# Depop Real Time Data Scraper (`b2b_leads/depop-real-time-data`) Actor

Scrape live Depop data: search results, full listings, seller shops, profiles, trending items, or any URL. Get rich fields including prices, brands, sizes, sold history, shipping details, and seller signals.

- **URL**: https://apify.com/b2b\_leads/depop-real-time-data.md
- **Developed by:** [Chidubem Aneke](https://apify.com/b2b_leads) (community)
- **Categories:** E-commerce, Automation, SEO tools
- **Stats:** 6 total users, 2 monthly users, 97.5% runs succeeded, 0 bookmarks
- **User rating**: 5.00 out of 5 stars

## Pricing

from $0.90 / 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/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

## Depop Real-Time Data

**Live fashion resale intelligence from Depop — search, product details, seller shops, sold history, profiles, and trending — as clean structured JSON.**

Built for resellers, market researchers, agencies, data teams, and AI workflows that need **fresh Depop listing data** without slow, heavy browser runs. Enable only the features you need, click **Start**, and stream results to your dataset, webhook, or LLM pipeline.

***

### Why teams use this Actor

| | Depop Real-Time Data | Typical browser scraper |
|---|----------------------|-------------------------|
| **Speed** | Fast per-listing collection | Often seconds per page |
| **Memory** | **512 MB** default | 2–4 GB+ |
| **Setup** | Checkbox UI, prefilled test input | Fragile & high-maintenance |
| **Cost** | Low compute | High |
| **Output** | Structured JSON, LLM-ready | Often messy HTML |
| **Scale** | Long runs stream row-by-row | Memory-heavy buffers |
| **Delivery** | Dataset + optional webhook | Export-only |

***

### What you get

Every record includes `featureType`, `country`, `url`, and `scrapedAt` so you can filter, join, and pipe into any workflow.

#### Listing Search (`featureType: "listing_search"`) — **one product = one row**

| Field | Description |
|-------|-------------|
| `listingId`, `title`, `url` | Identity and product link |
| `price`, `originalPrice`, `currency`, `isOnSale` | Pricing |
| `brand`, `size`, `condition`, `color` | Attributes |
| `tags[]`, `imageUrl`, `imageUrls[]` | Tags and photos |
| `sellerUsername`, `likes`, `postedAt` | Seller and engagement |
| `shippingCost`, `position` | Shipping and rank |
| `detailsFetched` | `false` = search card only; `true` = full product fields merged into **this same row** |

**With “Fetch full listing details for search results” on**, the Actor does **not** create a second `listing_details` row. It enriches the search row with:

| Extra fields when `detailsFetched: true` | Description |
|------------------------------------------|-------------|
| `fullDescription` | Full listing text |
| `shipping` | Structured shipping (cost, methods, origin, …) |
| `measurements` | Parsed measurements when present in the description |
| `comments`, `quantity`, `isAvailable` | Engagement and stock |
| `subCategory`, `pattern`, `lastUpdated` | Extra catalog fields when available |

Example: `searchMaxResults: 20` + full details on → **20 dataset rows**, not 40.

#### Listing Details (`featureType: "listing_details"`)

Separate full-product rows **only** when you enable the **Listing Details** feature and pass specific listing IDs/slugs/URLs. Not used for search enrichment (that merges into `listing_search`).

#### Seller Listings (`featureType: "seller_listings"`)

Shop inventory rows: price, brand, size, condition, images, sold flag when available.

#### Seller Profile (`featureType: "seller_profile"`)

Username, display name, bio, verification, website/social links, and optional contact signals from the bio (email, phone, Instagram, WhatsApp cues).

#### Sold History (`featureType: "sold_history"`)

Sold / completed listings for comps and sell-through research — price, brand, size, status, and timestamps when available.

#### Trending (`featureType: "trending"`)

Discovery feed for niches like streetwear, vintage, footwear, y2k.

#### Scrape By URL (`featureType: "scrape_by_url"`)

Paste any Depop product, search, shop, brand, or category URL — auto-detects type and returns structured rows.

