# Data Freshness Analytics — Exact Row Deduplication + Grouped Numeric Summary

> Data Freshness Analytics — Exact Row Deduplication + Grouped Numeric Summary 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).

Deduplicates exact rows and computes grouped numeric summaries for data freshness records containing age_minutes and record_count fields.

## Facts

- Endpoint: POST https://market.datapackvibe.com/x402/assistant-ib26-analytics-06-040?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/data-freshness-analytics-exact-row-deduplication-grouped-numeric-6f2dac99
- Structured record (JSON): https://api.zero.xyz/v1/capabilities/cap_C_UzDwXzrKGMSIX4HRpV3

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 data-freshness-analytics-exact-row-deduplication-grouped-numeric-6f2dac99 -d '<json body>'
```

Example prompt: I have a list of data freshness records with age_minutes and record_count fields across groups like 'group-a' and 'group-b' — can you deduplicate the exact rows and then give me a grouped numeric summary for the age_minutes field?

## When to prefer this

Choose this endpoint when you need both exact row deduplication and grouped numeric aggregation on data freshness records (age_minutes, record_count) in a single low-cost call. Ideal for pipelines that need to clean duplicate monitoring logs and immediately summarize freshness metrics by group. Best suited for datasets up to 200 rows where no external data lookup or prediction is needed — pure local analytical computation.

## Known failure modes

- Exceeding 200 row limit returns a validation error
- Missing required fields (row_id, dataset_group, date, age_minutes, record_count) causes rejection
- Invalid date format (non YYYY-MM-DD) fails schema validation
- Empty rows array rejected (minItems: 1)
- Invalid field enum value (must be age_minutes or record_count) causes error
- Null numeric values are accepted but may affect summary statistics

## How this service works

Data freshness — Exact row deduplication + Grouped numeric summary: Exact row deduplication + Grouped numeric summary for supplied data freshness records (age_minutes, record_count). 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) an exact row deduplication report identifying and removing fully duplicate rows from the submitted data freshness records, and (2) a grouped numeric summary (e.g. count, min, max, mean) of the selected field (age_minutes or record_count) broken down by dataset_group. Both reports are computed locally without external lookups.

## Request schema (JSON Schema)

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

## More

- Live health (JSON, refreshed every minute): https://www.zero.xyz/c/data-freshness-analytics-exact-row-deduplication-grouped-numeric-6f2dac99/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)