# JD.com Product, Seller & Price Intelligence (`quanmatrix/jd-product-seller-price-intelligence`) Actor

Use this Actor to analyze jd.com product, seller and price and return decision-ready structured signals. Monitor JD.com SKUs with product, seller, price, availability, snapshot deltas and decision-ready price intelligence.

- **URL**: https://apify.com/quanmatrix/jd-product-seller-price-intelligence.md
- **Developed by:** [Rafael Barreto Haddad](https://apify.com/quanmatrix) (community)
- **Categories:** E-commerce, Automation
- **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, 0 bookmarks
- **User rating**: No ratings yet

## Pricing

from $4.68 / 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.
Since this Actor supports Apify Store discounts, the price gets lower the higher subscription plan you have.

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

## Xianyu / Goofish Resale Market & Arbitrage Intelligence

Use this Actor to analyze jd.com product, seller and price and return decision-ready structured signals. It is designed for repeatable human, API, Apify AI, and MCP-driven workflows.

Analyze live public Xianyu/Goofish resale listings through Alibaba's signed H5/MTOP transport and turn raw marketplace rows into decision-ready resale intelligence.

### What it returns

Each row includes listing ID, title, live price in CNY, city, seller nickname, image, want count, free-shipping and bargainability flags, plus QuanMatrix decision fields: `demandScore`, optional benchmark `discountToReferencePct` and `grossSpreadCny`, snapshot `priceDelta` / `priceDeltaPct`, `arbitrageScore`, and `agentAction`.

The Actor also stores a reusable `SNAPSHOT` and a `MARKET_SUMMARY` with median price, buy-candidate count, average demand score and top opportunities.

### Why use this Actor

Most marketplace scrapers stop at extraction. This Actor is designed for repeated resale decisions. Supply a previous `SNAPSHOT` to measure exact price changes. Supply `referencePriceCny` to calculate benchmark spread and discount. The deterministic scoring layer turns those signals into `BUY_CANDIDATE`, `WATCH`, or `MONITOR` actions that downstream agents can consume without inventing their own business logic.

### Key features

It combines an anonymous signed H5/MTOP live feed with listing normalization, exact snapshot price deltas, reference-price spread analytics, a demand score based on observable marketplace signals, an arbitrage score, deterministic agent actions, reusable snapshots, and a compact market summary for recurring monitoring workflows.

### Input

- `maxResults`: 1-300 live listings.
- `keywordContains`: optional local title filter.
- `minPriceCny` / `maxPriceCny`: optional price band.
- `referencePriceCny`: optional retail, resale or sourcing benchmark.
- `sortBy`: `arbitrageScore`, `demandScore`, `price`, or `wantCount`.
- `previousSnapshot`: prior rows from `SNAPSHOT` for exact price deltas.

#### Example

```json
{
  "maxResults": 50,
  "referencePriceCny": 1200,
  "sortBy": "arbitrageScore",
  "previousSnapshot": []
}
```

### Use cases

Use it for resale-arbitrage scouting, second-hand market pricing, cross-market sourcing comparisons, price-drop monitoring, demand-signal research, and agentic ecommerce workflows. Twenty ready-made Task blueprints cover distinct commercial intents such as phones, laptops, gaming hardware, collectibles, luxury, wholesale-to-resale spread and recurring market pulse.

### Data transport

The Actor does not use brittle page HTML as its primary transport. It establishes an anonymous H5/MTOP session, obtains the temporary MTOP token, signs requests with the platform's normal H5 request scheme, and reads the structured home-feed response. No account credential is required for the supported v1 workflow.

### Limitations

v1 intentionally claims only the anonymous live home feed plus local title filtering. Authenticated keyword-search coverage is not advertised until it is independently hardened. Public platform contracts can change or throttle. Use the data in accordance with applicable laws and platform terms, and avoid decisions that unlawfully discriminate or misuse seller information.

### Pricing

Pay-per-event is tied to the useful customer outcome: one enriched resale-intelligence row. Tiered FREE / BRONZE / SILVER / GOLD pricing is managed by the factory pricing governor; higher account tiers inherit GOLD until a distinct enterprise case is justified.

### Output for automation

Dataset rows are JSON-first and stable for API, MCP and agent workflows. `SNAPSHOT` can be fed into a future run, while `MARKET_SUMMARY` gives a compact market-level result suitable for alerts and scheduled monitoring.

# Changelog

This Actor's version history is a separate document: https://apify.com/quanmatrix/jd-product-seller-price-intelligence/changelog.md

# Actor input Schema

## `skuIds` (type: `array`):

Numeric JD.com SKU IDs.

## `productUrls` (type: `array`):

Optional item.jd.com product URLs.

## `targetPriceCny` (type: `number`):

Optional buy threshold used for agentAction.

## `previousSnapshot` (type: `array`):

Optional prior rows for exact price deltas.

## `previousAnalysis` (type: `object`):

Optional prior Gen2 output used to calculate decision-metric deltas and regression.

## `valuePerImpactUnitUsd` (type: `number`):

Optional user-supplied economic value per impact unit. Leave empty to avoid monetary estimation.

## `monthlyRuns` (type: `integer`):

Optional expected monthly run count used only with valuePerImpactUnitUsd for economic impact estimation.

## `mcpConnectors` (type: `array`):

Optional MCP connectors authorized in your Apify account. Use them to send or write this Actor result to tools such as Slack, Notion, GitHub, Sentry, Supabase, or another compatible MCP service.

## `mcpToolName` (type: `string`):

Optional exact MCP tool name. Leave blank to let the selected MCP action preset discover a compatible tool automatically.

## `mcpToolArguments` (type: `object`):

JSON object passed to the selected MCP tool. String values may use {{actor\_title}}, {{result\_summary}}, or {{result\_json}} placeholders.

## `mcpFailOnError` (type: `boolean`):

When enabled, an MCP delivery error fails the Actor run. Disabled by default so data extraction and intelligence results remain available even if the external destination is unavailable.

## `mcpActionPreset` (type: `string`):

Choose a safe action pattern. AUTO\_SAFE\_WRITE discovers a compatible non-destructive write tool automatically; use a specific preset for Slack, GitHub, Notion, or database delivery.

## Actor input object example

```json
{
  "skuIds": [
    "100012043978"
  ],
  "monthlyRuns": 1,
  "mcpToolName": "",
  "mcpToolArguments": {},
  "mcpFailOnError": false,
  "mcpActionPreset": "AUTO_SAFE_WRITE"
}
```

# Actor output Schema

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

Structured JD.com SKU rows with seller, brand, price visibility, stock state, price deltas, target gaps, and agent-ready actions.

# 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 = {};

// Run the Actor and wait for it to finish
const run = await client.actor("quanmatrix/jd-product-seller-price-intelligence").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 = {}

# Run the Actor and wait for it to finish
run = client.actor("quanmatrix/jd-product-seller-price-intelligence").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 '{}' |
apify call quanmatrix/jd-product-seller-price-intelligence --silent --output-dataset

```

## MCP server setup

```json
{
    "mcpServers": {
        "apify": {
            "type": "http",
            "url": "https://mcp.apify.com/?tools=fetch-actor-details,quanmatrix/jd-product-seller-price-intelligence"
        }
    }
}
```

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/CytGiC2zP53LN7dz5/builds/C2dS634QkIhdAOwN7/openapi.json