How to Use the Amazon Ads MCP in Google ADK

Q: How do I configure the Amazon Ads MCP Server with Google ADK?

You define an McpToolset using your Vinkius endpoint URL. Wrap the URL in StreamableHttpServerParameters, then pass the resulting toolset into the tools array of your LlmAgent initialization.

Q: Can Google ADK filter which Amazon Ads tools my agent accesses?

You have full control over tool exposure. The McpToolset accepts an optional toolnames list. If you only want your agent to read data, you just include listspkeywords and exclude the reporting tools.

Feed Amazon Ads data directly into BigQuery using Google ADK. Build Gemini agents that reason over massive campaign histories.

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Connect Amazon Ads MCP to Google ADK

Create your Vinkius account to connect Amazon Ads 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 Amazon Ads with Google ADK

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Google ADK Integration for Retail Data

Retail media requires serious data infrastructure. Connecting this MCP Server to Google ADK lets your Gemini agents pull live advertising metrics straight into your Google Cloud environment. You can map out your entire account hierarchy by having the agent call `list_profiles` and then iterate through your active brands. Long-context reasoning changes how you analyze ad performance. Instead of looking at a single week, Gemini swallows months of keyword data. The agent triggers `request_sp_report`, waits for the file, and dumps the raw performance metrics directly into BigQuery for your data science team to model.

Asynchronous Reporting Pipelines

Amazon's reporting API requires a strict request-and-poll pattern. Your Google ADK agent handles this natively. It fires off `request_sp_report` to generate the initial job ID for your Sponsored Products performance data. The framework manages the waiting period perfectly. It loops `get_report_status` until Amazon finishes building the file. Once complete, the agent grabs the download link and can immediately pass those metrics into Vertex AI for predictive bid modeling.

Setup guide

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

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Common questions about Amazon Ads MCP in Google ADK

You define an `McpToolset` using your Vinkius endpoint URL. Wrap the URL in `StreamableHttpServerParameters`, then pass the resulting toolset into the `tools` array of your `LlmAgent` initialization.

You have full control over tool exposure. The `McpToolset` accepts an optional `tool_names` list. If you only want your agent to read data, you just include `list_sp_keywords` and exclude the reporting tools.

Gemini's massive context window pairs perfectly with bulk advertising data. Your agent can pull thousands of rows via `list_sb_keywords` and analyze the entire dataset in memory without chunking or losing the thread.

That is the primary advantage of this setup. Your agent grabs the report links via `get_report_status` and can immediately write the resulting CSV data into BigQuery tables or Cloud Storage buckets.

Every single request routes through a zero-trust Vinkius MCP isolate. When your agent fetches search term reports and bid amounts, the sandbox processes the API call and immediately destroys itself. Nobody else shares your execution memory.

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