# Shopify Agent-Readiness Score (`refreshing_travertine/shopify-readiness-score`) Actor

Scores a public Shopify storefront's product data against the attributes AI shopping agents actually check before recommending a product, and reports the specific gaps.

- **URL**: https://apify.com/refreshing\_travertine/shopify-readiness-score.md
- **Developed by:** [Andrew Harris](https://apify.com/refreshing_travertine) (community)
- **Categories:**
- **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, 0 bookmarks
- **User rating**: No ratings yet

## Pricing

from $80.00 / 1,000 store readiness scoreds

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?

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

## Shopify Agent-Readiness Score

Scores a public Shopify storefront's product catalog against the specific
attributes that determine whether AI shopping agents (ChatGPT Shopping,
Google's agentic shopping, etc.) surface or silently exclude a product, and
reports exactly which SKUs and fields are the problem.

### What it does

Give it a list of store domains. For each one it:

1. Checks `robots.txt` first — skips and reports, never bypasses.
2. Samples the public product feed.
3. Scores the sample against 8 checkable attributes (title quality,
   description depth, vendor field, category/product\_type, image presence,
   price presence, availability field presence, handle/URL presence).
4. Returns a 0–100 readiness score, the specific attributes that are
   failing, and per-attribute failure rates.

**Note on GTIN/barcode:** it's reported for information only and does not
count against the score. Private-label merchants legitimately have no
manufacturer barcode to report — Google Merchant Center's own
`identifier_exists:false` exemption exists specifically for this. An earlier
version of this project's own research briefly miscounted GTIN absence as a
defect and got a materially wrong result as a consequence (see this
project's `venture-register.html` for the full account); this actor is built
on the corrected version from the start.

### Status

**Not yet deployed to Apify Store.** The scoring logic is the exact code
that was run and manually verified against 377 real stores in two separate
passes during this project's research phase — it's not new/untested logic.
What's untested is this specific actor packaging on the live Apify platform.

### Pricing model

Pay-per-event: one `store-scored` charge per domain successfully scored.
Skipped domains aren't charged. Exact price per event gets set in the Apify
Console at publish time, not in code.

# Actor input Schema

## `domains` (type: `array`):

List of Shopify store domains to score, e.g. examplestore.com (no https://, no trailing slash). Each store is checked against robots.txt before being fetched.

## `sampleSize` (type: `integer`):

How many products to sample per store when computing the score. Higher is more representative, slower.

## Actor input object example

```json
{
  "domains": [
    "shop.example.com"
  ],
  "sampleSize": 20
}
```

# Actor output Schema

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

// Prepare Actor input
const input = {
    "domains": [
        "shop.example.com"
    ]
};

// Run the Actor and wait for it to finish
const run = await client.actor("refreshing_travertine/shopify-readiness-score").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 = { "domains": ["shop.example.com"] }

# Run the Actor and wait for it to finish
run = client.actor("refreshing_travertine/shopify-readiness-score").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 '{
  "domains": [
    "shop.example.com"
  ]
}' |
apify call refreshing_travertine/shopify-readiness-score --silent --output-dataset

```

## MCP server setup

```json
{
    "mcpServers": {
        "apify": {
            "type": "http",
            "url": "https://mcp.apify.com/?tools=fetch-actor-details,refreshing_travertine/shopify-readiness-score"
        }
    }
}

```

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/06AcZX9wU03Vm1djC/builds/NTid9lvweDYa473re/openapi.json