How to Use the DoorDash Drive MCP in Google ADK

Q: How do I set up the DoorDash Drive integration with Google ADK?

Install the SDK using pip, then initialize McpToolset with the HTTP URL provided by Vinkius. This registers the MCP Server directly with your agent.

Q: Can I restrict which DoorDash Drive tools my Google ADK agent can access?

Yes, you can use the optional toolnames filter when defining your McpToolset. This lets you expose read-only tools like getdeliverydetails while blocking write operations like cancelactivedelivery for specific agent profiles.

Connect DoorDash Drive logistics tools directly to your Gemini models using the enterprise-ready Google ADK.

See Vinkius in Action

Works with every AI agent you already use

…and any MCP-compatible client

MCP Servers - Free for Subscribers

Connect DoorDash Drive MCP to Google ADK

Create your Vinkius account to connect DoorDash Drive to Google ADK and route execution through our secure gateway. The platform manages server hosting, runtime updates, and security layers. Configuration requires no manual server provisioning.

GDPR Free for Subscribers

Setup DoorDash Drive with Google ADK

Ask AI about this MCP

ChatGPT

Claude

Perplexity

Enterprise Logistics with Google ADK

This MCP Server exposes `list_doordash_deliveries` to connect Gemini's million-token context window to your DoorDash logistics network. Your agent can ingest massive logistics logs from BigQuery and immediately cross-reference active DoorDash runs with historical performance data. By passing the `McpToolset` directly to your Google ADK `LlmAgent`, Gemini discovers tools like `get_delivery_details` and updates your database in real time. The model processes complex DoorDash routing instructions and customer feedback alongside raw API payloads without losing track of the execution state.

Automated Quote Analysis and Dispatch

Your Gemini agent uses `get_delivery_quote` to evaluate shipping costs across thousands of active DoorDash orders stored in Vertex AI. The Google ADK allows the agent to analyze these quotes in parallel, matching delivery ETAs against your customer service level agreements. Once the model selects the optimal DoorDash quote, it executes `create_new_delivery` to dispatch the driver. If you want to limit the agent's authority, you can use the `tool_names` filter in the ADK to restrict its access to specific operations like quoting and auditing.

High-Volume Delivery Auditing

The agent calls `quick_delivery_volume_audit` to pull high-level DoorDash performance summaries directly into your Google Cloud data pipeline. This tool tracks success rates and active volumes, allowing Gemini to draft operational reports and identify bottlenecks in your fulfillment centers. For granular tracking, the agent calls `list_in_progress_deliveries` and `list_latest_deliveries` to map out current DoorDash driver locations. If a customer disputes a dropoff, the model queries `search_deliveries_by_external_id` to retrieve the exact transit history and update your internal records.

Setup guide

Set up DoorDash Drive MCP in Google ADK

Prerequisites

Python 3.10+ installed
google-adk package (pip install google-adk)
Active Vinkius subscription with a valid endpoint token

1

Install Google ADK

Run pip install google-adk to install the Agent Development Kit. MCP support is included via the McpToolset class.
2

Connect via SSE transport

Use McpToolset.from_server() with SseServerParams pointing to your Vinkius endpoint. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com.
3

Create an LlmAgent

Pass the returned mcp_tools list directly to LlmAgent(tools=mcp_tools). The ADK maps each MCP tool to a native Gemini function call — no manual schema definitions required.
4

Run with any Gemini model

The agent works with any Gemini model (gemini-2.0-flash, gemini-2.5-pro, etc.). Copy the full example on the right to get started with DoorDash Drive tools in your ADK agent.

agent.py

from google.adk.agents import LlmAgent
from google.adk.tools.mcp_tool.mcp_toolset import McpToolset
from google.adk.tools.mcp_tool.mcp_session_manager import SseServerParams

# Connect to the MCP via SSE
mcp_tools, exit_stack = await McpToolset.from_server(
    connection_params=SseServerParams(
        url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
    )
)

# Create your agent with auto-discovered tools
agent = LlmAgent(
    name="DoorDash Drive_agent",
    model="gemini-2.0-flash",
    instruction="You have access to DoorDash Drive tools via MCP.",
    tools=mcp_tools,
)

Get your connection token →

Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by DoorDash Drive. All third-party trademarks, logos, and brand names are the property of their respective owners. Their use on this website is strictly for informational purposes to identify service compatibility and interoperability.

Why Choose Vinkius

Vinkius connects your tools to AI with real-time monitoring and automatic cost savings — all from one dashboard.

Connect DoorDash Drive now

Real-time monitoring

Live

visibility into every interaction

Connect your favorite tools to your AI and see exactly what's happening — every request, every response, in real time.

Built-in savings

60%

lower AI costs

Vinkius compresses data between your apps and your AI automatically. Lower bills every month — no configuration required.

Single dashboard

One

place for every integration

Every tool your AI connects to, managed from a single screen. One account, complete control.

Common questions about DoorDash Drive MCP in Google ADK

Install the SDK using pip, then initialize `McpToolset` with the HTTP URL provided by Vinkius. This registers the MCP Server directly with your agent.

Yes, you can use the optional `tool_names` filter when defining your `McpToolset`. This lets you expose read-only tools like `get_delivery_details` while blocking write operations like `cancel_active_delivery` for specific agent profiles.

Gemini's context window holds historical delivery data retrieved from `list_doordash_deliveries`. This lets the model evaluate historical trends over thousands of runs before deciding to call `get_delivery_quote` for new dispatches.

The integration supports both Stdio and HTTP transports. For cloud-deployed enterprise agents running on Vertex AI, we recommend using the Streamable HTTP transport to connect securely to the managed MCP Server.

We process your developer credentials and account metadata entirely in memory within our zero-trust V8 sandbox. Your DoorDash developer keys are encrypted in transit and never persisted, keeping your corporate profile secure.

Use it with your favorite AI tools

Connect this server to Cursor, Claude, VS Code, and more.

OpenAI Agents SDK sdk-python

Google ADK sdk-python

Pydantic AI sdk-python

Vercel AI SDK sdk-typescript