },
    "additionalProperties": false
   },
   "default": [
    {
     "date": "2026-10-01",
     "row_id": "row-1",
     "site_group": "group-a",
     "total_items": 1,
     "valid_items": 10
    },
    {
     "date": "2026-10-02",
     "row_id": "row-2",
     "site_group": "group-b",
     "total_items": 2,
     "valid_items": 20
    },
    {
     "date": "2026-10-03",
     "row_id": "row-3",
     "site_group": "group-a",
     "total_items": 3,
     "valid_items": 15
    },
    {
     "date": "2026-10-04",
     "row_id": "row-4",
     "site_group": "group-b",
     "total_items": 4,
     "valid_items": 30
    }
   ],
   "maxItems": 200,
   "minItems": 1
  },
  "field": {
   "enum": [
    "valid_items",
    "total_items"
   ],
   "type": "string",
   "default": "valid_items"
  },
  "maximum": {
   "type": "number",
   "default": 100,
   "maximum": 1000000000000,
   "minimum": -1000000000000
  },
  "minimum": {
   "type": "number",
   "default": 0,
   "maximum": 1000000000000,
   "minimum": -1000000000000
  }
 }
}
```

## More

- Live health (JSON, refreshed every minute): https://www.zero.xyz/c/structured-data-observations-numeric-interval-filter-adjacent-row-fbfff5ff/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)