# Schema Audit - JSON-LD Structured Data Validator (`artifact-machine/artifact-machine-schema-audit`) Actor

Check JSON-LD structured data quality and rich-result readiness.

- **URL**: https://apify.com/artifact-machine/artifact-machine-schema-audit.md
- **Developed by:** [The Artifact Machine](https://apify.com/artifact-machine) (community)
- **Categories:** SEO tools, AI, Developer tools
- **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, 0 bookmarks
- **User rating**: No ratings yet

## Pricing

$1.00 / schema audit report

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 web data automations that power AI and operations. They run on the Apify platform to scrape websites, process data, connect APIs, and automate workflows.
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.

- **AI agents and MCP clients** — the [Apify MCP server](https://docs.apify.com/integrations/mcp.md) at `https://mcp.apify.com` (remote, streamable HTTP, OAuth on first use).
- **Agentic workflows and local Actor development** — [Agent Skills](https://apify.com/.well-known/agent-skills/index.json) with the [Apify CLI](https://docs.apify.com/cli/docs.md): `npm install -g apify-cli`, then `apify login`.
- **JavaScript/TypeScript projects** — the official [JS/TS client](https://docs.apify.com/api/client/js/docs.md): `npm install apify-client`.
- **Python projects** — the official [Python client](https://docs.apify.com/api/client/python/docs.md): `pip install apify-client`.
- **Any other language** — 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

## The Artifact Machine Schema Audit

A small artifact for checking whether a webpage has valid, useful structured data for search engines and AI systems.

Use this Actor before a site launch, product-page review, content refresh, SEO audit, or AI visibility campaign. It checks JSON-LD structured data, identifies schema types, and reports missing fields that can reduce machine understanding or rich-result eligibility.

Pulled from The Artifact Machine: generated, tested, and cleaned up by a human.

### What It Checks

- JSON-LD presence
- malformed JSON-LD blocks
- schema types such as `Organization`, `Article`, `Product`, `FAQPage`, `HowTo`, `BreadcrumbList`, `Recipe`, and `Event`
- missing high-value fields for common rich-result types
- duplicate schema types
- useful entity signals for search and AI systems

### Best For

- SEO and GEO audits
- ecommerce product-page checks
- content and article QA
- local business schema reviews
- agency client diagnostics

### Input

```json
{
  "url": "https://example.com",
  "includeRawJsonLd": false
}
```

#### Input Fields

- `url`: page URL to audit.
- `includeRawJsonLd`: optional debugging flag that includes truncated raw JSON-LD blocks in the output.

### Output

The Actor writes one dataset item and an `OUTPUT` key-value record with:

- composite score and grade
- structured-data type inventory
- JSON-LD parse status
- issue counts by severity
- actionable issues with impact and how-to-fix guidance

Example output fields:

- `score`: 0-100 schema health score
- `grade`: A-F score band
- `issueCounts`: issue totals by severity
- `checks.presence`: JSON-LD count and parse status
- `checks.types`: schema type inventory and duplicates
- `checks.richResultFields`: missing required-field summary
- `issues`: prioritized fix list

### Limits

- This Actor audits one submitted page.
- It focuses on JSON-LD, the most common structured-data format.
- It does not guarantee Google rich-result eligibility.
- It does not render JavaScript in the first version.
- Some sites block automated requests differently from regular browsers, so results should be reviewed before client delivery.

### Pricing

Use pay-per-result pricing based on completed reports rather than per issue. Suggested starting price: `$1.00` to `$2.00` per completed schema audit report after private runtime cost is measured.

# Actor input Schema

## `url` (type: `string`):

The page URL to audit for structured data.

## `includeRawJsonLd` (type: `boolean`):

Include truncated raw JSON-LD blocks in the output for debugging.

## Actor input object example

```json
{
  "url": "https://example.com",
  "includeRawJsonLd": false
}
```

# Actor output Schema

## `results` (type: `string`):

Default dataset items. Each item contains the target URL, score, grade, schema summaries, issue counts, and actionable issues.

## `summary` (type: `string`):

The same single-run summary stored as the OUTPUT key-value record.

# 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 = {
    "url": "https://example.com"
};

// Run the Actor and wait for it to finish
const run = await client.actor("artifact-machine/artifact-machine-schema-audit").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 = { "url": "https://example.com" }

# Run the Actor and wait for it to finish
run = client.actor("artifact-machine/artifact-machine-schema-audit").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 '{
  "url": "https://example.com"
}' |
apify call artifact-machine/artifact-machine-schema-audit --silent --output-dataset

```

## MCP server setup

```json
{
    "mcpServers": {
        "apify": {
            "command": "npx",
            "args": [
                "mcp-remote",
                "https://mcp.apify.com/?tools=artifact-machine/artifact-machine-schema-audit",
                "--header",
                "Authorization: Bearer <YOUR_API_TOKEN>"
            ]
        }
    }
}

```

## OpenAPI specification

Download the OpenAPI definition: https://api.apify.com/v2/acts/GEylHTjSZxBWXtV8K/builds/tzwtRthFGjRTqxLtK/openapi.json