# Feature Availability Analytics: Category Frequencies + Keyed Dataset Comparison > Feature Availability Analytics: Category Frequencies + 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 2026-10-20). Analyzes feature availability records to produce two reports: category-level frequency distributions and a keyed comparison between two datasets (rows vs other), covering populated vs expected feature counts across up to 200 rows. ## Facts - Endpoint: POST https://market.datapackvibe.com/x402/assistant-ib26-analytics-10-111?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-category-frequencies-keyed-dataset-e254c958 - Structured record (JSON): https://api.zero.xyz/v1/capabilities/cap_q6M5XMjXG8ISt7UK_m8oi 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-category-frequencies-keyed-dataset-e254c958 -d '' ``` Example prompt: I have two batches of feature availability records — each with row IDs, dataset groups, dates, and populated vs expected feature counts — can you run a category frequency breakdown and a keyed comparison between them to see where the differences are? Here's the primary dataset: [{"row_id":"r1","dataset_group":"group-a","date":"2025-01-01","populated_features":80,"expected_features":100}] and the comparison dataset: [{"row_id":"r1","dataset_group":"group-a","date":"2025-01-01","populated_features":85,"expected_features":100}]. ## When to prefer this Choose this endpoint when you need both a category-level frequency breakdown and a keyed row-by-row comparison of two feature availability datasets in a single low-cost call. It is ideal for data quality monitoring, ML feature store audits, and dataset migration validation where you have structured records with populated vs expected feature counts grouped by dataset category. Prefer this over single-report endpoints when you need both analyses at once for up to 200 rows. ## Known failure modes - Input array exceeds 200 rows — request will be rejected or truncated - Missing required fields (row_id, dataset_group, date, populated_features, expected_features) cause row-level validation errors - Null values in populated_features or expected_features may affect comparison accuracy - Date strings not matching YYYY-MM-DD pattern will fail schema validation - Mismatched row_ids between primary and comparison datasets result in unmatched rows in the keyed comparison - Empty input array (minItems: 1 not met) returns an error ## How this service works Feature availability — Category frequencies + Keyed dataset comparison: Category frequencies + 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 analytical reports: (1) a category frequency distribution showing how feature availability records are distributed across dataset_group categories, and (2) a keyed comparison between the primary (rows) and secondary (other) datasets, highlighting differences in populated_features and expected_features matched by row_id. No external data sources are used; all computation is local and deterministic. ## 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 }, "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 }, "populated_features": { "type": [ "number", "null" ], "maximum": 1000000000000, "minimum": -10 … (truncated) ``` ## More - Live health (JSON, refreshed every minute): https://www.zero.xyz/c/feature-availability-analytics-category-frequencies-keyed-dataset-e254c958/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)