# Amazon Leads Engine SANDBOX (`xmiso_scrapers/amazon-leads-engine`) Actor

Database of scraped sellers from Amazon marketplaces with emails, phone numbers and other valuable data. SANDBOX testing version

- **URL**: https://apify.com/xmiso_scrapers/amazon-leads-engine.md
- **Developed by:** [Miso](https://apify.com/xmiso_scrapers) (community)
- **Stats:** 3 total users, 3 monthly users, 100.0% runs succeeded, 0 bookmarks
- **User rating**: No ratings yet

## Pricing

$0.00001 / actor start

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

## Test Amazon Actor

### 🧩 Input Parameters

| Name             | Type     | Description                                 | Example        |
|------------------|----------|---------------------------------------------|----------------|
| `amazon_category`| string   | Amazon category to search                   | `Fashion`      |
| `seller_country` | string   | Seller company country (2-letter code)      | `DE`, `FR`     |
| `max_results`    | integer  | Maximum sellers to return (max 100 for free users) | `100`    |
| `offset`    | integer  | How many rows to skip (useful to avoid duplicates when running repeatedly) | `100`    |

### 📝 Output Fields

Each row in the dataset includes:

- **Seller ID**
- **Seller Page URL**
- **Business Name**
- **Business Type**
- **Registration Number**
- **VAT Number**
- **Phone Number**
- **Email**
- **Business Address**
- **Country Code**
- **Seller Info/About**
- **ASIN**
- **Product Name**
- **Category**
- **Lifetime Rating / Reviews**
- **1-Year Rating / Reviews**
- **3-Months Rating / Reviews**

*(See Apify dataset for full column details.)*

### ⚡ Example Use Cases

- Building Amazon seller lead lists (by category/country)
- Analyzing seller competition in Europe
- Populating CRM or sales systems
- Enriching supplier/contact research

### 🟢 Quick Start

1. Choose category, country, and set max results.
2. Run the Actor on Apify.
3. Download your results as CSV, JSON, or view in the Apify UI.

> **Note:** Free users are limited to 100 results per run.

***

*For more technical details, check the Actor’s input schema and output dataset fields on Apify.*

# Actor input Schema

## `amazon_category` (type: `string`):

Select the Amazon product category. Categories are standardized across different Amazon marketplaces.

## `seller_country` (type: `string`):

Select sellers country of residence. Useful when you want to filter out sellers from countries you are not interested in.

## `date_added` (type: `string`):

Select only leads discovered after this date (YYYY-MM-DD)

## `max_results` (type: `integer`):

Maximum results you want to get

## `offset` (type: `integer`):

How many rows to skip. Useful when you already downloaded x rows and don't want to get duplicates

## Actor input object example

```json
{
  "amazon_category": "Fashion",
  "max_results": 100,
  "offset": 1
}
```

# Actor output Schema

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

Dataset with scraped Amazon sellers.

# 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 = {
    "amazon_category": "Fashion",
    "max_results": 100,
    "offset": 1
};

// Run the Actor and wait for it to finish
const run = await client.actor("xmiso_scrapers/amazon-leads-engine").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 = {
    "amazon_category": "Fashion",
    "max_results": 100,
    "offset": 1,
}

# Run the Actor and wait for it to finish
run = client.actor("xmiso_scrapers/amazon-leads-engine").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 '{
  "amazon_category": "Fashion",
  "max_results": 100,
  "offset": 1
}' |
apify call xmiso_scrapers/amazon-leads-engine --silent --output-dataset

```

## MCP server setup

```json
{
    "mcpServers": {
        "apify": {
            "type": "http",
            "url": "https://mcp.apify.com/?tools=fetch-actor-details,xmiso_scrapers/amazon-leads-engine"
        }
    }
}
```

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/DnW956av3bcdxZy3m/builds/saRdIc6kVYYNlHfaO/openapi.json