# Structured Data Monitor AI Search (`junipr/structured-data-monitor-ai-search`) Actor
Audit structured data, schema health, rich-result readiness, and AI-search clarity across pages and sitemaps.
- **URL**: https://apify.com/junipr/structured-data-monitor-ai-search.md
- **Developed by:** [junipr](https://apify.com/junipr) (community)
- **Categories:** SEO tools, Developer tools, Automation
- **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, 0 bookmarks
- **User rating**: No ratings yet
## Pricing
from $6.50 / 1,000 source processeds
This Actor is paid per event. You are not charged for the Apify platform usage, but only a fixed price for specific events.
Learn more: https://docs.apify.com/platform/actors/running/actors-in-store#pay-per-event
## What's an Apify Actor?
Actors are a software tools running on the Apify platform, for all kinds of web data extraction and automation use cases.
In Batch mode, an Actor accepts a well-defined JSON input, performs an action which can take anything from a few seconds to a few hours,
and optionally produces a well-defined JSON output, datasets with results, or files in key-value store.
In Standby mode, an Actor provides a web server which can be used as a website, API, or an MCP server.
Actors are written with capital "A".
## How to integrate an Actor?
If asked about integration, you help developers integrate Actors into their projects.
You adapt to their stack and deliver integrations that are safe, well-documented, and production-ready.
The best way to integrate Actors is as follows.
In JavaScript/TypeScript projects, use official [JavaScript/TypeScript client](https://docs.apify.com/api/client/js/docs.md):
```bash
npm install apify-client
```
In Python projects, use official [Python client library](https://docs.apify.com/api/client/python/docs.md):
```bash
pip install apify-client
```
In shell scripts, use [Apify CLI](https://docs.apify.com/cli/docs.md):
````bash
# MacOS / Linux
curl -fsSL https://apify.com/install-cli.sh | bash
# Windows
irm https://apify.com/install-cli.ps1 | iex
```bash
In AI frameworks, you might use the [Apify MCP server](https://docs.apify.com/integrations/mcp.md).
If your project is in a different language, use the [REST API](https://docs.apify.com/api/v2.md).
For usage examples, see the [API](#api) section below.
For more details, see Apify documentation as [Markdown index](https://docs.apify.com/llms.txt) and [Markdown full-text](https://docs.apify.com/llms-full.txt).
# README
## 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
```json
{
"targets": [
{
"sourceId": "product-page",
"sourceUrl": "https://example.com/products/field-guide",
"html": "Field Guide",
"previousHtml": ""
}
],
"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
```json
{
"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.
# Actor input Schema
## `targets` (type: `array`):
HTML snapshots to audit. Defaults to a small product-page fixture that finishes quickly.
## `urls` (type: `array`):
Optional URLs to fetch only when fetchUrls is true. Leave empty for fixture/local runs.
## `fetchUrls` (type: `boolean`):
Fetch URLs over HTTP. Default is false to keep zero-config runs local and cheap.
## `comparePrevious` (type: `boolean`):
Compare previousHtml schema types against current html when available.
## `expectedSchemaTypes` (type: `array`):
Schema.org types expected on at least some audited pages.
## `requireAiSearchSignals` (type: `boolean`):
Warn when entity clarity signals such as @id, sameAs, author, or organization are absent.
## `maxItems` (type: `integer`):
Alias for Max Targets/Maximum Records used by the Junipr actor baseline. When both are provided, actor-specific limits still apply.
## `includeReport` (type: `boolean`):
Write summary JSON and Markdown report artifacts to the key-value store.
## `maxTargets` (type: `integer`):
Maximum pages or HTML snapshots to audit.
## `fetchTimeoutMs` (type: `integer`):
HTTP timeout for optional URL fetches.
## `maxHtmlBytes` (type: `integer`):
Maximum fetched HTML size accepted per URL.
## `dryRun` (type: `boolean`):
Validate input and write a dry-run summary without PPE charges or dataset output.
## `debug` (type: `boolean`):
Enable debug logs.
## Actor input object example
```json
{
"targets": [
{
"sourceId": "sample-product-page",
"sourceUrl": "https://example.com/products/field-guide",
"html": "Field Guide - Example Co
Field Guide
",
"previousHtml": "Field Guide - Example Co
Field Guide
"
}
],
"urls": [],
"fetchUrls": false,
"comparePrevious": true,
"expectedSchemaTypes": [
"Organization",
"WebSite",
"Article",
"Product",
"FAQPage",
"BreadcrumbList"
],
"requireAiSearchSignals": true,
"maxItems": 25,
"includeReport": true,
"maxTargets": 25,
"fetchTimeoutMs": 10000,
"maxHtmlBytes": 500000,
"dryRun": false,
"debug": false
}
```
# Actor output Schema
## `results` (type: `string`):
Each dataset row is one audited page or HTML snapshot with schema, rich-result, and AI-search readiness signals.
## `resultsJson` (type: `string`):
Full JSON array of audit results.
## `summaryJson` (type: `string`):
Run-level counts, scores, schema types, and top issues.
