# Structured Data Extractor — JSON-LD, Microdata, RDFa (`q_services/structured-data-extractor`) Actor

Extract schema.org structured data (JSON-LD, Microdata, RDFa) from any URLs. Clean output for SEO audits and AI data pipelines.

- **URL**: https://apify.com/q_services/structured-data-extractor.md
- **Developed by:** [Q Services](https://apify.com/q_services) (community)
- **Categories:** SEO tools, AI
- **Features:** Limited permissions
- **Stats:** 3 total users, 0 monthly users, 100.0% runs succeeded, 0 bookmarks
- **User rating**: No ratings yet

## Pricing

from $0.50 / 1,000 results

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/actors/running/actors-in-store.md#pay-per-event

## What's an Apify Actor?

An Actor is a serverless cloud program that runs on the Apify platform. It has two run modes.
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.

Apify vocabulary and the platform model are defined once, in the agent quickstart at https://apify.com/agents.md.

## 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.

Do not guess an integration path. Every one of them is in the agent quickstart at https://apify.com/agents.md: the Apify MCP server, Agent Skills with the Apify CLI, the JavaScript and Python clients, the REST API, and the account-free path for an agent with no human to sign in. It also carries the rule on stating cost before the first paid run.

For examples already wired to this Actor's own input schema, see the [API](#api) section below.

Each client library has reference documentation the quickstart does not restate: [JavaScript/TypeScript](https://docs.apify.com/api/client/js/docs.md) (`npm install apify-client`) and [Python](https://docs.apify.com/api/client/python/docs.md) (`pip install apify-client`).

# README

## Structured Data Extractor — JSON-LD, Microdata, RDFa

Extrait les **données structurées schema.org** (JSON-LD, Microdata, RDFa) de n'importe quelle liste d'URLs et retourne un dataset propre, prêt pour l'audit SEO ou l'alimentation de pipelines IA.

Idéal pour : vérifier ses rich snippets Google, extraire des données produit/article/événement structurées, alimenter un LLM avec des données propres et typées.

**Exemple de résultat :**

```json
{
    "url": "https://example.com/produit/123",
    "schemaTypes": ["Product", "Offer", "BreadcrumbList"],
    "blocksFound": 2,
    "jsonLd": [ { "@context": "https://schema.org", "@type": "Product", "name": "..." } ],
    "microdata": [],
    "rdfa": [],
    "scrapedAt": "2026-07-10T10:30:00.000Z"
}
```

### Comment l'utiliser

1. Collez vos **URLs**
2. Cliquez sur **Start** — les blocs structurés apparaissent dans l'onglet *Dataset*

### Combien ça coûte ?

| Événement | Prix |
|---|---|
| Démarrage du run | 0,005 $ |
| Par URL traitée | 0,0005 $ |

**Exemple : 1 000 URLs ≈ 0,51 $.**

### Champs retournés

`url`, `schemaTypes` (liste des types schema.org détectés), `blocksFound`, `jsonLd` (objets bruts), `microdata`, `rdfa`, `scrapedAt`.

### Limitations

- Le contenu injecté par JavaScript côté client n'est pas vu (extraction HTML statique) — la plupart des données structurées SEO sont dans le HTML initial
- Le JSON-LD syntaxiquement invalide est ignoré (avec tolérance sur la virgule finale)

### FAQ

**Pourquoi extraire les données structurées ?** C'est le format que Google et les IA lisent pour comprendre une page. Les auditer garantit des rich snippets corrects.

**Puis-je l'utiliser via API ?** Oui, appelable par API et via MCP.

# Actor input Schema

## `startUrls` (type: `array`):

Pages dont extraire les données structurées (JSON-LD, Microdata, RDFa).

## `maxResults` (type: `integer`):

L'Actor s'arrête (et arrête de facturer) une fois ce nombre atteint.

## `proxyConfiguration` (type: `object`):

Configuration proxy. Le proxy Apify par défaut convient dans la plupart des cas.

## Actor input object example

```json
{
  "startUrls": [
    "https://en.wikipedia.org/wiki/Web_scraping"
  ],
  "maxResults": 1000,
  "proxyConfiguration": {
    "useApifyProxy": true
  }
}
```

# 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 '<YOUR_API_TOKEN>' with your token
const client = new ApifyClient({
    token: '<YOUR_API_TOKEN>',
});

// Prepare Actor input
const input = {
    "startUrls": [
        "https://en.wikipedia.org/wiki/Web_scraping"
    ],
    "maxResults": 1000,
    "proxyConfiguration": {
        "useApifyProxy": true
    }
};

// Run the Actor and wait for it to finish
const run = await client.actor("q_services/structured-data-extractor").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 '<YOUR_API_TOKEN>' with your token.
client = ApifyClient("<YOUR_API_TOKEN>")

# Prepare the Actor input
run_input = {
    "startUrls": ["https://en.wikipedia.org/wiki/Web_scraping"],
    "maxResults": 1000,
    "proxyConfiguration": { "useApifyProxy": True },
}

# Run the Actor and wait for it to finish
run = client.actor("q_services/structured-data-extractor").call(run_input=run_input)

# Fetch and print Actor results from the run's dataset (if there are any)
print(f"💾 Check your data here: https://console.apify.com/storage/datasets/{run.default_dataset_id}")
for item in client.dataset(run.default_dataset_id).iterate_items():
    print(item)

# 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/python/docs/quick-start

```

## CLI example

```bash
echo '{
  "startUrls": [
    "https://en.wikipedia.org/wiki/Web_scraping"
  ],
  "maxResults": 1000,
  "proxyConfiguration": {
    "useApifyProxy": true
  }
}' |
apify call q_services/structured-data-extractor --silent --output-dataset

```

## MCP server setup

```json
{
    "mcpServers": {
        "apify": {
            "type": "http",
            "url": "https://mcp.apify.com/?tools=fetch-actor-details,q_services/structured-data-extractor"
        }
    }
}
```

The hosted server signs you in with OAuth on first connect, so no API token belongs in this config. Clients without OAuth support can send an `Authorization: Bearer <APIFY_API_TOKEN>` header instead, using a token from API & Integrations in Apify Console (https://console.apify.com/settings/integrations).

## OpenAPI specification

Download the OpenAPI definition: https://api.apify.com/v2/actors/IaTA4C4yYx3oRUWco/builds/J6RTbVjFnIm54wxNP/openapi.json