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TradepilotUSA Data Quality Plan Generator
x402
TradepilotUSA Data Quality Plan Generator is a paid API for AI agents from www.tradepilotusa.com, paid per call via x402, $0.25/call, status unknown (last checked 2026-10-02).
Analyzes a source text document and generates a structured data quality assessment plan identifying evidence gaps, assumptions, and review requirements
Price / call$0.25
Success—
StatusUnknown
How this service works
Review supplied schema/sample rows for quality issues and propose specific validation rules and correction actions. Uses buyer-supplied information only; no external research or outbound actions. Returns a draft for human review.
Output
Returns a JSON object containing: a task identifier, a plain-language summary of data quality findings, a detailed artifact with specific observations about the source document, provenance metadata (source, timestamp, whether external research was performed), a request ID, a list of assumptions made during analysis, a list of evidence gaps identified, and a boolean flag indicating whether human review is required.
Example use cases
Pre-analytics data gap audit
“I have this export from our CRM with customer orders and invoices — can you analyze it for data quality issues and tell me what fields are missing or inconsistent before I hand it to the BI team? The audience is our data engineering team.”
Business process document review
“Here's our residential installation service workflow document — can you review it for data quality gaps, flag any missing metrics like delay rates or budget figures, and tell me if it needs human review? We'll use this for our ops analytics project.”
Lead pipeline data completeness check
“I'm about to import our lead and customer records into a new dashboard — can you run a data quality plan on this source text and list out all the evidence gaps and assumptions so I know what to clean up first? Audience is our sales operations team.”
Technical details
Endpoint
POSThttps://www.tradepilotusa.com/api/agent-commerce/v1/services/data_quality_plan/execute?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you need to assess the quality, completeness, and trustworthiness of an unstructured or semi-structured business document before ingesting it into analytics pipelines, dashboards, or automated workflows. It is particularly useful for business operations contexts (leads, customers, orders, invoices) where data gaps can cause downstream reporting errors. Prefer this over generic text analysis tools when you need structured output with explicit evidence gaps, assumptions, and a human-review recommendation flag.
Side effects
- source_text too short (below 20 characters) — returns validation error
- brief too short (below 10 characters) — returns validation error
- source_text exceeds 32,000 character limit — returns payload too large error
- brief exceeds 12,000 character limit — returns validation error
- malformed JSON body — returns 400 bad request
- payment not provided or insufficient — returns 402 payment required
- service unavailable — returns 5xx error
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| brief | string | |
| audience | string | |
| source_text | string |
Output
Schema · Response
{
"type": "json",
"example": {
"task": "data_quality_plan",
"summary": "The supplied fictional source document lacks explicit schema and sample data rows, and critical data such as appointment delay rates and final project budget are missing. To ensure data quality, the o",
"artifact": "Illustrative excerpt from a synthetic provider check:\n## Quality Review of Supplied Schema/Sample Rows\n\n### Observations from Supplied Fictional Source Document\n- The document describes a residential entry door installation service in Dallas.\n- Consultations are scheduled Monday through Friday.\n- The operations manager is responsible for consultation scheduling.\n- The installation crew confirms measurements before providing a final quote.\n- There is an upcoming meeting to review appointment delays.\n- Current delay rates and final project budget dat",
"provenance": {
"source": "buyer_supplied",
"generated_at": "2026-09-22T14:42:55.249Z",
"external_research_performed": false
},
"request_id": "example_data_quality_plan",
"assumptions": [
"The source text is the only data provided; no additional schema or sample rows were supplied.",
"The operations manager’s scheduling notes are the primary source for appointment data."
],
"evidence_gaps": [
"Explicit data schema or sample rows for consultation scheduling, installation measurements, quotes, delays, and budgets.",
"Quantitative data on appointment delays."
],
"requires_human_review": true
}
}Tags
x402
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