# Account Research Coverage Analyzer — Record Quality Audit, String Normalisation & Keyed Dataset Comparison > Account Research Coverage 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 2026-10-21). Runs three local analytical reports (record quality audit, string normalisation, and keyed dataset comparison) on up to 200 rows of account research coverage data containing researched_accounts and target_accounts fields. ## Facts - Endpoint: POST https://market.datapackvibe.com/x402/assistant-ib26-company-08-207?utm_source=zero.xyz - Price: $0.01/call - Payment: x402 - Status: unknown - Last checked: 2026-10-21 - Activations on Zero: 0 - Tags: x402 - Canonical page: https://www.zero.xyz/c/account-research-coverage-analyzer-record-quality-audit-string-c3ceb903 - Structured record (JSON): https://api.zero.xyz/v1/capabilities/cap_yit8oLcIfKRT30uHCEvo1 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 account-research-coverage-analyzer-record-quality-audit-string-c3ceb903 -d '' ``` Example prompt: Can you run the three-report account research coverage audit on these two datasets — rows and other — each with up to 50 records per company group, with fields row_id, company_group, date, researched_accounts, and target_accounts? I want the record quality audit, string normalisation, and keyed dataset comparison all in one shot. ## When to prefer this Choose this endpoint when you have up to 200 rows of account research coverage data in a structured tabular format and need all three analytical passes — quality audit, string normalisation, and keyed comparison — bundled in a single low-cost call ($0.01 USDC). It is ideal for sales ops, RevOps, or data engineering teams that want a quick pre-processing validation step before loading account research data into a CRM or data warehouse. Prefer this over building custom scripts when you need a repeatable, schema-validated audit without any external API dependency or live data lookup. ## Known failure modes - Exceeding 200 rows in either the rows or other array returns a validation error - Missing required fields (row_id, company_group, date, researched_accounts, target_accounts) per row causes rejection - Dates not matching YYYY-MM-DD pattern fail schema validation - Numeric values outside ±1,000,000,000,000 are rejected - Empty rows array (minItems: 1 not met) returns an error - Malformed JSON body causes a 400-level error ## How this service works Account research coverage — Record quality audit + String normalisation + Keyed dataset comparison: Record quality audit + String normalisation + Keyed dataset comparison for supplied account research coverage records (researched_accounts, target_accounts). 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 delivered together: (1) a record quality audit flagging nulls, out-of-range values, and structural issues; (2) a string normalisation report standardising company_group and other string fields; (3) a keyed dataset comparison report showing row-level differences between the two supplied datasets. No external lookups or predictions are made — all analysis is performed locally on the supplied data. ## Request schema (JSON Schema) ```json { "type": "object", "properties": { "rows": { "type": "array", "items": { "type": "object", "required": [ "row_id", "company_group", "date", "researched_accounts", "target_accounts" ], "properties": { "date": { "type": "string", "default": "", "pattern": "^\\d{4}-\\d{2}-\\d{2}$", "maxLength": 10 }, "row_id": { "type": "string", "default": "", "maxLength": 80, "minLength": 1 }, "company_group": { "type": "string", "default": "", "maxLength": 80 }, "target_accounts": { "type": [ "number", "null" ], "maximum": 1000000000000, "minimum": -1000000000000 }, "researched_accounts": { "type": [ "number", "null" ], "maximum": 1000000000000, "minimum": -1000000000000 } }, "additionalProperties": false }, "default": [ { "date": "2026-10-01", "row_id": "row-1", "company_group": "group-a", "target_accounts": 1, "researched_accounts": 10 }, { "date": "2026-10-02", "row_id": "row-2", "company_group": "group-b", "target_accounts": 2, "researched_accounts": 20 }, { "date": "2026-10-03", "row_id": "row-3", "company_group": "group-a", "target_accounts": 3, "researched_accounts": 15 }, { "date": "2026-10-04", "row_id": "row-4", "company_group": "group-b", "target_accounts": 4, "researched_accounts": 30 } ], "maxItems": 200, "minItems": 1 }, "other": { "type": "array", "items": { "type": "object", "required": [ "row_id", "company_group", "date", "researched_accounts", "target_accounts" ], "properties": { "date": { "type": "string", "default": "", "pattern": "^\\d{4}-\\d{2}-\\d{2}$", "maxLength": 10 }, "row_id": { "type": "string", "default": "", "maxLength": 80, "minLength": 1 }, "company_group": { "type": "string", "default": "", "maxLength": 80 }, "target_accounts": { "type": [ "number", "null" ], "maximum": 1000000000000, "minimum": -1000000000000 }, "researched_accounts": { "type": [ "number", "null" ], "maximum": 1000000000000, "minimum": -1000000000 … (truncated) ``` ## More - Live health (JSON, refreshed every minute): https://www.zero.xyz/c/account-research-coverage-analyzer-record-quality-audit-string-c3ceb903/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)