How to Use the AdRoll MCP in Google ADK

Bridge AdRoll to Google ADK to analyze marketing data alongside your BigQuery and Vertex AI workflows.

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Connect AdRoll MCP to Google ADK

Create your Vinkius account to connect AdRoll 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.

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Setup AdRoll with Google ADK

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Query AdRoll stats with Google ADK

Your agent runs `get_performance_report` to pull metrics for specific advertiser accounts. It acts as a bridge between your marketing spend and your cloud data warehouse. This setup allows for deep analysis within your existing Google Cloud environment. You get clean performance data without building complex ingestion pipelines.

Inspect active campaigns using Google ADK

The `list_campaigns` tool returns your current account status instantly. Your agent uses this to verify campaign health before triggering downstream logic. It removes the need for constant manual checks in the web interface. You have a direct line to your live account status from inside your terminal.

Organize creatives via Google ADK

Run `list_ads` to catalog your creative assets for audit purposes. Your agent sorts through your library and identifies which creatives are tied to specific campaigns. This gives your agent total visibility into your ad structure. You control exactly which assets are analyzed by your model.

Setup guide

Set up AdRoll 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 AdRoll 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="AdRoll_agent",
    model="gemini-2.0-flash",
    instruction="You have access to AdRoll tools via MCP.",
    tools=mcp_tools,
)

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Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by AdRoll. 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.

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Common questions about AdRoll MCP in Google ADK

Add the server to your McpToolset instance using the provided HTTP parameters. The framework handles the transport, making tools available for your Gemini models.

Gemini models with long-context windows can ingest large JSON payloads returned by these tools. You can pass extensive performance data directly into your prompt.

The Vinkius endpoint token handles the heavy lifting. You keep your API keys away from your source code and rely on the managed server transport.

The data stays within your agent session. Only the specific performance metrics requested by your agent are transmitted between the API and your environment.

Your agent uses these IDs to identify campaign performance. This information is encrypted in transit and never persisted by the server itself.

Use it with your favorite AI tools

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