DoorDash MCP Server
Enables AI agents to search restaurants, place delivery orders, and track real-time delivery status using the DoorDash Drive API. It includes a built-in mock data mode that allows for testing and demonstrating delivery lifecycles without requiring live API credentials.
README
DoorDash MCP Server
MCP server that wraps DoorDash's Drive API. Lets AI agents (Claude, Cursor, etc.) search restaurants, place delivery orders, and track them in real time.
Comes with built-in mock data so you can try it without DoorDash API credentials.
Architecture
graph TB
subgraph "MCP Clients"
C1[Claude Desktop]
C2[Cursor]
C3[Claude Code]
end
subgraph "MCP Server"
T[Transport - stdio / SSE]
S[Server Core]
subgraph "Tools"
RT[Restaurant Tools]
DT[Delivery Tools]
TT[Tracking Tools]
AT[Account Tools]
end
subgraph "Service Layer"
MC[Mock Client]
DC[DoorDash Client]
end
end
DD[DoorDash Drive API]
C1 & C2 & C3 --> T
T --> S
S --> RT & DT & TT & AT
RT & DT & TT & AT --> MC
RT & DT & TT & AT -.->|live mode| DC
DC --> DD
Quick Start
No API keys needed — mock mode is the default:
git clone https://github.com/chaituredd/doordash-mcp-server.git
cd doordash-mcp-server
npm install && npm run build
The build step prints the MCP client config JSON. Paste it into your Claude Desktop or Cursor settings.
Or with Docker:
docker compose -f docker/docker-compose.yml up
Tools
12 tools across 4 domains:
| Tool | What it does | Mock | Live |
|---|---|---|---|
search_restaurants |
Find nearby restaurants | ✓ | ✓ |
get_menu |
Full menu with prices and modifiers | ✓ | ✓ |
get_restaurant_details |
Hours, ratings, delivery info | ✓ | ✓ |
create_delivery_quote |
Fee estimate, valid for 5 min | ✓ | ✓ |
accept_delivery_quote |
Confirm quote → start delivery | ✓ | ✓ |
create_delivery |
Skip the quote, create directly | ✓ | ✓ |
cancel_delivery |
Cancel before pickup | ✓ | ✓ |
get_delivery_status |
Status timeline + ETA | ✓ | ✓ |
list_active_deliveries |
All in-progress deliveries | ✓ | ✓ |
get_business_info |
Business account details | ✓ | ✓ |
list_stores |
Stores under a business | ✓ | ✓ |
create_store |
Register a new store | ✓ | ✓ |
Configuration
Copy .env.example to .env:
| Variable | Required | Default | Description |
|---|---|---|---|
DOORDASH_API_MODE |
No | mock |
mock or live |
TRANSPORT |
No | stdio |
stdio or http |
PORT |
No | 3000 |
Port when using HTTP transport |
DOORDASH_DEVELOPER_ID |
Live only | — | From DoorDash Developer Portal |
DOORDASH_KEY_ID |
Live only | — | From DoorDash Developer Portal |
DOORDASH_SIGNING_SECRET |
Live only | — | From DoorDash Developer Portal |
MCP Client Config
Claude Desktop / Claude Code:
{
"mcpServers": {
"doordash": {
"command": "node",
"args": ["/absolute/path/to/doordash-mcp-server/build/index.js"],
"env": {
"DOORDASH_API_MODE": "mock"
}
}
}
}
Cursor (.cursor/mcp.json):
{
"mcpServers": {
"doordash": {
"command": "node",
"args": ["/absolute/path/to/doordash-mcp-server/build/index.js"]
}
}
}
Development
npm install # install deps
npm run dev # watch mode
npm test # run tests
npm run test:watch # tests in watch mode
npm run typecheck # type checking
Project Layout
src/
├── index.ts # entry point, transport setup
├── server.ts # tool/resource/prompt registration
├── config.ts # env validation (Zod)
├── tools/ # one file per tool domain
├── services/ # DoorDash clients (real + mock)
├── data/ # mock restaurants, menus, deliveries
├── types/
└── utils/ # logger, errors, validation
Live Mode
- Sign up at developer.doordash.com (sandbox is free)
- Grab your credentials and run:
DOORDASH_API_MODE=live \
DOORDASH_DEVELOPER_ID=your_id \
DOORDASH_KEY_ID=your_key \
DOORDASH_SIGNING_SECRET=your_secret \
npm start
New accounts start in sandbox. Production requires a separate application to DoorDash.
How It Works
Implements the Model Context Protocol spec. AI agents call tools through MCP, the server translates those into DoorDash Drive API requests (or returns mock data), and formats responses for the agent to show the user.
The mock layer is stateful — create a delivery and it'll progress through created → confirmed → enroute_to_pickup → picked_up → enroute_to_dropoff → delivered each time you check status. Makes demos actually useful.
Tech
TypeScript (strict), Node 18+, @modelcontextprotocol/sdk v1.x, Zod for validation, Axios for HTTP, Vitest for tests, GitHub Actions CI, Docker multi-stage build.
Why This Exists
Wanted to learn MCP properly by building something non-trivial with it. DoorDash's Drive API seemed like a good fit since it has a real delivery lifecycle to model. The mock layer turned out to be the most useful part — you can demo the whole flow without any API keys.
License
MIT
Recommended Servers
playwright-mcp
A Model Context Protocol server that enables LLMs to interact with web pages through structured accessibility snapshots without requiring vision models or screenshots.
Magic Component Platform (MCP)
An AI-powered tool that generates modern UI components from natural language descriptions, integrating with popular IDEs to streamline UI development workflow.
Audiense Insights MCP Server
Enables interaction with Audiense Insights accounts via the Model Context Protocol, facilitating the extraction and analysis of marketing insights and audience data including demographics, behavior, and influencer engagement.
VeyraX MCP
Single MCP tool to connect all your favorite tools: Gmail, Calendar and 40 more.
graphlit-mcp-server
The Model Context Protocol (MCP) Server enables integration between MCP clients and the Graphlit service. Ingest anything from Slack to Gmail to podcast feeds, in addition to web crawling, into a Graphlit project - and then retrieve relevant contents from the MCP client.
Kagi MCP Server
An MCP server that integrates Kagi search capabilities with Claude AI, enabling Claude to perform real-time web searches when answering questions that require up-to-date information.
E2B
Using MCP to run code via e2b.
Neon Database
MCP server for interacting with Neon Management API and databases
Exa Search
A Model Context Protocol (MCP) server lets AI assistants like Claude use the Exa AI Search API for web searches. This setup allows AI models to get real-time web information in a safe and controlled way.
Qdrant Server
This repository is an example of how to create a MCP server for Qdrant, a vector search engine.