# Feature Availability Analytics — Base-100 Series & Secondary-Measure Weighted Mean

> Feature Availability Analytics — Base-100 Series & Secondary-Measure Weighted Mean is a paid API for AI agents from market.datapackvibe.com, paid per call via x402, $0.01/call, status unknown (last checked 2026-10-20).

Computes a Base-100 index series and a secondary-measure weighted mean for feature availability records (populated_features vs expected_features) over up to 200 rows.

## Facts

- Endpoint: POST https://market.datapackvibe.com/x402/assistant-ib26-analytics-10-166?utm_source=zero.xyz
- Price: $0.01/call
- Payment: x402
- Status: unknown
- Last checked: 2026-10-20
- Activations on Zero: 0
- Tags: x402
- Canonical page: https://www.zero.xyz/c/feature-availability-analytics-base-100-series-secondary-measure-e0ae942a
- Structured record (JSON): https://api.zero.xyz/v1/capabilities/cap_McOAvIGRX4wg9je4AHcRg

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 feature-availability-analytics-base-100-series-secondary-measure-e0ae942a -d '<json body>'
```

Example prompt: I have 4 rows of feature availability data for two dataset groups — group-a and group-b — covering dates 2026-10-01 to 2026-10-04, with populated_features counts of 10, 20, 15, and 30 and expected_features of 1, 2, 3, and 4. Can you run the Base-100 index series and weighted mean analytics on the populated_features field, using row index 0 as the base?

## When to prefer this

Choose this endpoint when you have up to 200 rows of feature availability records (populated vs expected feature counts) and need both a normalized Base-100 time series and a weighted mean in a single low-cost call ($0.01 USDC). Ideal for data quality audits, pipeline monitoring, and dataset completeness reporting where no external data lookup or prediction is needed — pure local analytical computation on your supplied data.

## Known failure modes

- Missing required fields (row_id, date, populated_features, expected_features) cause validation errors
- Submitting more than 200 rows exceeds the maxItems limit and will be rejected
- Invalid date format (not YYYY-MM-DD) triggers a pattern validation failure
- base_index out of range (>=number of rows) may cause indexing errors
- Null values in populated_features or expected_features may affect weighted mean computation
- Malformed JSON body returns a 400-level error

## How this service works

Feature availability — Base-100 series + Secondary-measure weighted mean: Base-100 series + Secondary-measure weighted mean for supplied feature availability records (populated_features, expected_features). 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 for the supplied feature availability rows: (1) a Base-100 index series normalizing the chosen field (populated_features or expected_features) relative to the selected base row, and (2) a secondary-measure weighted mean across all supplied rows. Both reports are computed locally on the submitted data with no external lookups.

## Request schema (JSON Schema)

```json
{
 "type": "object",
 "properties": {
  "rows": {
   "type": "array",
   "items": {
    "type": "object",
    "required": [
     "row_id",
     "dataset_group",
     "date",
     "populated_features",
     "expected_features"
    ],
    "properties": {
     "date": {
      "type": "string",
      "default": "",
      "pattern": "^\\d{4}-\\d{2}-\\d{2}$",
      "maxLength": 10
     },
     "row_id": {
      "type": "string",
      "default": "",
      "maxLength": 80,
      "minLength": 1
     },
     "dataset_group": {
      "type": "string",
      "default": "",
      "maxLength": 80
     },
     "expected_features": {
      "type": [
       "number",
       "null"
      ],
      "maximum": 1000000000000,
      "minimum": -1000000000000
     },
     "populated_features": {
      "type": [
       "number",
       "null"
      ],
      "maximum": 1000000000000,
      "minimum": -1000000000000
     }
    },
    "additionalProperties": false
   },
   "default": [
    {
     "date": "2026-10-01",
     "row_id": "row-1",
     "dataset_group": "group-a",
     "expected_features": 1,
     "populated_features": 10
    },
    {
     "date": "2026-10-02",
     "row_id": "row-2",
     "dataset_group": "group-b",
     "expected_features": 2,
     "populated_features": 20
    },
    {
     "date": "2026-10-03",
     "row_id": "row-3",
     "dataset_group": "group-a",
     "expected_features": 3,
     "populated_features": 15
    },
    {
     "date": "2026-10-04",
     "row_id": "row-4",
     "dataset_group": "group-b",
     "expected_features": 4,
     "populated_features": 30
    }
   ],
   "maxItems": 200,
   "minItems": 1
  },
  "field": {
   "enum": [
    "populated_features",
    "expected_features"
   ],
   "type": "string",
   "default": "populated_features"
  },
  "base_index": {
   "type": "integer",
   "default": 0,
   "maximum": 199,
   "minimum": 0
  }
 }
}
```

## More

- Live health (JSON, refreshed every minute): https://www.zero.xyz/c/feature-availability-analytics-base-100-series-secondary-measure-e0ae942a/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)