## `summaryReport` (type: `string`):
Markdown report describing structured-data audit results.
# API
You can run this Actor programmatically using our API. Below are code examples in JavaScript, Python, and CLI, as well as the OpenAPI specification and MCP server setup.
## JavaScript example
```javascript
import { ApifyClient } from 'apify-client';
// Initialize the ApifyClient with your Apify API token
// Replace the '' with your token
const client = new ApifyClient({
token: '',
});
// Prepare Actor input
const input = {};
// Run the Actor and wait for it to finish
const run = await client.actor("junipr/structured-data-monitor-ai-search").call(input);
// Fetch and print Actor results from the run's dataset (if any)
console.log('Results from dataset');
console.log(`💾 Check your data here: https://console.apify.com/storage/datasets/${run.defaultDatasetId}`);
const { items } = await client.dataset(run.defaultDatasetId).listItems();
items.forEach((item) => {
console.dir(item);
});
// 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/js/docs
```
## Python example
```python
from apify_client import ApifyClient
# Initialize the ApifyClient with your Apify API token
# Replace '' with your token.
client = ApifyClient("")
# Prepare the Actor input
run_input = {}
# Run the Actor and wait for it to finish
run = client.actor("junipr/structured-data-monitor-ai-search").call(run_input=run_input)
# Fetch and print Actor results from the run's dataset (if there are any)
print("💾 Check your data here: https://console.apify.com/storage/datasets/" + run["defaultDatasetId"])
for item in client.dataset(run["defaultDatasetId"]).iterate_items():
print(item)
# 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/python/docs/quick-start
```
## CLI example
```bash
echo '{}' |
apify call junipr/structured-data-monitor-ai-search --silent --output-dataset
```
## MCP server setup
```json
{
"mcpServers": {
"apify": {
"command": "npx",
"args": [
"mcp-remote",
"https://mcp.apify.com/?tools=junipr/structured-data-monitor-ai-search",
"--header",
"Authorization: Bearer "
]
}
}
}
```
## OpenAPI specification
```json
{
"openapi": "3.0.1",
"info": {
"title": "Structured Data Monitor AI Search",
"description": "Audit structured data, schema health, rich-result readiness, and AI-search clarity across pages and sitemaps.",
"version": "1.0",
"x-build-id": "bmQNfqsbNKcMa4bvB"
},
"servers": [
{
"url": "https://api.apify.com/v2"
}
],
"paths": {
"/acts/junipr~structured-data-monitor-ai-search/run-sync-get-dataset-items": {
"post": {
"operationId": "run-sync-get-dataset-items-junipr-structured-data-monitor-ai-search",
"x-openai-isConsequential": false,
"summary": "Executes an Actor, waits for its completion, and returns Actor's dataset items in response.",
"tags": [
"Run Actor"
],
"requestBody": {
"required": true,
"content": {
"application/json": {
"schema": {
"$ref": "#/components/schemas/inputSchema"
}
}
}
},
"parameters": [
{
"name": "token",
"in": "query",
"required": true,
"schema": {
"type": "string"
},
"description": "Enter your Apify token here"
}
],
"responses": {
"200": {
"description": "OK"
}
}
}
},
"/acts/junipr~structured-data-monitor-ai-search/runs": {
"post": {
"operationId": "runs-sync-junipr-structured-data-monitor-ai-search",
"x-openai-isConsequential": false,
"summary": "Executes an Actor and returns information about the initiated run in response.",
"tags": [
"Run Actor"
],
"requestBody": {
"required": true,
"content": {
"application/json": {
"schema": {
"$ref": "#/components/schemas/inputSchema"
}
}
}
},
"parameters": [
{
"name": "token",
"in": "query",
"required": true,
"schema": {
"type": "string"
},
"description": "Enter your Apify token here"
}
],
"responses": {
"200": {
"description": "OK",
"content": {
"application/json": {
"schema": {
"$ref": "#/components/schemas/runsResponseSchema"
}
}
}
}
}
}
},
"/acts/junipr~structured-data-monitor-ai-search/run-sync": {
"post": {
"operationId": "run-sync-junipr-structured-data-monitor-ai-search",
"x-openai-isConsequential": false,
"summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.",
"tags": [
"Run Actor"
],
"requestBody": {
"required": true,
"content": {
"application/json": {
"schema": {
"$ref": "#/components/schemas/inputSchema"
}
}
}
},
"parameters": [
{
"name": "token",
"in": "query",
"required": true,
"schema": {
"type": "string"
},
"description": "Enter your Apify token here"
}
],
"responses": {
"200": {
"description": "OK"
}
}
}
}
},
"components": {
"schemas": {
"inputSchema": {
"type": "object",
"properties": {
"targets": {
"title": "HTML Targets",
"type": "array",
"description": "HTML snapshots to audit. Defaults to a small product-page fixture that finishes quickly.",
"items": {
"type": "object"
},
"default": [
{
"sourceId": "sample-product-page",
"sourceUrl": "https://example.com/products/field-guide",
"html": "Field Guide - Example Co