# Time Series Completeness Analyzer — Record Quality Audit, String Normalisation & Keyed Dataset Comparison > Time Series Completeness Analyzer — Record Quality Audit, String Normalisation & 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). Audits time series completeness records for quality, normalises strings, and compares two keyed datasets, producing 3 independent analytical reports for up to 200 rows. ## Facts - Endpoint: POST https://market.datapackvibe.com/x402/assistant-ib26-analytics-16-207?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/time-series-completeness-analyzer-record-quality-audit-string-fbe0963a - Structured record (JSON): https://api.zero.xyz/v1/capabilities/cap_cM5VY2lQ3z87Uf_lip6SK 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 time-series-completeness-analyzer-record-quality-audit-string-fbe0963a -d '' ``` Example prompt: Can you run a quality audit, string normalisation, and dataset comparison on my two time series completeness datasets? The first dataset has 4 rows: row-1 on 2026-10-01 in group-a with 10 observed and 1 expected points, row-2 on 2026-10-02 in group-b with 20 observed and 2 expected, row-3 on 2026-10-03 in group-a with 15 observed and 3 expected, and row-4 on 2026-10-04 in group-b with 30 observed and 4 expected — and I have a second comparison dataset with slightly different observed values. ## When to prefer this Choose this endpoint when you need all three of record quality auditing, string normalisation, and keyed dataset comparison for time series completeness data in a single cost-efficient call (0.01 USDC for all 3 reports). Ideal for data engineers validating pipeline completeness, QA workflows comparing observed vs expected counts across dates and groups, or any offline analytical task on up to 200 rows that does not require live lookups, predictions, or external data sources. ## Known failure modes - More than 200 rows submitted — request rejected or truncated - Missing required fields (row_id, dataset_group, date, observed_points, expected_points) cause validation errors - Malformed date strings not matching YYYY-MM-DD pattern rejected - Numeric values out of allowed range (-1,000,000,000,000 to 1,000,000,000,000) rejected - Empty rows array (minItems:1) causes error - Payment failure via x402 protocol prevents endpoint execution ## How this service works Time series completeness — Record quality audit + String normalisation + Keyed dataset comparison: Record quality audit + String normalisation + Keyed dataset comparison for supplied time series completeness records (observed_points, expected_points). Up to 200 rows; 3 independent reports using shared local analytical engines. No live lookup, prediction, professional assessment or external action. 0.01 USDC includes 3 supplied-data reports for up to 200 rows. ## Output Returns 3 independent reports: (1) a record quality audit highlighting data issues in the supplied rows, (2) a string normalisation report cleaning dataset_group and other string fields, and (3) a keyed dataset comparison report showing differences between the 'rows' and 'other' datasets, all computed locally without any external lookups. ## Request schema (JSON Schema) ```json { "type": "object", "properties": { "rows": { "type": "array", "items": { "type": "object", "required": [ "row_id", "dataset_group", "date", "observed_points", "expected_points" ], "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_points": { "type": [ "number", "null" ], "maximum": 1000000000000, "minimum": -1000000000000 }, "observed_points": { "type": [ "number", "null" ], "maximum": 1000000000000, "minimum": -1000000000000 } }, "additionalProperties": false }, "default": [ { "date": "2026-10-01", "row_id": "row-1", "dataset_group": "group-a", "expected_points": 1, "observed_points": 10 }, { "date": "2026-10-02", "row_id": "row-2", "dataset_group": "group-b", "expected_points": 2, "observed_points": 20 }, { "date": "2026-10-03", "row_id": "row-3", "dataset_group": "group-a", "expected_points": 3, "observed_points": 15 }, { "date": "2026-10-04", "row_id": "row-4", "dataset_group": "group-b", "expected_points": 4, "observed_points": 30 } ], "maxItems": 200, "minItems": 1 }, "other": { "type": "array", "items": { "type": "object", "required": [ "row_id", "dataset_group", "date", "observed_points", "expected_points" ], "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_points": { "type": [ "number", "null" ], "maximum": 1000000000000, "minimum": -1000000000000 }, "observed_points": { "type": [ "number", "null" ], "maximum": 1000000000000, "minimum": -1000000000000 } }, "additiona … (truncated) ``` ## More - Live health (JSON, refreshed every minute): https://www.zero.xyz/c/time-series-completeness-analyzer-record-quality-audit-string-fbe0963a/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)