Mercari Search Results Scraper

Overview

The Mercari Search Results Scraper is a production-grade Apify actor designed to extract product listings from Mercari.com search results using Mercari’s official GraphQL endpoints.

This actor is built for stability, scalability, and ease of use, providing clean, structured output suitable for analytics, monitoring, research, and downstream automation.

It supports keyword search, category filtering, price ranges, item conditions, sorting, pagination, and rate-limit handling.

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Key Features

• 🔍 Keyword-based product search
• 🗂 Category filtering
• 💰 Minimum and maximum price filters
• 🧾 Item condition filtering
• ↕️ Multiple sorting modes (best match, newest, price)
• 📦 Clean, simplified output schema
• 🔁 Automatic pagination
• 🛡 Built-in retry and rate-limit handling
• 🚀 Production-ready and Apify Store compliant

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How It Works

1. The actor initializes a valid Mercari session token.
2. It queries Mercari’s GraphQL search API using persisted queries.
3. Results are paginated automatically until the requested limit is reached.
4. Each item is simplified into a clean, user-friendly structure.
5. Data is stored in the default Apify dataset.

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Input Configuration

Required Fields

Field	Type	Description
keywords	string	Search keywords seperated by comma (e.g. nike, iphone , jordan)

Optional Fields

Field	Type	Description
categoryIds	string	Filter by category
itemConditions	array	Filter by item condition
minPrice	integer	Minimum price ($)
maxPrice	integer	Maximum price ($)
sortBy	integer	Sorting mode
maxItems	integer	Maximum number of items to scrape

Sorting Options (sortBy)

Value	Meaning
2	Best match
11	Newest
6	Lowest price
5	Highest price

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Output Data Structure

The actor outputs a simplified, stable schema designed for non-technical users and analysts.

Example Output

{
  "id": "m11151988527",
  "title": "Nike Air Max 270 Washed Coral White Running Shoes",
  "price": 4800,
  "currency": "$",
  "status": "on_sale",
  "brand": "Nike",
  "condition": "Good",
  "size": "9.5 (40)",
  "category": {
    "main": "Women",
    "sub": "Shoes",
    "leaf": "Athletic"
  },
  "categoryId": 283,
  "categoryTitle": "Athletic Shoes",
  "sellerId": 917213388,
  "countrySource": 1,
  "images": [
    {
      "url": "https://u-mercari-images.mercdn.net/photos/...",
      "thumbnail": "https://u-mercari-images.mercdn.net/photos/...&width=200"
    }
  ],
  "score": 83.58
}

Use Cases

1. 📊 Market research and price analysis

2. 🛒 Product discovery and comparison

3. 📈 Trend monitoring

4. 🤖 Feeding downstream automation pipelines

5. 🧠 Dataset creation and enrichment

Performance & Reliability

1. Automatic retries with exponential backoff

2. Graceful handling of rate limits (403 / 429)

3. Batched dataset writes for efficiency

4. Designed for long-running and large-scale jobs

Support & Maintenance

This actor is actively maintained and designed for production usage. For issues or feature requests, please use the Apify platform issue tracker.

Final Notes

This actor prioritizes:

1. Data clarity

2. Operational reliability

3. Professional usability

It is suitable for both individual users and enterprise-scale workflows.