JSON-LD Rich Result & Schema Drift Auditor

Use this Actor to check json-ld rich result and schema drift and return a compact structured result with evidence and actionable issues. Audit JSON-LD for rich-result readiness, schema coverage, semantic drift and regressions with fingerprints, release gates and structured remediation.

Quick start

Start with this working example and replace the target values with your own:

{
  "urls": [
    "https://www.python.org",
    "https://www.wikipedia.org"
  ]
}

The Actor writes structured results to the default Apify Dataset and can be used from the Store, API, schedules, Tasks, automations and MCP-compatible AI workflows.

Input

• urls — URLs: Public HTTP(S) URLs to inspect
• previous_results — Previous structured-data snapshots: Optional map keyed by URL. Enables semantic schema diff, regression detection and rich-result readiness tracking.

All integration and advanced analysis fields are optional. The default example is intentionally runnable without configuring MCP or a previous-run baseline.

Output

The Dataset exposes predictable machine-readable output. Representative fields include results.

Pricing

This Actor uses Pay Per Event. The current factory base price is $0.001200 per result event. The Apify Store remains the source of truth for the price and plan/tier details shown to the buyer.

Use cases

• Run the buyer-ready workflows exposed as Apify Tasks without preparing a custom integration first.
• Use the Actor from API or schedules for recurring collection, comparison or monitoring.
• Feed the structured Dataset output into spreadsheets, databases, automations or AI agents.
• Compare repeat runs when the product supports snapshots or previous-run inputs.

Automation and AI

Use the same Actor through Apify API, schedules, public Tasks and the Apify MCP server. Outputs are structured for downstream workflows and AI agents rather than requiring manual copy/paste.

Limitations

• Public websites and upstream APIs can change markup, access rules, rate limits or field availability without notice.
• Fields that are not publicly available are returned as unavailable or omitted rather than fabricated.
• Analytical outputs depend on the quality and coverage of the supplied or collected source data.
• Treat marketplace, reputation, workforce, safety or commercial signals as decision support and validate material decisions against the underlying source evidence.

Detailed documentation

Detailed documentation

Audit JSON-LD for rich-result readiness, schema coverage and semantic drift across public web pages. The Actor fingerprints schema state, compares previous snapshots, flags regressions, scores rich-result readiness and emits release/remediation actions for technical SEO, QA, agents and MCP-connected workflows.

Key features

• Fetches each public URL with safe redirect handling.
• Finds every <script type="application/ld+json"> block.
• Parses valid JSON-LD and reports malformed blocks with concise errors.
• Summarizes discovered @type values and schema.org context usage.
• Returns one structured dataset item per URL with an audit score and issues.

Input

{"urls":["https://www.python.org","https://www.wikipedia.org"]}

Output

Each dataset item contains the requested URL, final URL, HTTP status, JSON-LD block counts, valid/malformed counts, discovered types, score, and issues.

Example

A page with two valid JSON-LD blocks can return jsonldBlocks: 2, validBlocks: 2, discovered types such as Organization and WebSite, a score, and any structural issues. Malformed JSON-LD is retained as an explicit parse error instead of disappearing silently.

Why use this Actor

1. Malformed JSON-LD is visible instead of being silently ignored.
2. Type inventory per page makes monitoring schema changes straightforward.
3. Automation-first output gives one deterministic record per URL.
4. No paid SEO API or AI dependency, keeping run cost predictable.

Pricing

Pay per dataset result. One URL audit produces one primary chargeable result. The price is intentionally low for recurring QA and monitoring.

Good use cases

• Pre-release SEO checks
• Structured-data regression monitoring
• Agency technical audits
• Dataset generation for downstream QA workflows

Limitations

• Server-delivered HTML only; JSON-LD inserted exclusively by client-side JavaScript may not appear.
• This Actor checks structure and parseability, not eligibility for a specific search-engine rich result.
• Sites can block automated HTTP clients; failures are returned explicitly.

Privacy and safety

Only public HTTP(S) destinations are accepted. Private, loopback, link-local, and unsafe redirect destinations are rejected. The Actor requests minimal permissions and does not require credentials.