# Competitor Supplied Prices — Record Quality Audit + String Normalisation + Keyed Dataset Comparison > Competitor Supplied Prices — 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 analytical reports (record quality audit, string normalisation, and keyed dataset comparison) on up to 200 rows of competitor supplied price data (list_price, package_quantity) with no external lookups. ## Facts - Endpoint: POST https://market.datapackvibe.com/x402/assistant-ib26-company-07-207?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/competitor-supplied-prices-record-quality-audit-string-normalisation-c1415e7e - Structured record (JSON): https://api.zero.xyz/v1/capabilities/cap_E2V2UnVgMnBjnaZHQN9ag 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 competitor-supplied-prices-record-quality-audit-string-normalisation-c1415e7e -d '' ``` Example prompt: I have two batches of competitor pricing records — each up to 200 rows with row_id, company_group, date, list_price, and package_quantity — can you run the full quality audit, string normalisation, and keyed comparison across both datasets and tell me what issues you find? ## When to prefer this Choose this endpoint when you have already collected competitor pricing data (list_price, package_quantity) and need fast, local analytical checks — quality auditing, string normalisation, and keyed comparison — without sending data to external systems or making live lookups. It is ideal for pre-ingestion data validation pipelines, data reconciliation between two snapshots, and lightweight QA on small-to-medium batches (up to 200 rows). At $0.01 USDC for three reports, it is cost-effective for routine data hygiene tasks on competitor pricing feeds. ## Known failure modes - More than 200 rows submitted — request rejected with row limit error - Missing required fields (row_id, company_group, date, list_price, package_quantity) — validation error returned - Invalid date format (not YYYY-MM-DD) — schema validation failure - Numeric values outside allowed range (±1,000,000,000,000) — validation error - Empty rows array (minItems: 1 not met) — rejection - Payment not processed — HTTP 402 response blocking access ## How this service works Competitor supplied prices — Record quality audit + String normalisation + Keyed dataset comparison: Record quality audit + String normalisation + Keyed dataset comparison for supplied competitor supplied prices records (list_price, package_quantity). Up to 200 rows; 3 independent reports using shared local analytical engines. No live lookup, prediction, professional assessment or external action. 0.01 USDC includes 3 supplied-data reports for up to 200 rows. ## Output Three independent analytical reports covering: (1) a record quality audit flagging malformed, null, or out-of-range values in list_price and package_quantity fields; (2) a string normalisation report identifying and correcting inconsistencies in text fields like company_group; and (3) a keyed dataset comparison report showing differences between the two supplied row arrays matched by row_id. All processing is performed locally with no external lookups. ## Request schema (JSON Schema) ```json { "type": "object", "properties": { "rows": { "type": "array", "items": { "type": "object", "required": [ "row_id", "company_group", "date", "list_price", "package_quantity" ], "properties": { "date": { "type": "string", "default": "", "pattern": "^\\d{4}-\\d{2}-\\d{2}$", "maxLength": 10 }, "row_id": { "type": "string", "default": "", "maxLength": 80, "minLength": 1 }, "list_price": { "type": [ "number", "null" ], "maximum": 1000000000000, "minimum": -1000000000000 }, "company_group": { "type": "string", "default": "", "maxLength": 80 }, "package_quantity": { "type": [ "number", "null" ], "maximum": 1000000000000, "minimum": -1000000000000 } }, "additionalProperties": false }, "default": [ { "date": "2026-10-01", "row_id": "row-1", "list_price": 10, "company_group": "group-a", "package_quantity": 1 }, { "date": "2026-10-02", "row_id": "row-2", "list_price": 20, "company_group": "group-b", "package_quantity": 2 }, { "date": "2026-10-03", "row_id": "row-3", "list_price": 15, "company_group": "group-a", "package_quantity": 3 }, { "date": "2026-10-04", "row_id": "row-4", "list_price": 30, "company_group": "group-b", "package_quantity": 4 } ], "maxItems": 200, "minItems": 1 }, "other": { "type": "array", "items": { "type": "object", "required": [ "row_id", "company_group", "date", "list_price", "package_quantity" ], "properties": { "date": { "type": "string", "default": "", "pattern": "^\\d{4}-\\d{2}-\\d{2}$", "maxLength": 10 }, "row_id": { "type": "string", "default": "", "maxLength": 80, "minLength": 1 }, "list_price": { "type": [ "number", "null" ], "maximum": 1000000000000, "minimum": -1000000000000 }, "company_group": { "type": "string", "default": "", "maxLength": 80 }, "package_quantity": { "type": [ "number", "null" ], "maximum": 1000000000000, "minimum": -1000000000000 } }, "additionalProperties": false }, "de … (truncated) ``` ## More - Live health (JSON, refreshed every minute): https://www.zero.xyz/c/competitor-supplied-prices-record-quality-audit-string-normalisation-c1415e7e/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)