Structured Data Monitor AI Search

Store Positioning

Store title: Structured Data Monitor AI Search

Short description: Audit structured data, schema health, rich-result readiness, and AI-search clarity across pages and sitemaps.

SEO title: Structured Data Monitor AI Search — evidence-backed report and intelligence workflow

SEO description: Audit structured data, schema health, rich-result readiness, and AI-search clarity across pages and sitemaps. Use it to turn sources into decision-ready rollups, executive summaries, and evidence-backed recommendations.

Categories: SEO_TOOLS, DEVELOPER_TOOLS, AUTOMATION

Keywords: structured, data, monitor, ai, search, structured data, schema, data qa, sitemap, premium report/intelligence actor

Fixed-Inclusive PPE Pricing

This actor uses pay-per-event pricing. Event prices include Apify platform usage; users are not expected to pay a separate platform-usage pass-through charge for the configured pricing model.

• Tier: R1 — Premium report/intelligence actor
• Primary event: source-processed at $0.00892 base
• Default max charge: $25.00
• Store discounts: FREE/BRONZE base, SILVER discounted, GOLD deepest approved discount

Event set:

• actor-start: base $0.02000, GOLD $0.01600. Structured Data Monitor Ai Search: charged when actor start is completed. The price includes Apify platform usage; no separate usage pass-through is intended.
• source-processed: base $0.00892, GOLD $0.00714. Structured Data Monitor Ai Search: charged when source processed is completed. The price includes Apify platform usage; no separate usage pass-through is intended.
• finding-emitted: base $0.00892, GOLD $0.00714. Structured Data Monitor Ai Search: charged when finding emitted is completed. The price includes Apify platform usage; no separate usage pass-through is intended.
• report-section-generated: base $0.03600, GOLD $0.02880. Structured Data Monitor Ai Search: charged when report section generated is completed. The price includes Apify platform usage; no separate usage pass-through is intended.
• executive-report-generated: base $0.25000, GOLD $0.20000. Structured Data Monitor Ai Search: charged when executive report generated is completed. The price includes Apify platform usage; no separate usage pass-through is intended.

Public Task Concepts

• Compare previous and current Structured Data Monitor AI Search snapshots
• Monitor Structured Data Monitor AI Search changes for a capped watchlist
• Build a Structured Data Monitor AI Search change digest from supplied evidence
• Prioritize high-impact Structured Data Monitor AI Search changes
• Export Structured Data Monitor AI Search change rows for client review

Audit structured data, schema health, rich-result readiness, and AI-search clarity across HTML snapshots or small URL lists.

What This Actor Does

Structured Data Monitor AI Search checks page HTML for JSON-LD, microdata item types, rich-result candidate schema, entity clarity signals, canonical metadata, noindex blockers, and previous/current schema regressions. It emits one dataset row per target plus optional JSON and Markdown summary artifacts.

The default run uses a built-in product-page HTML fixture, so it produces useful output without fetching a live website or spending extra platform usage.

What This Actor Does Not Do

• It does not guarantee Google rich results, AI-search citations, rankings, or traffic.
• It does not use private search-engine APIs.
• It does not validate every Schema.org property exhaustively.
• It does not collect sensitive personal data.
• It does not provide legal, medical, or financial advice.

Best Use Cases

• Monitor whether product, article, FAQ, organization, or breadcrumb schema disappeared after a deploy.
• Check if JSON-LD parses before publishing SEO changes.
• Flag noindex pages that still have rich-result schema.
• Compare previous and current HTML snapshots for schema-type regressions.
• Prepare a concise structured-data QA report for SEO, AEO, and AI-search workflows.

Input Fields

• targets: HTML snapshots with optional sourceId, sourceUrl, html, and previousHtml.
• urls: Optional URLs to fetch only when fetchUrls is true.
• fetchUrls: Fetch URL targets over HTTP. Defaults to false so zero-config runs are local and cheap.
• comparePrevious: Compare previousHtml schema types against current html.
• expectedSchemaTypes: Schema.org types expected on at least some audited pages.
• requireAiSearchSignals: Warn when entity clarity signals such as @id, sameAs, author, publisher, person, organization, or local business are absent.
• includeReport: Write STRUCTURED_DATA_RESULTS.json, STRUCTURED_DATA_SUMMARY.json, and STRUCTURED_DATA_REPORT.md to the key-value store.
• maxTargets: Maximum targets to audit. Default is 25; hard cap is 250.
• fetchTimeoutMs: HTTP timeout for optional URL fetches.
• maxHtmlBytes: Maximum fetched HTML size per URL.
• debug: Enable extra troubleshooting logs.

