# Structured Data Validator (JSON-LD / OG) (`jungle_synthesizer/structured-data-validator-pro`) Actor
Extract and validate structured data from any URL: JSON-LD, Open Graph, Twitter Cards, microdata, RDFa, meta tags. Local schema.org validation. Flags Google rich-result eligibility and AI-discovery readiness. Pure HTTP. Built for SEO audits and structured-data debugging at scale.
- **URL**: https://apify.com/jungle\_synthesizer/structured-data-validator-pro.md
- **Developed by:** [BowTiedRaccoon](https://apify.com/jungle_synthesizer) (community)
- **Categories:** SEO tools, Developer tools, AI
- **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, NaN bookmarks
- **User rating**: No ratings yet
## Pricing
Pay per event
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.md):
```bash
npm install apify-client
```
In Python projects, use official [Python client library](https://docs.apify.com/api/client/python.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/platform/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 Validator Pro (JSON-LD, Open Graph, Schema.org)
Extract and validate structured data from any URL — JSON-LD, Open Graph, Twitter Cards, microdata, RDFa, and meta tags — in one pass. Local schema.org validation, Google rich-result eligibility check, and an AI-discovery readiness score. Pure HTTP, no browser.
---
### Structured Data Validator Features
- Extracts six structured-data formats per URL: JSON-LD, Open Graph, Twitter Cards, microdata, RDFa, and meta tags.
- Validates JSON-LD blocks against a bundled schema.org rule set with required-field gates per type (Article, Recipe, Product, Event, FAQPage, HowTo, VideoObject).
- Flags Google rich-result eligibility — true when any block satisfies the relevant rich-result requirement set.
- Scores AI-discovery readiness on a 0-100 scale, weighted toward the signals LLM crawlers actually use.
- Detects and lists every schema.org `@type` found across all formats.
- Optional raw-HTML dump to KVS for offline debugging.
- Pure HTTP fetch via CheerioCrawler — no browser, no proxy by default. The cheap default.
---
### Who Uses Structured Data Audits?
- **SEO teams** — audit rich-result eligibility across a sitemap before chasing rank changes that turn out to be markup bugs.
- **Content engineering** — verify JSON-LD blocks ship with every article, product, or recipe page.
- **AI / LLM-discovery auditors** — score how well a site speaks to AI crawlers, since LLMs lean heavily on structured data.
- **Migration QA** — diff structured-data coverage before and after a CMS swap or template refactor.
- **Competitive research** — see exactly which schema.org types competitors mark up, and which ones they miss.
---
### How Structured Data Validator Works
1. Pass in a list of URLs. The actor caps at 15 per run by default to stay inside the Apify tester's 5-minute timeout.
2. CoreCrawler fetches the static HTML over plain HTTP. The handler runs all six extractors in parallel.
3. JSON-LD blocks are validated against the bundled schema.org rule set. Each issue is recorded with severity, path, type, and message.
4. The actor flags Google rich-result eligibility and computes the AI-discovery readiness score, then emits one row per URL.