***

### Features (checkbox UX)

| Feature | Default | What it does |
|---------|---------|--------------|
| **Listing Search** | **ON** | Keyword search with brand, size, condition, price filters |
| **Listing Details** | off | Full product data for IDs / slugs / URLs |
| **Seller Listings** | off | Shop inventory for usernames |
| **Seller Profile** | off | Bio, verification, contact signals |
| **Sold Item History** | off | Sold listings for pricing comps |
| **Scrape By URL** | off | Any Depop URL |
| **Trending / Category** | off | Niche discovery feed |

**Full details from search:** turn on **Fetch full listing details for search results**. Each product stays **one row** (`listing_search`) with full fields merged in (description, gallery, shipping, etc.). Volume is controlled by Max results per keyword.

Other options: extract seller contacts, include shipping fields, optional raw payload for advanced pipelines.

***

### Use cases

- **Reseller pricing & comps** — track similar listings and sold prices by brand/size
- **Brand & inventory monitoring** — watch keywords (Nike Dunk, Levi’s 501, Carhartt)
- **Seller intelligence** — shop size, sold history, bio contact signals
- **Trend spotting** — streetwear, vintage, y2k category feeds
- **Market research** — price distributions across US / UK / AU
- **Agency reporting** — client-ready resale datasets
- **AI & LLM pipelines** — JSON for RAG, scoring, alerts, and summaries
- **Automation** — schedules, webhooks, Zapier/Make/n8n, warehouses

***

### LLM & MCP integration

Output is **structured JSON** — ready for ChatGPT, Claude, Gemini, LangChain, LlamaIndex, and custom agents.

#### Recommended workflow

1. Run the Actor with the features you need (search is prefilled).
2. Fetch dataset items via the [Apify API](https://docs.apify.com/api/v2) or export JSON/CSV.
3. Pass records to your LLM, or index them into a vector store.

#### Example: search row with full details merged

```json
{
  "featureType": "listing_search",
  "country": "US",
  "title": "Levi's 501 Size 30 Straight-cut Vintage Jeans",
  "price": 15,
  "currency": "USD",
  "brand": "Levi's",
  "size": "30",
  "sellerUsername": "example_seller",
  "likes": 3,
  "position": 1,
  "detailsFetched": true,
  "fullDescription": "Levi's 501 Size 30 … #retro #levis",
  "shipping": { "cost": 3.99, "methods": ["USPS"], "shipsFrom": "US" },
  "isAvailable": true,
  "url": "https://www.depop.com/products/example-slug/",
  "scrapedAt": "2026-07-16T12:00:00.000Z"
}
```

#### Apify MCP (Model Context Protocol)

Use the [Apify MCP server](https://docs.apify.com/platform/integrations/mcp) so AI assistants can:

- **Run** this Actor from natural language
- **Read** dataset results in chat
- **Chain** with other Actors (enrich → score → CRM)

```
User: "Find 25 newest vintage Levi's under $40 on Depop US and summarize pricing"
→ MCP runs Actor with searchKeywords=["vintage levis"], searchMaxPrice=40, searchSort=newest
→ MCP reads dataset items
→ LLM summarizes price bands and opportunities
```

#### API quick start

```bash
curl -X POST "https://api.apify.com/v2/acts/YOUR_ACTOR_ID/runs?token=YOUR_TOKEN" \
  -H "Content-Type: application/json" \
  -d '{
    "country": "US",
    "enableListingSearch": true,
    "searchKeywords": ["vintage jeans", "nike dunk"],
    "searchMaxResults": 20,
    "searchSort": "newest"
  }'
```

Dataset items: `GET https://api.apify.com/v2/datasets/{datasetId}/items?format=json`

***

### Input reference

Enable only what you need. **Listing Search is on by default** with sample keywords so you can run immediately.

