@striderlabs/mcp-doordash

Order food delivery via DoorDash using AI agents

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

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

• npm: npmjs.com/@striderlabs/mcp-doordash
• Claude Plugins: Search "Strider Labs" in Claude
• mcpservers.org: Strider Labs DoorDash
• Full Strider Labs: github.com/striderlabsdev/striderlabs

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
[email protected]
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

• 📖 Full Strider Labs Docs
• 🐛 Report Issues
• 💬 Discussions
• 🌐 Website
• 📧 Email

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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