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Lordevo Structured Data Extractor
x402
Lordevo Structured Data Extractor is a paid API for AI agents from shop.lordevo.com, paid per call via x402, $0.002/call, status unknown (last checked 2026-10-31).
Extracts and validates structured data (JSON-LD, microdata, RDFa) from any public webpage, returning parsed schema.org items with missing property diagnostics
Price / call$0.002
Success—
StatusUnknown
How this service works
SEO audits, website security checks, SEO content plans, articles and site fix plans as API endpoints for AI agents. Pay per request in USDC on Base with…
Output
Returns a JSON object containing the target URL, a count of JSON-LD blocks found, an array of parsed schema.org items (each with its @type, data, and lists of missing required and recommended properties), detected JSON-LD types, microdata types, RDFa types, a count of invalid JSON-LD blocks, and an issues array flagging any structural or validation problems.
Example use cases
E-commerce product schema audit
“Check https://shop.example.com/blue-sneakers for JSON-LD structured data — I want to know if the Product schema is complete and if there are any missing required or recommended properties.”
FAQ schema validation for SEO
“Pull the structured data from https://myblog.com/faq and tell me if the FAQPage JSON-LD is valid, what questions are marked up, and whether there are any issues that could hurt rich snippet eligibility.”
Competitor structured data intelligence
“Scrape the structured data from https://competitor.com/about and show me every schema.org type they're using — JSON-LD, microdata, and RDFa — so I can see what markup they've implemented.”
Technical details
Endpoint
GEThttps://shop.lordevo.com/api/quick/structured-data?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you need to programmatically extract and validate schema.org structured data from a single webpage — especially for SEO audits, rich snippet eligibility checks, or competitive intelligence on what schema types a site implements. It handles JSON-LD, microdata, and RDFa in one call and surfaces missing required/recommended properties, making it more useful than generic scrapers that return raw HTML. Prefer it over full-site crawlers when you need deep structured data analysis on one specific URL rather than broad coverage.
Side effects
- URL is not publicly accessible or returns non-200 status — empty items array or error in issues
- Page has no structured data — returns valid response with json_ld_count=0 and empty arrays
- Invalid or malformed URL input — request fails with validation error
- Page blocks crawlers (bot protection, CAPTCHA) — may return empty or partial results
- Very large pages may time out or return incomplete extraction
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| inputrequired | object | |
| output | object |
Output
Schema · Response
{
"type": "json",
"example": {
"url": "https://example.com/product/widget",
"tool": "structured-data",
"items": [
{
"id": null,
"data": {
"name": "Widget",
"@type": "Product",
"offers": {
"@type": "Offer",
"price": "19.00",
"priceCurrency": "USD"
}
},
"type": [
"Product"
],
"missing_required": [],
"missing_recommended": [
"aggregateRating",
"review"
]
}
],
"issues": [],
"rdfa_types": [],
"json_ld_count": 2,
"json_ld_types": [
"Product",
"BreadcrumbList"
],
"microdata_types": [],
"invalid_json_ld_blocks": 0
}
}Tags
x402
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