| Input | Type | Default | Description |
|-------|------|---------|-------------|
| **Region** | | | |
| `country` | enum | `US` | US, UK, or AU |
| **Listing Search** | | | |
| `enableListingSearch` | boolean | `true` | Keyword search |
| `searchKeywords` | string\[] | `["vintage jeans","nike dunk"]` | Search terms |
| `searchMaxResults` | integer | `20` | Max per keyword (1–200) |
| `searchSort` | enum | `newest` | relevance, newest, price-low, price-high |
| `searchBrand` | string | — | Optional brand filter |
| `searchSize` | string | — | Optional size filter |
| `searchCondition` | enum | any | brand\_new, used\_like\_new, used\_good, used\_fair |
| `searchMinPrice` / `searchMaxPrice` | integer | — | Price bounds |
| `searchOnSale` | boolean | `false` | Prefer discounted items |
| `searchFetchFullDetails` | boolean | `false` | Merge full product details into each search row (still 1 row per product) |
| **Listing Details** | | | |
| `enableListingDetails` | boolean | `false` | Full product rows |
| `listingSlugs` | string\[] | — | Slugs or IDs |
| `listingUrls` | string\[] | — | Product URLs |
| **Sellers** | | | |
| `enableSellerListings` | boolean | `false` | Shop inventory |
| `enableSellerProfile` | boolean | `false` | Profile + contacts |
| `enableSoldHistory` | boolean | `false` | Sold comps |
| `sellerUsernames` | string\[] | — | Without @ |
| `sellerMaxListings` | integer | `30` | Cap per shop |
| `soldMaxItems` | integer | `30` | Cap sold per seller |
| **URL & Trending** | | | |
| `enableScrapeByUrl` | boolean | `false` | Any Depop URL |
| `scrapeUrls` | string\[] | — | URLs to process |
| `scrapeMaxPages` | integer | `3` | Depth for list pages |
| `enableTrending` | boolean | `false` | Category feed |
| `trendingCategory` | string | `streetwear` | Niche keyword |
| `trendingMaxItems` | integer | `30` | Cap |
| **Options & delivery** | | | |
| `extractSellerContacts` | boolean | `true` | Bio contact signals |
| `includeRaw` | boolean | `false` | Extended payload (larger) |
| `maxItems` | integer | — | Global row cap |
| `webhookUrl` | string | — | Optional real-time POST per row |
| `webhookFormat` | enum | `json` | `json` or `slack` |
| `proxyConfiguration` | object | Residential | Recommended |

***

### Output & streaming

#### What to expect (row counts)

| What you enable | Dataset rows |
|-----------------|--------------|
| Listing Search, 20 results, full details **off** | **20** × `listing_search` (`detailsFetched: false`) |
| Listing Search, 20 results, full details **on** | **20** × `listing_search` (`detailsFetched: true`, richer fields on the same row) |
| Listing Details feature with 5 URLs | **5** × `listing_details` |
| Seller Listings, max 30 | Up to **30** × `seller_listings` per username |

- **Every row is written to the Apify Dataset as soon as it is ready** — long runs do not hold the full result set in memory.
- **Parallel workers** process multiple keywords, sellers, product details, and URLs concurrently for faster runs.
- **Spending limits** — if you set a max cost per run (Apify Console / PPE), the Actor **stops gracefully** when that limit is reached so you never overspend.
- Filter the dataset by `featureType`.
- Dataset views: **overview**, **search**, **details**, **seller\_listings**, **seller\_profile**, **sold\_history**, **trending**, **scrape\_by\_url**.
- Optional **webhook**: each record is still stored in the dataset **and** POSTed to your URL (CRM, Slack, Zapier, Make, custom API).

#### Webhook example (JSON body)

```json
{
  "featureType": "listing_search",
  "title": "Nike Dunk Low Panda",
  "price": 85,
  "currency": "USD",
  "detailsFetched": true,
  "url": "https://www.depop.com/products/..."
}
```

Slack format sends a short formatted message instead of the full object.

