# Feature Availability: Exact Row Deduplication + Keyed Dataset Comparison > Feature Availability: Exact Row Deduplication + 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). Performs exact row deduplication and keyed dataset comparison on two sets of feature availability records (populated_features vs expected_features), returning two independent analytical reports for up to 200 rows. ## Facts - Endpoint: POST https://market.datapackvibe.com/x402/assistant-ib26-analytics-10-053?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-exact-row-deduplication-keyed-dataset-comparison-fd0f3c05 - Structured record (JSON): https://api.zero.xyz/v1/capabilities/cap_LtwwmqKTEdcS2RvLlpc1k 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-exact-row-deduplication-keyed-dataset-comparison-fd0f3c05 -d '' ``` Example prompt: Can you deduplicate and compare these two feature availability datasets for me? The first set has rows like row-1 in group-a on 2026-10-01 with 10 populated features and 1 expected, and the second set has the same rows but with slightly different values — I need both a deduplication report and a keyed comparison report showing where they diverge. ## When to prefer this Choose this endpoint when you need both exact row deduplication and keyed dataset comparison on feature availability data in a single $0.01 call. It is ideal for ML/data engineering teams auditing feature store completeness, detecting ETL drift between two pipeline runs, or validating that populated feature counts match expected counts across dataset groups and dates — all without any external lookups or live data access. Prefer this over general-purpose data tools when your records conform to the row_id/dataset_group/date/populated_features/expected_features schema and you need two reports from one API call. ## Known failure modes - Input arrays exceed 200 rows — request rejected with validation error - Missing required fields (row_id, dataset_group, date, populated_features, expected_features) cause schema validation failure - Date field not matching YYYY-MM-DD pattern returns format error - row_id exceeding 80 characters triggers length validation error - Numeric values for populated_features or expected_features outside ±1,000,000,000,000 range rejected - Empty rows array (minItems:1 not met) returns validation error - Payment of 0.01 USDC not included or rejected causes 402 response ## How this service works Feature availability — Exact row deduplication + Keyed dataset comparison: Exact row deduplication + 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 Two independent analytical reports: (1) an exact row deduplication report identifying duplicate rows within each supplied dataset, and (2) a keyed dataset comparison report showing row-by-row differences between the 'rows' and 'other' input datasets based on populated_features and expected_features values. No external data sources or live lookups are used — all analysis is performed on the supplied data locally. ## 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-exact-row-deduplication-keyed-dataset-comparison-fd0f3c05/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)