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@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:
{
  "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)

npm install @striderlabs/mcp-doordash

Or with npx directly:

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

Component Version Status
MCP SDK ^1.0.0
Node.js 18+
Claude Desktop Latest
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:

// Search for restaurants
restaurants = search_restaurants({
  location: "San Francisco, CA",
  cuisine: "Thai",
  max_delivery_time: 30
})

// Browse a restaurant's menu
menu = get_restaurant_menu({
  restaurant_id: "thai-place-downtown",
  search: "Pad Thai"
})

// Place an order
order = 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 delivery
status = 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

# Optional: Use a specific DoorDash account
DOORDASH_EMAIL=your-email@example.com
DOORDASH_PASSWORD=your-password  # Highly recommend using .env file

Self-Hosted

# Clone the repo
git clone https://github.com/striderlabsdev/mcp-doordash
cd mcp-doordash

# Install dependencies
npm install

# Start the server
npm 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.

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