---
### Input
```json
{
"urls": [
"https://schema.org/Article",
"https://www.apify.com"
],
"maxItems": 5,
"extractWhich": ["json-ld", "open-graph", "twitter-cards", "microdata", "rdfa", "meta-tags"],
"validateAgainst": "schema.org",
"includeRawHtml": false
}
````
| Field | Type | Default | Description |
|-------|------|---------|-------------|
| `urls` | array | required | URLs to extract and validate structured data from. |
| `maxItems` | integer | 5 | Hard cap on URLs per run. Range 1-15. |
| `extractWhich` | array | all six | Formats to extract: `json-ld`, `open-graph`, `twitter-cards`, `microdata`, `rdfa`, `meta-tags`. |
| `validateAgainst` | enum | `schema.org` | Validation rule set. `schema.org` runs the bundled gates; `none` skips validation. |
| `includeRawHtml` | boolean | false | Save the fetched HTML to KVS and link via `rawHtmlKvsKey` on each row. |
| `proxyConfiguration` | object | none | Optional. Default is no proxy. |
***
### Structured Data Validator Output Fields
```json
{
"url": "https://www.apify.com",
"finalUrl": "https://www.apify.com/",
"jsonLd": [
"{\"@context\":\"https://schema.org\",\"@type\":\"Organization\",\"name\":\"Apify\"}"
],
"openGraph": {
"og:title": "Apify - The Web Scraping Platform",
"og:type": "website",
"og:url": "https://apify.com/",
"og:image": "https://apify.com/img/social.png"
},
"twitterCard": { "twitter:card": "summary_large_image" },
"microdata": [],
"rdfa": [],
"metaTags": { "viewport": "width=device-width, initial-scale=1", "robots": "index, follow" },
"validationErrors": [],
"schemaTypes": ["Organization"],
"googleRichResultEligible": false,
"aiDiscoveryReadiness": {
"hasJsonLd": true,
"hasArticleSchema": false,
"hasFAQ": false,
"hasHowTo": false,
"hasOpenGraph": true,
"score": 60
},
"rawHtmlKvsKey": "",
"status": "success",
"errorMsg": "",
"extractedAt": "2026-04-30T12:00:00Z"
}
```
| Field | Type | Description |
|-------|------|-------------|
| `url` | string | Audited URL. |
| `finalUrl` | string | URL after redirects. |
| `jsonLd` | array | Parsed JSON-LD blocks as JSON-stringified objects (CSV/Excel safe). |
| `openGraph` | object | All `og:*` meta tags flattened into a single object. |
| `twitterCard` | object | All `twitter:*` meta tags flattened into a single object. |
| `microdata` | array | `itemscope`/`itemtype` blocks as JSON-stringified objects. |
| `rdfa` | array | `property`/`typeof`/`resource` blocks as JSON-stringified objects. |
| `metaTags` | object | All `` and `` tags as a flat object. |
| `validationErrors` | array | Issues formatted as ` [] () `. |
| `schemaTypes` | array | Detected schema.org types (e.g. Article, Recipe, Product). |
| `googleRichResultEligible` | boolean | True when any block satisfies a Google rich-result requirement set. |
| `aiDiscoveryReadiness` | object | `{hasJsonLd, hasArticleSchema, hasFAQ, hasHowTo, hasOpenGraph, score 0-100}`. |
| `rawHtmlKvsKey` | string | KVS key for raw HTML when `includeRawHtml=true` (else empty). |
| `status` | string | `success`, `not_found`, or `error`. |
| `errorMsg` | string | Error message on failure (empty on success). |
| `extractedAt` | string | ISO timestamp. |
***
### Pricing
Token charge — functionally free. Apify rejects truly $0 PPE events, so the per-record price is the smallest practical floor.
| Event | Price |
|-------|-------|
| Actor start | $0.10 |
| Per record | $0.0001 |
| Volume | Cost |
|--------|------|
| 100 records | $0.11 |
| 1,000 records | $0.20 |
| 10,000 records | $1.10 |
This actor is the cheap discovery primitive that pairs with paid downstream actors. Audit liberally.
***
### Limits
- `maxItems` caps at 15 per run by default — sized for the Apify tester's 5-minute timeout.
- The schema.org validator covers the common Google-rich-result types (Article, Recipe, Product, Event, FAQPage, HowTo, VideoObject). Other types parse but skip required-field validation.
- The actor uses HTTP fetch only. Sites that require JS rendering for structured data won't surface anything — pair with a render crawler upstream.
- `includeRawHtml=true` writes one KVS entry per URL. KVS quotas apply.
- Validation severity is internal — `validationErrors` strings start with `error`, `warn`, or `info` for downstream filtering.
***
### Related Actors
- **Sitemap Walker Pro** — feed discovered URLs straight into this validator for site-wide structured-data audits.
- **SSL & Security Headers Checker** — pair for full SEO + security audits per URL.
- **Angular SSR State Extractor** — for sites where the structured data lives inside Angular's TransferState payload.
***
### Need More Features?
Need additional schema.org types, custom validation rules, or a render-crawler variant? [File an issue](https://console.apify.com/actors/issues) or get in touch.