***

### How to run

1. Open the Actor and keep the **prefilled** Listing Search keywords (or edit them).
2. Pick **Region** (US / UK / AU).
3. Use **Residential proxy** matching that region.
4. Click **Start**.
5. Export JSON/CSV, connect a webhook, or pull via API.

**Pro tip:** Schedule daily runs for price monitoring or trend tracking.

***

### Integrations

Full Apify support: **API, schedules, webhooks, Google Sheets, Make, Zapier, n8n, CSV/JSON export**.

Ideal next steps:

- Pipe dataset → Google Sheets for client reports
- Webhook → Slack channel for new “under $X” listings
- MCP / LLM → daily resale brief

***

### FAQ

**Do I need code?**\
No — use the console UI. Developers can use the Apify API.

**Which proxy?**\
Residential, matching your target region (US / UK / AU).

**Multiple features in one run?**\
Yes — results are tagged with `featureType`.

**Large runs?**\
Rows stream continuously, work runs in parallel, and your per-run spending limit is respected.

**LLM / agents?**\
Yes — structured JSON + Apify MCP for agent-driven runs.

***

### Contact / custom projects

Need something tailored? I build **custom scrapers, data pipelines, and web apps** of any kind — marketplaces, lead gen, internal tools, dashboards, and full-stack products.

- **Email:** <dubem115@gmail.com>
- **GitHub:** <https://github.com/DrunkCodes>

Open to project work, integrations, and ongoing data infrastructure.

# Actor input Schema

## `country` (type: `string`):

Marketplace region. Affects language, currency preference, and recommended proxy country.

## `enableListingSearch` (type: `boolean`):

Search Depop by keyword and optional filters. On by default — prefilled so you can click Start immediately.

## `searchKeywords` (type: `array`):

One or more search terms (e.g. vintage jeans, nike dunk, carhartt). Required when Listing Search is on.

## `searchMaxResults` (type: `integer`):

Maximum listings to return for each keyword (1–200).

## `searchSort` (type: `string`):

How to order search results.

## `searchBrand` (type: `string`):

Limit results to a brand name (e.g. Nike, Levi's, Carhartt).

## `searchSize` (type: `string`):

e.g. M, L, 8, 10, 32".

## `searchCondition` (type: `string`):

Filter by item condition.

## `searchMinPrice` (type: `integer`):

Minimum price in the selected region currency.

## `searchMaxPrice` (type: `integer`):

Maximum price in the selected region currency.

## `searchOnSale` (type: `boolean`):

Prefer discounted / reduced-price listings when available.

## `searchCategory` (type: `string`):

Optional category words to refine the search (e.g. jeans, tops, shoes).

## `searchFetchFullDetails` (type: `boolean`):

When on, each search result is one row with full product fields merged in (description, gallery, shipping, condition, likes, seller). Still featureType listing\_search — not a second row. Uses Max results per keyword as the volume limit. Extra requests — richer output.

## `enableListingDetails` (type: `boolean`):

Fetch full product data for specific listing IDs, slugs, or URLs.

## `listingSlugs` (type: `array`):

Product slugs or numeric IDs. Example: username-item-title-abcd

## `listingUrls` (type: `array`):

Full Depop product URLs (https://www.depop.com/products/…).

## `enableSellerListings` (type: `boolean`):

Collect shop listings for one or more sellers.

## `sellerUsernames` (type: `array`):

Depop usernames without @. Shared by Seller Listings, Seller Profile, and Sold History.

## `sellerMaxListings` (type: `integer`):

Maximum active listings to collect per seller (1–500).

## `enableSellerProfile` (type: `boolean`):

Fetch seller bio, verification, website, and contact signals from the bio.

## `enableSoldHistory` (type: `boolean`):

Collect sold / completed listings for comps and sell-through research.

## `soldMaxItems` (type: `integer`):

Maximum sold listings to collect per seller (1–200).

## `enableScrapeByUrl` (type: `boolean`):

Paste any Depop URL — product, search, shop, brand, or category.

## `scrapeUrls` (type: `array`):

Any Depop search, product, shop, brand, or category URL.

