# Data Reconciliation — Record Quality Audit + String Normalisation + Keyed Dataset Comparison > Data Reconciliation — 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 2026-10-19). Runs three local analytical reports on supplied reconciliation data: record quality audit, string normalisation, and keyed dataset comparison between source and target totals across up to 200 rows. ## Facts - Endpoint: POST https://market.datapackvibe.com/x402/assistant-ib26-analytics-11-207?utm_source=zero.xyz - Price: $0.01/call - Payment: x402 - Status: unknown - Last checked: 2026-10-19 - Activations on Zero: 0 - Tags: x402 - Canonical page: https://www.zero.xyz/c/data-reconciliation-record-quality-audit-string-normalisation-keyed-4c3929bc - Structured record (JSON): https://api.zero.xyz/v1/capabilities/cap_2tZpW29UFJmZ-82XkSlQM 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 data-reconciliation-record-quality-audit-string-normalisation-keyed-4c3929bc -d '' ``` Example prompt: Can you reconcile these two datasets and run all three reports — record quality audit, string normalisation, and keyed comparison — on my rows? The main dataset has entries like row-1 on 2025-01-01 with source_total 10 and target_total 1 in group-a, and I have a second dataset with slightly different values to compare against. ## When to prefer this Choose this endpoint when you have two keyed datasets with source and target numeric totals and need all three analytical outputs — quality audit, string normalisation, and keyed comparison — in a single call at low cost ($0.01 USDC). Best suited for batch reconciliation jobs of up to 200 rows where no external data lookup, live prediction, or third-party enrichment is required. Prefer alternatives if you need real-time data fetching, more than 200 rows, or only a single report type. ## Known failure modes - Submitting more than 200 rows causes rejection or truncation - Missing required fields (row_id, dataset_group, date, source_total, target_total) cause validation errors - Dates not matching YYYY-MM-DD pattern are rejected - Numeric values outside ±1,000,000,000,000 are rejected - Empty rows array (minItems:1 not met) returns an error - Mismatched row_ids between rows and other datasets may produce incomplete comparison report ## How this service works Data reconciliation — Record quality audit + String normalisation + Keyed dataset comparison: Record quality audit + String normalisation + Keyed dataset comparison for supplied data reconciliation records (source_total, target_total). Up to 200 rows; 3 independent reports using shared local analytical engines. No live lookup, prediction, professional assessment or external action. 0.01 USDC includes 3 supplied-data reports for up to 200 rows. ## Output Three independent analytical reports returned for the supplied data: (1) a record quality audit highlighting completeness and anomalies, (2) a string normalisation report showing standardised field values, and (3) a keyed dataset comparison identifying rows where source_total and target_total diverge across the two supplied datasets. No external lookups or live data are involved — all analysis is performed locally on the submitted rows. ## Request schema (JSON Schema) ```json { "type": "object", "properties": { "rows": { "type": "array", "items": { "type": "object", "required": [ "row_id", "dataset_group", "date", "source_total", "target_total" ], "properties": { "date": { "type": "string", "default": "", "pattern": "^\\d{4}-\\d{2}-\\d{2}$", "maxLength": 10 }, "row_id": { "type": "string", "default": "", "maxLength": 80, "minLength": 1 }, "source_total": { "type": [ "number", "null" ], "maximum": 1000000000000, "minimum": -1000000000000 }, "target_total": { "type": [ "number", "null" ], "maximum": 1000000000000, "minimum": -1000000000000 }, "dataset_group": { "type": "string", "default": "", "maxLength": 80 } }, "additionalProperties": false }, "default": [ { "date": "2026-10-01", "row_id": "row-1", "source_total": 10, "target_total": 1, "dataset_group": "group-a" }, { "date": "2026-10-02", "row_id": "row-2", "source_total": 20, "target_total": 2, "dataset_group": "group-b" }, { "date": "2026-10-03", "row_id": "row-3", "source_total": 15, "target_total": 3, "dataset_group": "group-a" }, { "date": "2026-10-04", "row_id": "row-4", "source_total": 30, "target_total": 4, "dataset_group": "group-b" } ], "maxItems": 200, "minItems": 1 }, "other": { "type": "array", "items": { "type": "object", "required": [ "row_id", "dataset_group", "date", "source_total", "target_total" ], "properties": { "date": { "type": "string", "default": "", "pattern": "^\\d{4}-\\d{2}-\\d{2}$", "maxLength": 10 }, "row_id": { "type": "string", "default": "", "maxLength": 80, "minLength": 1 }, "source_total": { "type": [ "number", "null" ], "maximum": 1000000000000, "minimum": -1000000000000 }, "target_total": { "type": [ "number", "null" ], "maximum": 1000000000000, "minimum": -1000000000000 }, "dataset_group": { "type": "string", "default": "", "maxLength": 80 } }, "additionalProperties": false }, "default": [ { … (truncated) ``` ## More - Live health (JSON, refreshed every minute): https://www.zero.xyz/c/data-reconciliation-record-quality-audit-string-normalisation-keyed-4c3929bc/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)