How to Extract JSON-LD from Web Pages and Verify Its Claims
Inventory embedded JSON-LD with a tested Python example, select the right entity, and check extracted fields against readable page evidence.
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Inventory embedded JSON-LD with a tested Python example, select the right entity, and check extracted fields against readable page evidence.
Check a page's robots rules for the actual crawler identity before an agent reads it. Use an explicit allow, deny, or defer decision and preserve the evidence across redirects and failed rule requests.
A practical way to split retrieved web pages into RAG chunks while keeping section context, source identity, and citations verifiable.
Preserve table header paths, raw cell text, units, notes and sources before normalizing web data. Includes tested acquisition and parsing examples plus review rules.
Choose a single-page Fetch, query-driven Search, or bounded site crawl by the URLs you know and the coverage you need. Includes a tested public probe and verification checklist.
Design narrow agent web tools with validated inputs, bounded content, URL identity, explicit errors, and usage fields. Includes a tested public-page adapter.
Check extracted documents for missing facts, sidebar noise, broken structure, and unknown provenance before admitting them to model context.
Build a reviewable screenshot evidence bundle with source URLs, separate capture times, companion text, file hashes, and explicit human review decisions.
Keep pricing observations comparable with billing and currency context, original text, screenshots, and review states. Includes a tested collector and evidence record.