# DoorDash Restaurant Scraper (`jungle_synthesizer/doordash-scraper`) Actor

Extract restaurant listings from DoorDash for any US city. Enter a location and optional search query to get restaurant names, ratings, delivery fees, estimated delivery times, and cuisine types.

- **URL**: https://apify.com/jungle\_synthesizer/doordash-scraper.md
- **Developed by:** [BowTiedRaccoon](https://apify.com/jungle_synthesizer) (community)
- **Categories:** E-commerce
- **Stats:** 2 total users, 1 monthly users, 68.2% runs succeeded, 0 bookmarks
- **User rating**: No ratings yet

## Pricing

Pay per event

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

## DoorDash Restaurant Scraper

Extract restaurant listings from DoorDash by location and search query. Collect restaurant names, cuisines, ratings, delivery fees, addresses, and listing URLs as structured data.

### How It Works

1. Provide a location (e.g., "New York, NY") and an optional search query (e.g., "pizza", "sushi")
2. Set the maximum number of restaurants to return
3. The actor loads DoorDash results for that location and returns structured restaurant data

### Output Fields

| Field | Description |
|-------|-------------|
| `name` | Restaurant name |
| `url` | DoorDash restaurant page URL |
| `cuisine` | Cuisine type(s) |
| `rating` | Average customer rating |
| `review_count` | Number of ratings |
| `delivery_fee` | Delivery fee shown for the location |
| `delivery_time` | Estimated delivery time |
| `address` | Restaurant address |
| `is_open` | Whether the restaurant is currently open |
| `location` | The location that returned this result |
| `search_query` | The query that returned this result |
| `scraped_at` | ISO 8601 timestamp |

### Input Parameters

- **location** (required): City / area to search within (e.g., "New York, NY")
- **searchQuery**: Keyword to filter restaurants (e.g., "pizza"). Omit to return all restaurants for the location
- **maxItems**: Maximum number of restaurants to return (default: 10, max: 100)

### Notes

Data reflects what DoorDash shows for the requested location at scrape time. Availability, delivery fees, and estimated times are location- and time-dependent.

# Actor input Schema

## `sp_intended_usage` (type: `string`):

What will this data feed? E.g. lead lists, KYB checks, price tracking.

## `sp_improvement_suggestions` (type: `string`):

Provide any feedback or suggestions for improvements.

## `sp_contact` (type: `string`):

We'll personally help with your use case. No spam.

## `location` (type: `string`):

US city to search for restaurants (e.g. "New York, NY", "Chicago, IL", "Los Angeles, CA").

## `searchQuery` (type: `string`):

Optional restaurant name or cuisine filter (e.g. "pizza", "sushi", "Mexican").

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

Maximum number of restaurants to return. Default 10, max 100.

## Actor input object example

```json
{
  "sp_intended_usage": "Describe your intended use...",
  "sp_improvement_suggestions": "Share your suggestions here...",
  "sp_contact": "Share your email here...",
  "location": "New York, NY",
  "searchQuery": "pizza",
  "maxItems": 10
}
```

# Actor output Schema

## `results` (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 = {
    "sp_intended_usage": "Describe your intended use...",
    "sp_improvement_suggestions": "Share your suggestions here...",
    "sp_contact": "Share your email here...",
    "location": "New York, NY",
    "searchQuery": "pizza",
    "maxItems": 10
};

// Run the Actor and wait for it to finish
const run = await client.actor("jungle_synthesizer/doordash-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 = {
    "sp_intended_usage": "Describe your intended use...",
    "sp_improvement_suggestions": "Share your suggestions here...",
    "sp_contact": "Share your email here...",
    "location": "New York, NY",
    "searchQuery": "pizza",
    "maxItems": 10,
}

# Run the Actor and wait for it to finish
run = client.actor("jungle_synthesizer/doordash-scraper").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 '{
  "sp_intended_usage": "Describe your intended use...",
  "sp_improvement_suggestions": "Share your suggestions here...",
  "sp_contact": "Share your email here...",
  "location": "New York, NY",
  "searchQuery": "pizza",
  "maxItems": 10
}' |
apify call jungle_synthesizer/doordash-scraper --silent --output-dataset

```

## MCP server setup

```json
{
    "mcpServers": {
        "apify": {
            "command": "npx",
            "args": [
                "mcp-remote",
                "https://mcp.apify.com/?tools=jungle_synthesizer/doordash-scraper",
                "--header",
                "Authorization: Bearer <YOUR_API_TOKEN>"
            ]
        }
    }
}

```

## OpenAPI specification

Download the OpenAPI definition: https://api.apify.com/v2/acts/xqhmQdzlR6XFzBUyh/builds/aEghAyfwS7fhBxKPL/openapi.json