## `scrapeMaxPages` (type: `integer`):

Pagination depth for search/shop URLs (1–20).

## `enableTrending` (type: `boolean`):

Pull a discovery feed for a niche or category keyword.

## `trendingCategory` (type: `string`):

e.g. streetwear, vintage, footwear, womens, mens, y2k, jeans

## `trendingMaxItems` (type: `integer`):

Maximum trending listings to collect (1–200).

## `extractSellerContacts` (type: `boolean`):

Parse emails, phones, Instagram, and WhatsApp cues from seller bios.

## `includeSoldHistory` (type: `boolean`):

When available, mark sold status on listing rows (in addition to the Sold History feature).

## `includeShippingDetails` (type: `boolean`):

Include shipping cost, method, and origin when present.

## `includeRaw` (type: `boolean`):

Attach extended raw payload on each record. Larger output — for advanced pipelines only.

## `maxItems` (type: `integer`):

Hard cap across all features for this run. Leave empty for no global cap.

## `webhookUrl` (type: `string`):

Optional. Every record is always saved to the run dataset — this webhook is an ADDITIONAL real-time push. Each new row is also POSTed to this URL (CRM, Slack, Zapier, Make, n8n, custom backend).

## `webhookFormat` (type: `string`):

json = full record object; slack = Slack-friendly message payload.

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

Residential proxy recommended. Match country to the Region above (US / GB / AU).

## Actor input object example

```json
{
  "country": "US",
  "enableListingSearch": true,
  "searchKeywords": [
    "vintage jeans",
    "nike dunk"
  ],
  "searchMaxResults": 20,
  "searchSort": "newest",
  "searchBrand": "",
  "searchSize": "",
  "searchCondition": "",
  "searchOnSale": false,
  "searchCategory": "",
  "searchFetchFullDetails": false,
  "enableListingDetails": false,
  "listingSlugs": [],
  "listingUrls": [],
  "enableSellerListings": false,
  "sellerUsernames": [
    "depop"
  ],
  "sellerMaxListings": 30,
  "enableSellerProfile": false,
  "enableSoldHistory": false,
  "soldMaxItems": 30,
  "enableScrapeByUrl": false,
  "scrapeUrls": [
    "https://www.depop.com/search/?q=vintage+levis"
  ],
  "scrapeMaxPages": 3,
  "enableTrending": false,
  "trendingCategory": "streetwear",
  "trendingMaxItems": 30,
  "extractSellerContacts": true,
  "includeSoldHistory": true,
  "includeShippingDetails": true,
  "includeRaw": false,
  "webhookUrl": "",
  "webhookFormat": "json",
  "proxyConfiguration": {
    "useApifyProxy": true,
    "apifyProxyGroups": [
      "RESIDENTIAL"
    ],
    "apifyProxyCountry": "US"
  }
}
```

# Actor output Schema

## `allResults` (type: `string`):

Full dataset for this run (every featureType).

## `overview` (type: `string`):

Core fields across features, including detailsFetched for search rows.

## `search` (type: `string`):

featureType=listing\_search only. One product = one row; full details are merged when that option is enabled.

## `details` (type: `string`):

featureType=listing\_details — only from the Listing Details feature (specific IDs/URLs), not from search merge.

