# DoorDash Store & Menu Scraper (`xtracto/doordash-store-scraper`) Actor

Extract a DoorDash restaurant's full menu with prices, plus its street address, city and cuisine category. Reads the page's structured data, so every section and item comes back with its price. HTTP-only, no account, no browser.

- **URL**: https://apify.com/xtracto/doordash-store-scraper.md
- **Developed by:** [Farhan Febrian Nauval](https://apify.com/xtracto) (community)
- **Categories:** E-commerce
- **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, 0 bookmarks
- **User rating**: No ratings yet

## Pricing

from $1.33 / 1,000 results

This Actor is paid per event and usage. You are charged both the fixed price for specific events and for Apify platform usage.
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

## DoorDash Store & Menu Scraper

Extract a DoorDash restaurant's **full menu with prices**, plus its street address, city and
cuisine category. HTTP-only, no account, no browser.

### Input

| Field | Type | Default | Description |
|---|---|---|---|
| `stores` | array | *required* | Store URLs or bare numeric store ids |
| `maxProfiles` | integer | `6` | TLS profiles to try before giving up on a store |
| `maxConcurrency` | integer | `3` | Stores in parallel |
| `proxyConfiguration` | object | RESIDENTIAL | **Residential strongly recommended** |

**The slug in a store URL is decoration.** `/store/panda-express-san-francisco-303380/` and
`/store/rosalind-coffee-company-garland-303380/` both resolve to store `303380` — only the
trailing number is identity. The actor extracts it and rebuilds a canonical URL.

### Output

```jsonc
{
  "_input": "…/store/rosalind-coffee-company-garland-303380/",
  "_source": "S1-jsonld",
  "_scrapedAt": "2026-09-07T02:03:40Z",
  "_tlsProfile": "chrome124",          // which profile got through

  "storeId": "303380",
  "name": "Rosalind Coffee Company",
  "address": "107 N 6th St, Garland, TX 75040, USA",
  "city": "Garland",
  "cuisine": "Coffee",

  "menuSectionCount": 4,
  "menuItemCount": 22,
  "menuSections": [
    { "name": "Not Coffee", "itemCount": 8, "items": [
      { "name": "Matcha", "description": null,
        "price": "$4.75", "priceAmount": 4.75, "priceCurrency": "USD" }
    ]}
  ],
  "faqs": [ { "question": "…", "answer": "…" } ],
  "breadcrumbs": [ { "position": 1, "name": "Home", "item": "/" } ]
}
```

Every item in testing came back priced — 57 of 57 across three stores.

### How it works

All of it comes from the page's JSON-LD, which carries more than the rendered page conveniently
shows: a `Menu` block with every section and item, a `FAQPage` whose answers contain the street
address, and a `BreadcrumbList` giving the city and cuisine DoorDash files the store under.

#### Cloudflare: the TLS ladder is the normal path, not a fallback

**No single TLS profile is reliable here.** Measured over six independent rounds from Apify
residential, the winning profile changed almost every time — `safari180` twice, `chrome124`
twice, `chrome131` once, and one round where none of six landed. A fixed profile fails often
enough to look like an outage.

So the actor rotates a six-profile ladder, each rung on its own session and its own exit IP
(Cloudflare decides per connection, so reusing a session would make the attempts correlated).
That took the hit rate to **5 of 6 rounds**, and 2 of 2 on a re-run.

A block is detected from a **positive data marker** — the presence of the JSON-LD `Menu` block —
not from the status code, because Cloudflare serves some interstitials as a 200 with a large
body.

#### Prices come back in two different spellings

The same store returned `"$4.75"` on one fetch and `"USD 4.75"` (ISO code, non-breaking
space) on the next, minutes apart. The raw string is passed through unchanged, and
`priceAmount` (float) and `priceCurrency` are added alongside it so you never have to handle
both.

Parsing respects separator conventions: `1,234.56` and `1.234,56` are both 1234.56, and `3,50`
is 3.50 — whichever of `.` or `,` comes last is the decimal separator. Getting that backwards
inflates a price 100-fold without any error, which is why it is unit-tested rather than
eyeballed.

### Known limits

| Limit | Detail |
|---|---|
| No ratings or reviews | Not present in the structured data on this surface |
| No opening hours | Not in the JSON-LD; the FAQ answers sometimes mention delivery/pickup availability |
| Deleted vs blocked | DoorDash does not 404 a missing store — it serves a challenge, so a bad id reports `blocked` rather than `not_found` |
| Cost per store | A store can take several requests when the first profiles are challenged |

# Actor input Schema

## `stores` (type: `array`):

DoorDash store URLs (https://www.doordash.com/store/some-name-303380/) or bare numeric store ids (303380). Only the trailing number identifies a store - the slug is decoration.

## `maxProfiles` (type: `integer`):

DoorDash is behind Cloudflare and no single TLS profile is reliable - the actor rotates through a ladder until a store page comes back. Lower this for cheaper runs, raise it for stubborn stores.

## `maxConcurrency` (type: `integer`):

Stores fetched in parallel. Keep this low - each store may cost several requests.

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

Residential is strongly recommended. A bare or datacenter IP is usually refused by Cloudflare here.

## `maxRounds` (type: `integer`):

How many times to repeat the whole TLS ladder before giving up. Cloudflare here has hot spells where all profiles are refused in a row, but a fresh round moments later usually lands - so repeating beats widening.

## Actor input object example

```json
{
  "stores": [
    "303380"
  ],
  "maxProfiles": 6,
  "maxConcurrency": 3,
  "proxyConfiguration": {
    "useApifyProxy": true,
    "apifyProxyGroups": [
      "RESIDENTIAL"
    ]
  },
  "maxRounds": 3
}
```

# Actor output Schema

## `stores` (type: `string`):

Dataset items shown in the 'Stores' view.

## `items` (type: `string`):

Every record this run produced, with all fields, as JSON.

# 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 = {
    "stores": [
        "https://www.doordash.com/store/rosalind-coffee-company-garland-303380/"
    ],
    "proxyConfiguration": {
        "useApifyProxy": true,
        "apifyProxyGroups": [
            "RESIDENTIAL"
        ]
    }
};

// Run the Actor and wait for it to finish
const run = await client.actor("xtracto/doordash-store-scraper").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 = {
    "stores": ["https://www.doordash.com/store/rosalind-coffee-company-garland-303380/"],
    "proxyConfiguration": {
        "useApifyProxy": True,
        "apifyProxyGroups": ["RESIDENTIAL"],
    },
}

# Run the Actor and wait for it to finish
run = client.actor("xtracto/doordash-store-scraper").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 '{
  "stores": [
    "https://www.doordash.com/store/rosalind-coffee-company-garland-303380/"
  ],
  "proxyConfiguration": {
    "useApifyProxy": true,
    "apifyProxyGroups": [
      "RESIDENTIAL"
    ]
  }
}' |
apify call xtracto/doordash-store-scraper --silent --output-dataset

```

## MCP server setup

```json
{
    "mcpServers": {
        "apify": {
            "type": "http",
            "url": "https://mcp.apify.com/?tools=fetch-actor-details,xtracto/doordash-store-scraper"
        }
    }
}
```

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/pEbtkdNkTfR74FNgM/builds/zgUnbfQ44Pj0eT813/openapi.json