Example Input

{
  "targets": [
    {
      "sourceId": "product-page",
      "sourceUrl": "https://example.com/products/field-guide",
      "html": "<html><head><title>Field Guide</title><link rel=\"canonical\" href=\"https://example.com/products/field-guide\"><meta name=\"description\" content=\"Field guide\"><script type=\"application/ld+json\">{\"@context\":\"https://schema.org\",\"@type\":\"Product\",\"name\":\"Field Guide\"}</script></head></html>",
      "previousHtml": "<html><head><script type=\"application/ld+json\">{\"@context\":\"https://schema.org\",\"@type\":\"Product\",\"name\":\"Field Guide\"}</script></head></html>"
    }
  ],
  "fetchUrls": false,
  "comparePrevious": true,
  "expectedSchemaTypes": ["Organization", "Product", "BreadcrumbList"],
  "includeReport": true,
  "maxTargets": 10
}

Output Fields

Each dataset item represents one audited target:

• auditId: Stable hash for the target snapshot.
• sourceType, sourceId, and sourceUrl: Target provenance.
• status and severity: Overall result and highest issue severity.
• title, metaDescription, and canonicalUrl: Core page metadata.
• schemaTypes: Detected Schema.org types from JSON-LD and microdata.
• previousSchemaTypes, addedSchemaTypes, removedSchemaTypes, and changedSchemaTypes: Snapshot diff fields.
• richResultCandidateTypes: Detected schema types commonly eligible for rich-result features.
• schemaNodeCount, jsonLdBlockCount, validJsonLdBlockCount, invalidJsonLdBlockCount, and microdataItemTypeCount: Structured-data inventory.
• schemaHealthScore: 0-100 score for parseability, blockers, and issue severity.
• aiSearchReadinessScore: 0-100 score for entity clarity, canonical/meta signals, rich-result types, and date/FAQ/HowTo signals.
• Boolean signal fields such as hasEntitySignals, hasAuthorOrOrganization, hasRobotsNoindex, and hasOpenGraph.
• issues: Structured issue list.
• recommendation: Suggested next action.
• checkedAt: ISO audit timestamp.

The key-value store also contains:

• STRUCTURED_DATA_RESULTS.json: Full result array.
• STRUCTURED_DATA_SUMMARY.json: Run-level counts, scores, schema types, and top issues.
• STRUCTURED_DATA_REPORT.md: Human-readable summary.

Example Output

{
  "sourceId": "product-page",
  "sourceUrl": "https://example.com/products/field-guide",
  "status": "warn",
  "severity": "low",
  "schemaTypes": ["Brand", "BreadcrumbList", "ListItem", "Offer", "Organization", "Product"],
  "addedSchemaTypes": ["Brand", "BreadcrumbList", "ListItem", "Offer", "Organization"],
  "removedSchemaTypes": [],
  "richResultCandidateTypes": ["BreadcrumbList", "Product"],
  "schemaHealthScore": 98,
  "aiSearchReadinessScore": 90,
  "issueCount": 1,
  "recommendation": "Address the warnings and monitor schema changes after deployment."
}

Cost-Control Tips

• Use inline html snapshots for most QA and regression checks.
• Keep fetchUrls false unless you explicitly want the actor to perform HTTP requests.
• Use maxTargets between 1 and 10 for initial runs.
• Keep maxHtmlBytes low when fetching live pages.
• Use previous/current snapshots from your CI or crawler when possible.

Public Task Examples

This actor includes five prepared task concepts:

• Product page structured data check.
• Article AI-search clarity check.
• Schema regression diff.
• Invalid JSON-LD detector.
• Noindex structured data check.

FAQ

Does this guarantee rich results or AI-search visibility?

No. It checks technical structured-data and clarity signals that search engines and AI systems can use, but it cannot guarantee visibility, ranking, or citations.

Can it fetch live pages?

Yes, set fetchUrls to true and provide urls. The default is false to keep the Store quality run deterministic and cheap.

Does it need a browser?

No. This version parses supplied or fetched HTML. It does not render JavaScript.

Are diagnostics billed as dataset rows?

Troubleshooting

• missing-structured-data: Add valid JSON-LD or microdata matching the page type.
• invalid-json-ld: Paste the JSON-LD into a parser and fix trailing commas, comments, or malformed escaping.
• schema-type-removed: Compare the previous and current deployment to confirm whether the removal was intentional.
• robots-noindex: Remove noindex if the page should appear in search and AI answer surfaces.

Limitations

• HTML parsing is static; JavaScript-rendered schema is not detected unless included in the supplied HTML.
• The actor checks practical readiness signals, not complete Schema.org validation.
• Live URL fetching can fail due network, robots, auth, or anti-bot behavior; inline snapshots are more deterministic.

Source And Safety Notes

Only audit pages and HTML snapshots you are authorized to inspect. Do not upload sensitive personal data in page snapshots.

Changelog

• 1.0.0: Initial production build with JSON-LD parsing, schema diffing, AI-search readiness scoring, KVS reports, PPE billing, examples, and fixture tests.