### Why Use Structured Data Validator Pro?
- **Functionally free** — $0.0001 per record. Audit your whole sitemap and barely move the needle.
- **Six formats, one pass** — JSON-LD, Open Graph, Twitter Cards, microdata, RDFa, and meta tags in a single dataset row. Most tools cover one, maybe two.
- **AI-discovery score baked in** — rich-result eligibility plus an LLM-readiness score, so you know how the site reads to both Google and Claude.
***
Built by [OrbTop](https://orbtop.com).
# Actor input Schema
## `sp_intended_usage` (type: `string`):
Please describe how you plan to use the data extracted by this crawler.
## `sp_improvement_suggestions` (type: `string`):
Provide any feedback or suggestions for improvements.
## `sp_contact` (type: `string`):
Provide your email address so we can get in touch with you.
## `urls` (type: `array`):
List of URLs to extract + validate structured data from.
## `maxItems` (type: `integer`):
Hard cap on URLs processed per run. Keep small for the Apify console tester (5-min timeout).
## `extractWhich` (type: `array`):
Which structured-data formats to extract. Pick from: json-ld, open-graph, twitter-cards, microdata, rdfa, meta-tags.
## `validateAgainst` (type: `string`):
Validation rule set. 'schema.org' runs the bundled type / required-field gates. 'none' skips validation -- pure extraction only.
## `includeRawHtml` (type: `boolean`):
Save the fetched HTML to KVS and link via rawHtmlKvsKey on each row.
## `proxyConfiguration` (type: `object`):
Optional. Default is no proxy.
## Actor input object example
```json
{
"sp_intended_usage": "Describe your intended use...",
"sp_improvement_suggestions": "Share your suggestions here...",
"sp_contact": "Share your email here...",
"urls": [
"https://schema.org/Article"
],
"maxItems": 5,
"extractWhich": [
"json-ld",
"open-graph",
"twitter-cards",
"microdata",
"rdfa",
"meta-tags"
],
"validateAgainst": "schema.org",
"includeRawHtml": false,
"proxyConfiguration": {
"useApifyProxy": false
}
}
```
# Actor output Schema
## `results` (type: `string`):
No description
# 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 = {
"sp_intended_usage": "Describe your intended use...",
"sp_improvement_suggestions": "Share your suggestions here...",
"sp_contact": "Share your email here...",
"urls": [
"https://schema.org/Article"
],
"maxItems": 5,
"extractWhich": [
"json-ld",
"open-graph",
"twitter-cards",
"microdata",
"rdfa",
"meta-tags"
],
"validateAgainst": "schema.org",
"includeRawHtml": false,
"proxyConfiguration": {
"useApifyProxy": false
}
};
// Run the Actor and wait for it to finish
const run = await client.actor("jungle_synthesizer/structured-data-validator-pro").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 = {
"sp_intended_usage": "Describe your intended use...",
"sp_improvement_suggestions": "Share your suggestions here...",
"sp_contact": "Share your email here...",
"urls": ["https://schema.org/Article"],
"maxItems": 5,
"extractWhich": [
"json-ld",
"open-graph",
"twitter-cards",
"microdata",
"rdfa",
"meta-tags",
],
"validateAgainst": "schema.org",
"includeRawHtml": False,
"proxyConfiguration": { "useApifyProxy": False },
}
# Run the Actor and wait for it to finish
run = client.actor("jungle_synthesizer/structured-data-validator-pro").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 '{
"sp_intended_usage": "Describe your intended use...",
"sp_improvement_suggestions": "Share your suggestions here...",
"sp_contact": "Share your email here...",
"urls": [
"https://schema.org/Article"
],
"maxItems": 5,
"extractWhich": [
"json-ld",
"open-graph",
"twitter-cards",
"microdata",
"rdfa",
"meta-tags"
],
"validateAgainst": "schema.org",
"includeRawHtml": false,
"proxyConfiguration": {
"useApifyProxy": false
}
}' |
apify call jungle_synthesizer/structured-data-validator-pro --silent --output-dataset
```
## MCP server setup
```json
{
"mcpServers": {
"apify": {
"command": "npx",
"args": [
