# Feature Availability Analytics — Weighted Mean & Keyed Dataset Comparison > Feature Availability Analytics — Weighted Mean & Keyed Dataset Comparison is a paid API for AI agents from market.datapackvibe.com, paid per call via x402, $0.01/call, status unknown (last checked 2026-10-15). Computes a secondary-measure weighted mean and performs a keyed dataset comparison on supplied feature availability records (populated_features vs expected_features) for up to 200 rows, returning two independent analytical reports. ## Facts - Endpoint: POST https://market.datapackvibe.com/x402/assistant-ib26-analytics-10-186?utm_source=zero.xyz - Price: $0.01/call - Payment: x402 - Status: unknown - Last checked: 2026-10-15 - Activations on Zero: 0 - Tags: x402 - Canonical page: https://www.zero.xyz/c/feature-availability-analytics-weighted-mean-keyed-dataset-comparison-6f21eaea - Structured record (JSON): https://api.zero.xyz/v1/capabilities/cap_1oRxdbf17t4yhUVeXopl1 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-weighted-mean-keyed-dataset-comparison-6f21eaea -d '' ``` Example prompt: I have two sets of feature availability records — each with populated_features and expected_features per row, grouped by dataset_group and date — can you run a secondary-measure weighted mean on the populated_features field and also compare the two datasets side by side to see where they differ? ## When to prefer this Choose this endpoint when you need two simultaneous analytical reports — a weighted mean and a keyed dataset comparison — on feature availability data (populated vs expected features) in a single $0.01 call. It is ideal for data quality workflows, ML feature pipeline audits, and dataset completeness checks where you already have the data locally and need fast, offline statistical analysis without external lookups or predictions. Prefer this over general-purpose analytics tools when your data fits within 200 rows and you specifically need the weighted mean + keyed comparison combination. ## Known failure modes - Exceeding 200 rows causes a validation error — keep each array at or under the 200-row limit - Missing required fields (row_id, dataset_group, date, populated_features, expected_features) on any row will cause a schema validation failure - Invalid date format (must match YYYY-MM-DD) will be rejected - Null values in populated_features or expected_features may affect weighted mean accuracy — check for missing data before submitting - Mismatched row_ids between primary and secondary arrays may reduce the quality of the keyed comparison report ## How this service works Feature availability — Secondary-measure weighted mean + Keyed dataset comparison: Secondary-measure weighted mean + Keyed dataset comparison 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 independent analytical reports: (1) a secondary-measure weighted mean computed over the selected field (populated_features or expected_features) across the supplied rows, and (2) a keyed dataset comparison between the primary and secondary row arrays, highlighting differences by dataset_group and date. No external lookups or predictions are performed — all analysis uses local analytical engines on the supplied data only. ## 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" }, "other": { "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 … (truncated) ``` ## More - Live health (JSON, refreshed every minute): https://www.zero.xyz/c/feature-availability-analytics-weighted-mean-keyed-dataset-comparison-6f21eaea/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)