Doordash

io.github.markswendsen-codePublished by GitHub account markswendsen-codev0.4.1Updated Oct 24, 2026

MCP server for DoorDash - let AI agents order food delivery autonomously with browser-based ordering

VerifiedSTDIODesktop onlyLocation & LifestyleBrowser Automation

Overview

AI-generated overview

A local MCP server that lets an AI assistant search DoorDash restaurants, browse menus, build a cart, place food delivery orders and track them.

What you can do
Search restaurants by name, cuisine or food type and browse menus with item details and prices. Add items to a cart with quantities and special instructions. Place delivery orders with a confirmation step and track their status. Keep a persistent, per-user login session across restarts, with automatic multi-factor authentication handling.
When to use it
Useful when you want an assistant to handle everyday DoorDash ordering end to end — finding nearby restaurants, comparing menus and placing a delivery order — rather than just looking things up. Best for users who already have a DoorDash account and are comfortable letting an agent act on it.
Requirements
Runs locally as a Node.js process (Node.js 18 or later) via the npm package, typically launched with npx from the Claude Desktop config. Needs a DoorDash account, supplied through the DOORDASH_EMAIL and DOORDASH_PASSWORD environment variables, plus DOORDASH_ADDRESS for the delivery location. Desktop only; requires network access and a browser automation runtime.
Before you install
The server stores your DoorDash account password in DOORDASH_PASSWORD and logs in on your behalf, so treat that secret carefully and prefer a local env file. It can place real orders that spend money, so review the confirmation step before letting an agent order unattended. It drives DoorDash through browser automation and realistic browser profiles rather than an official API, which may conflict with DoorDash's terms and can break when the site changes.

Installation

In SourceWeft

  1. Open Doordash in the dashboard and add it to a workspace.
  2. Enable the server for the chats that should use its tools.

Desktop only via STDIO. STDIO servers start a local process, so they need the SourceWeft desktop host.

Other MCP clients

Follow the launch instructions in the repository.

README

@striderlabs/mcp-doordash

Order food delivery via DoorDash using AI agents

[npm] [MCP Registry] [Claude Desktop] [License: MIT]

Part of Strider Labs — action execution for personal AI agents.

Get Started in 2 Minutes

For Claude Desktop Users

  1. Add this to ~/.openclaw/config.json or your Claude Desktop config:
json
{  "mcpServers": {    "doordash": {      "command": "npx",      "args": ["-y", "@striderlabs/mcp-doordash"]    }  }}
  1. Restart Claude.
  2. Tell Claude: "Order Thai food from nearby for delivery today"

Your agent can now place orders. That's it.

Installation (NPM)

bash
npm install @striderlabs/mcp-doordash

Or with npx directly:

bash
npx @striderlabs/mcp-doordash

Features

  • 🔍 Search restaurants by name, cuisine, or food type
  • 📜 Browse menus with full item details and prices
  • 🛒 Add to cart with quantity and special instructions
  • 💳 Place orders with confirmation step
  • 📍 Track orders with real-time status updates
  • 🔐 Persistent sessions - stay logged in across restarts
  • 🔄 Automatic MFA - handles multi-factor authentication
  • 📱 Per-user credentials - encrypted session storage

Tested & Compatible

ComponentVersionStatus
MCP SDK^1.0.0✅
Node.js18+✅
Claude DesktopLatest✅
Claude (API)claude-3.5-sonnet+✅
Anthropic SDK^0.20+✅

Metrics

  • Weekly downloads: 395 (Apr 10-17, 2026) — #1 Strider Labs connector (+24% growth)
  • Status: ✅ Live in production
  • Reliability: 85%+ task completion rate
  • Discovery: npm, Claude Plugins, mcpservers.org, ClawHub, PulseMCP

Available Elsewhere

How It Works

For Agents

Your agent can use these capabilities:

javascript
// Search for restaurantsrestaurants = search_restaurants({  location: "San Francisco, CA",  cuisine: "Thai",  max_delivery_time: 30})
// Browse a restaurant's menumenu = get_restaurant_menu({  restaurant_id: "thai-place-downtown",  search: "Pad Thai"})
// Place an orderorder = place_order({  restaurant_id: "thai-place-downtown",  items: [    { item_id: "pad_thai", quantity: 1 },    { item_id: "spring_rolls", quantity: 2 }  ],  delivery_address: "123 Main St, San Francisco, CA",  special_instructions: "Extra lime on the side"})
// Track deliverystatus = track_order({ order_id: order.order_id })

Session Management

  • Each user has encrypted, persistent credentials
  • Automatic OAuth token refresh
  • MFA handling (SMS/email)
  • Sessions survive agent restarts

Reliability

  • 85%+ task completion rate
  • Automated UI change detection (connectors update when DoorDash changes)
  • Fallback paths for failures
  • 24/7 monitoring + alerting

Configuration

Environment Variables

bash
# Optional: Use a specific DoorDash account[email protected]DOORDASH_PASSWORD=your-password  # Highly recommend using .env file

Self-Hosted

bash
# Clone the repogit clone https://github.com/striderlabsdev/mcp-doordashcd mcp-doordash
# Install dependenciesnpm install
# Start the servernpm start
# Your agent can now connect to localhost:3000

Architecture

How We Connect

This connector uses browser automation (Playwright) to interact with DoorDash, because DoorDash doesn't have a public API. Here's why that's safe and reliable:

  • User-controlled: Your agent only accesses your own DoorDash account
  • Session-based: We store your login session securely, not your password
  • Change-aware: We detect DoorDash UI changes and alert immediately
  • Fingerprinting: We use realistic browser profiles to avoid bot detection
  • Rate-limited: We respect DoorDash's infrastructure with appropriate delays

Security

  • Credentials stored encrypted in your local .env or secure vault
  • Sessions isolated per user
  • No data sent to third parties
  • MIT Licensed — audit the code yourself

Support

Contributing

We welcome contributions! Areas of interest:

  • Bug reports and fixes
  • Feature requests (new restaurants, cuisines, etc.)
  • Performance improvements
  • Documentation enhancements

See CONTRIBUTING.md for guidelines.

License

MIT — Free to use, modify, and distribute. See LICENSE for details.

Built by Strider Labs — Making AI agents actually useful.

GitHub | Website | Discord

Source: README.md at commit 9a6fe59

Tools

0
SourceWeft has not started this package in its sandbox yet, so its tools are not known.

Version history

1
  1. v0.4.1LatestSep 16, 2026

Score

40% of the applicable checks met

How complete this listing is, computed from the registry entry, the endpoint and public sources. Not a rating of the server and not a statement that it is safe or working today: reachability is a dated check, and no installs or reviews go into the score.

  • Reached in the last 30 days25 pts

    Keep the endpoint answering the MCP handshake; SourceWeft probes it every 30 days.

  • Auth declared10 pts

    Declare the credential headers in the registry entry, or keep the endpoint answering the probe so its auth can be classified.

  • Tools listed15 pts

    Answer tools/list without a credential, or ship a package that starts in a sandbox without one.

  • DescriptionRequired15 pts
  • Logo10 pts
  • Documentation15 pts
  • License10 pts

    Declare a license in the repository or the registry entry.