DoorDash MCP Server

An MCP (Model Context Protocol) server that lets AI agents search restaurants, browse menus, compare prices, and manage your DoorDash cart — all without opening a browser or wasting tokens on HTML parsing.

How It Works

Instead of using a browser-based AI agent (which eats context and tokens parsing HTML), this MCP server runs a lightweight background browser that handles authentication and API calls. Your AI agent gets clean JSON — no HTML, no screenshots, no wasted tokens.

Under the hood, it uses DoorDash's internal GraphQL API (reverse-engineered from web traffic) via a Playwright browser instance that maintains your session.

Features

Tool	Description
login_check	Check if your DoorDash session is active
search_restaurants	Search restaurants and food by keyword
get_store_menu	Get full menu with prices, deals, and badges
add_to_cart	Add items to your cart
remove_from_cart	Remove items from cart
list_carts	View all active carts
order_history	Get recent order history

Deals and promotions (Buy 1 Get 1 Free, DashPass offers, etc.) are surfaced in both search results and menu items.

Setup

1. Install

git clone <this-repo>
cd doordash-mcp
npm install
npx playwright install chromium

2. Configure Email

cp .env.example .env

Edit .env and set your DoorDash account email:

DOORDASH_EMAIL=your-email@example.com

3. Login (one-time)

node login.js

This opens a browser, sends an OTP to your email/phone, and saves the session. You only need to do this once (or when your session expires).

On headless Linux: The script auto-starts a virtual display via Xvfb. Make sure xvfb is installed (sudo apt install xvfb).

4. Add to Claude Code

Add to ~/.claude/settings.json:

{
  "mcpServers": {
    "doordash": {
      "command": "node",
      "args": ["/absolute/path/to/doordash-mcp/mcp-server.js"]
    }
  }
}

Restart Claude Code to pick up the new server.

Usage

Once configured, just talk to your AI agent naturally:

• "Search for biryani near me"
• "Show me the Pizza Hut menu"
• "What's the cheapest dosa at Thanjai Restaurant?"
• "Add 2 Masala Dosas to my cart"
• "What did I order last time?"
• "Find me a burger place with deals"

How the Spy Tool Works

Want to discover new endpoints or debug? The spy.js script opens a browser and logs all DoorDash API traffic as you browse:

node spy.js

Browse DoorDash normally — search, view menus, add to cart. All API calls get logged to logs/api-calls.jsonl. Close the browser when done.

Architecture

AI Agent  ──(MCP stdio)──>  mcp-server.js  ──(GraphQL via Playwright)──>  DoorDash API
                                 │
                            browser-data/    (persistent session cookies)

• No headless HTTP: Cloudflare blocks plain HTTP requests. The server uses a real browser (positioned off-screen on macOS, or via Xvfb on Linux)
• Persistent session: Login once, the browser profile in browser-data/ keeps your cookies alive
• Minimal tokens: AI agents get structured JSON, never HTML

Platform Notes

Platform	How it runs
macOS	Browser window positioned off-screen (-32000, -32000)
Linux with display	Same as macOS
Linux headless (SSH)	Auto-starts Xvfb virtual display

Disclaimer

This project reverse-engineers DoorDash's internal web APIs for personal use. It is not affiliated with, endorsed by, or connected to DoorDash in any way. Use at your own risk — endpoints may change without notice, and automated access may violate DoorDash's Terms of Service.

License

MIT