"mcp-remote",
"https://mcp.apify.com/?tools=jungle_synthesizer/structured-data-validator-pro",
"--header",
"Authorization: Bearer "
]
}
}
}
```
## OpenAPI specification
```json
{
"openapi": "3.0.1",
"info": {
"title": "Structured Data Validator (JSON-LD / OG)",
"description": "Extract and validate structured data from any URL: JSON-LD, Open Graph, Twitter Cards, microdata, RDFa, meta tags. Local schema.org validation. Flags Google rich-result eligibility and AI-discovery readiness. Pure HTTP. Built for SEO audits and structured-data debugging at scale.",
"version": "0.1",
"x-build-id": "v5AradFb8oJTGpt8a"
},
"servers": [
{
"url": "https://api.apify.com/v2"
}
],
"paths": {
"/acts/jungle_synthesizer~structured-data-validator-pro/run-sync-get-dataset-items": {
"post": {
"operationId": "run-sync-get-dataset-items-jungle_synthesizer-structured-data-validator-pro",
"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": {
"$ref": "#/components/schemas/inputSchema"
}
}
}
},
"parameters": [
{
"name": "token",
"in": "query",
"required": true,
"schema": {
"type": "string"
},
"description": "Enter your Apify token here"
}
],
"responses": {
"200": {
"description": "OK"
}
}
}
},
"/acts/jungle_synthesizer~structured-data-validator-pro/runs": {
"post": {
"operationId": "runs-sync-jungle_synthesizer-structured-data-validator-pro",
"x-openai-isConsequential": false,
"summary": "Executes an Actor and returns information about the initiated run in response.",
"tags": [
"Run Actor"
],
"requestBody": {
"required": true,
"content": {
"application/json": {
"schema": {
"$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": {
"$ref": "#/components/schemas/runsResponseSchema"
}
}
}
}
}
}
},
"/acts/jungle_synthesizer~structured-data-validator-pro/run-sync": {
"post": {
"operationId": "run-sync-jungle_synthesizer-structured-data-validator-pro",
"x-openai-isConsequential": false,
"summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.",
"tags": [
"Run Actor"
],
"requestBody": {
"required": true,
"content": {
"application/json": {
"schema": {
"$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",
"required": [
"sp_intended_usage",
"sp_improvement_suggestions",
"urls"
],
"properties": {
"sp_intended_usage": {
"title": "What is the intended usage of this data?",
"minLength": 1,
"type": "string",
"description": "Please describe how you plan to use the data extracted by this crawler."
},
"sp_improvement_suggestions": {
"title": "How can we improve this crawler for you?",
"minLength": 1,
"type": "string",
"description": "Provide any feedback or suggestions for improvements."
},
"sp_contact": {
"title": "Contact Email",
"minLength": 1,
"type": "string",
"description": "Provide your email address so we can get in touch with you."
},
"urls": {
"title": "URLs",
"type": "array",
"description": "List of URLs to extract + validate structured data from.",
"items": {
"type": "string"
}
},
"maxItems": {
"title": "Max Items",
"minimum": 1,
"maximum": 15,
"type": "integer",
"description": "Hard cap on URLs processed per run. Keep small for the Apify console tester (5-min timeout).",
"default": 5
},
"extractWhich": {
"title": "Extract Which Formats",
"type": "array",
"description": "Which structured-data formats to extract. Pick from: json-ld, open-graph, twitter-cards, microdata, rdfa, meta-tags.",
"items": {
"type": "string"
}
},
"validateAgainst": {
"title": "Validate Against",
"enum": [
"schema.org",
"none"
],
"type": "string",
"description": "Validation rule set. 'schema.org' runs the bundled type / required-field gates. 'none' skips validation -- pure extraction only.",
"default": "schema.org"
},
"includeRawHtml": {
"title": "Include Raw HTML",
"type": "boolean",
"description": "Save the fetched HTML to KVS and link via rawHtmlKvsKey on each row.",
"default": false
},
"proxyConfiguration": {
"title": "Proxy",
"type": "object",
"description": "Optional. Default is no proxy."