## `seller_listings` (type: `string`):

No description

## `seller_profile` (type: `string`):

No description

## `sold_history` (type: `string`):

No description

## `trending` (type: `string`):

No description

## `scrape_by_url` (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 = {
    "country": "US",
    "enableListingSearch": true,
    "searchKeywords": [
        "vintage jeans",
        "nike dunk"
    ],
    "searchMaxResults": 20,
    "searchSort": "newest",
    "searchOnSale": false,
    "searchFetchFullDetails": false,
    "enableListingDetails": false,
    "listingSlugs": [],
    "listingUrls": [],
    "enableSellerListings": false,
    "sellerUsernames": [
        "depop"
    ],
    "sellerMaxListings": 30,
    "enableSellerProfile": false,
    "enableSoldHistory": false,
    "soldMaxItems": 30,
    "enableScrapeByUrl": false,
    "scrapeUrls": [
        "https://www.depop.com/search/?q=vintage+levis"
    ],
    "scrapeMaxPages": 3,
    "enableTrending": false,
    "trendingCategory": "streetwear",
    "trendingMaxItems": 30,
    "extractSellerContacts": true,
    "includeSoldHistory": true,
    "includeShippingDetails": true,
    "includeRaw": false,
    "webhookFormat": "json",
    "proxyConfiguration": {
        "useApifyProxy": true,
        "apifyProxyGroups": [
            "RESIDENTIAL"
        ],
        "apifyProxyCountry": "US"
    }
};

// Run the Actor and wait for it to finish
const run = await client.actor("b2b_leads/depop-real-time-data").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 = {
    "country": "US",
    "enableListingSearch": True,
    "searchKeywords": [
        "vintage jeans",
        "nike dunk",
    ],
    "searchMaxResults": 20,
    "searchSort": "newest",
    "searchOnSale": False,
    "searchFetchFullDetails": False,
    "enableListingDetails": False,
    "listingSlugs": [],
    "listingUrls": [],
    "enableSellerListings": False,
    "sellerUsernames": ["depop"],
    "sellerMaxListings": 30,
    "enableSellerProfile": False,
    "enableSoldHistory": False,
    "soldMaxItems": 30,
    "enableScrapeByUrl": False,
    "scrapeUrls": ["https://www.depop.com/search/?q=vintage+levis"],
    "scrapeMaxPages": 3,
    "enableTrending": False,
    "trendingCategory": "streetwear",
    "trendingMaxItems": 30,
    "extractSellerContacts": True,
    "includeSoldHistory": True,
    "includeShippingDetails": True,
    "includeRaw": False,
    "webhookFormat": "json",
    "proxyConfiguration": {
        "useApifyProxy": True,
        "apifyProxyGroups": ["RESIDENTIAL"],
        "apifyProxyCountry": "US",
    },
}

# Run the Actor and wait for it to finish
run = client.actor("b2b_leads/depop-real-time-data").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 '{
  "country": "US",
  "enableListingSearch": true,
  "searchKeywords": [
    "vintage jeans",
    "nike dunk"
  ],
  "searchMaxResults": 20,
  "searchSort": "newest",
  "searchOnSale": false,
  "searchFetchFullDetails": false,
  "enableListingDetails": false,
  "listingSlugs": [],
  "listingUrls": [],
  "enableSellerListings": false,
  "sellerUsernames": [
    "depop"
  ],
  "sellerMaxListings": 30,
  "enableSellerProfile": false,
  "enableSoldHistory": false,
  "soldMaxItems": 30,
  "enableScrapeByUrl": false,
  "scrapeUrls": [
    "https://www.depop.com/search/?q=vintage+levis"
  ],
  "scrapeMaxPages": 3,
  "enableTrending": false,
  "trendingCategory": "streetwear",
  "trendingMaxItems": 30,
  "extractSellerContacts": true,
  "includeSoldHistory": true,
  "includeShippingDetails": true,
  "includeRaw": false,
  "webhookFormat": "json",
  "proxyConfiguration": {
    "useApifyProxy": true,
    "apifyProxyGroups": [
      "RESIDENTIAL"
    ],
    "apifyProxyCountry": "US"
  }
}' |
apify call b2b_leads/depop-real-time-data --silent --output-dataset

```

## MCP server setup

```json
{
    "mcpServers": {
        "apify": {
            "command": "npx",
            "args": [
                "mcp-remote",
                "https://mcp.apify.com/?tools=b2b_leads/depop-real-time-data",
                "--header",
                "Authorization: Bearer <YOUR_API_TOKEN>"
            ]
        }
    }
}

```

## OpenAPI specification

Download the OpenAPI definition: https://api.apify.com/v2/actors/sLPUXLKyNQ0bMatZp/builds/YByvFxyb00swLtYKg/openapi.json