# Data Freshness Analytics: Numeric Distribution + Daily Sum Series

> Data Freshness Analytics: Numeric Distribution + Daily Sum Series is a paid API for AI agents from market.datapackvibe.com, paid per call via x402, $0.01/call, status unknown (last checked 2026-10-22).

Computes a numeric distribution report and a daily sum time series for data freshness records (age_minutes, record_count) supplied by the caller — up to 200 rows, two reports per call.

## Facts

- Endpoint: POST https://market.datapackvibe.com/x402/assistant-ib26-analytics-06-075?utm_source=zero.xyz
- Price: $0.01/call
- Payment: x402
- Status: unknown
- Last checked: 2026-10-22
- Activations on Zero: 0
- Tags: x402
- Canonical page: https://www.zero.xyz/c/data-freshness-analytics-numeric-distribution-daily-sum-series-fb31b658
- Structured record (JSON): https://api.zero.xyz/v1/capabilities/cap_V4NHMvmJeuYIUGTi1L-q7

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-numeric-distribution-daily-sum-series-fb31b658 -d '<json body>'
```

Example prompt: Can you run a numeric distribution and daily sum series on these data freshness records? I have rows with date, age_minutes, and record_count for two groups — 'group-a' and 'group-b' — and I want to analyze the age_minutes field across up to 200 rows.

## When to prefer this

Choose this endpoint when you need both a numeric distribution and a daily sum time series for data freshness metrics in a single low-cost call ($0.01 USDC for both reports). It is best suited for offline or batch analytical use cases where data is already collected and you want quick statistical summaries without any live lookup, prediction, or external data enrichment.

## Known failure modes

- More than 200 rows supplied — endpoint enforces a maxItems:200 limit
- Missing required fields (row_id, dataset_group, date, age_minutes, record_count) cause validation errors
- Invalid date format (must match YYYY-MM-DD pattern) causes row rejection
- Null values in age_minutes or record_count are accepted but may affect distribution output
- Payment failure (insufficient USDC or x402 protocol error) blocks execution

## How this service works

Data freshness — Numeric distribution + Daily sum series: Numeric distribution + Daily sum series 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 independent analytical reports: (1) a numeric distribution breaking down the selected field (age_minutes or record_count) into frequency/distribution buckets, and (2) a daily sum time series aggregating the selected field by date — both computed locally from the supplied rows without any external lookup.

## 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-numeric-distribution-daily-sum-series-fb31b658/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)