}
}
},
"runsResponseSchema": {
"type": "object",
"properties": {
"data": {
"type": "object",
"properties": {
"id": {
"type": "string"
},
"actId": {
"type": "string"
},
"userId": {
"type": "string"
},
"startedAt": {
"type": "string",
"format": "date-time",
"example": "2025-01-08T00:00:00.000Z"
},
"finishedAt": {
"type": "string",
"format": "date-time",
"example": "2025-01-08T00:00:00.000Z"
},
"status": {
"type": "string",
"example": "READY"
},
"meta": {
"type": "object",
"properties": {
"origin": {
"type": "string",
"example": "API"
},
"userAgent": {
"type": "string"
}
}
},
"stats": {
"type": "object",
"properties": {
"inputBodyLen": {
"type": "integer",
"example": 2000
},
"rebootCount": {
"type": "integer",
"example": 0
},
"restartCount": {
"type": "integer",
"example": 0
},
"resurrectCount": {
"type": "integer",
"example": 0
},
"computeUnits": {
"type": "integer",
"example": 0
}
}
},
"options": {
"type": "object",
"properties": {
"build": {
"type": "string",
"example": "latest"
},
"timeoutSecs": {
"type": "integer",
"example": 300
},
"memoryMbytes": {
"type": "integer",
"example": 1024
},
"diskMbytes": {
"type": "integer",
"example": 2048
}
}
},
"buildId": {
"type": "string"
},
"defaultKeyValueStoreId": {
"type": "string"
},
"defaultDatasetId": {
"type": "string"
},
"defaultRequestQueueId": {
"type": "string"
},
"buildNumber": {
"type": "string",
"example": "1.0.0"
},
"containerUrl": {
"type": "string"
},
"usage": {
"type": "object",
"properties": {
"ACTOR_COMPUTE_UNITS": {
"type": "integer",
"example": 0
},
"DATASET_READS": {
"type": "integer",
"example": 0
},
"DATASET_WRITES": {
"type": "integer",
"example": 0
},
"KEY_VALUE_STORE_READS": {
"type": "integer",
"example": 0
},
"KEY_VALUE_STORE_WRITES": {
"type": "integer",
"example": 1
},
"KEY_VALUE_STORE_LISTS": {
"type": "integer",
"example": 0
},
"REQUEST_QUEUE_READS": {
"type": "integer",
"example": 0
},
"REQUEST_QUEUE_WRITES": {
"type": "integer",
"example": 0
},
"DATA_TRANSFER_INTERNAL_GBYTES": {
"type": "integer",
"example": 0
},
"DATA_TRANSFER_EXTERNAL_GBYTES": {
"type": "integer",
"example": 0
},
"PROXY_RESIDENTIAL_TRANSFER_GBYTES": {
"type": "integer",
"example": 0
},
"PROXY_SERPS": {
"type": "integer",
"example": 0
}
}
},
"usageTotalUsd": {
"type": "number",
"example": 0.00005
},
"usageUsd": {
"type": "object",
"properties": {
"ACTOR_COMPUTE_UNITS": {
"type": "integer",
"example": 0
},
"DATASET_READS": {
"type": "integer",
"example": 0
},
"DATASET_WRITES": {
"type": "integer",
"example": 0
},
"KEY_VALUE_STORE_READS": {
"type": "integer",
"example": 0
},
"KEY_VALUE_STORE_WRITES": {
"type": "number",
"example": 0.00005
},
"KEY_VALUE_STORE_LISTS": {
"type": "integer",
"example": 0
},
"REQUEST_QUEUE_READS": {
"type": "integer",
"example": 0
},
"REQUEST_QUEUE_WRITES": {
"type": "integer",
"example": 0
},
"DATA_TRANSFER_INTERNAL_GBYTES": {
"type": "integer",
"example": 0
},
"DATA_TRANSFER_EXTERNAL_GBYTES": {
"type": "integer",
"example": 0
},
"PROXY_RESIDENTIAL_TRANSFER_GBYTES": {
"type": "integer",
"example": 0
},
"PROXY_SERPS": {
"type": "integer",
"example": 0
}
}
}
}
}
}
}
}
}
}
```