How to Use the Dot Object Transformer MCP in Google ADK

Q: How does Google ADK interact with Dot Object Transformer?

You instantiate an McpToolset pointing to the Vinkius endpoint and add it to your LlmAgent configuration. This makes the transformation tool available to the agent as a native capability.

Bridge your BigQuery data to Google ADK agents using the Dot Object Transformer.

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Connect Dot Object Transformer MCP to Google ADK

Create your Vinkius account to connect Dot Object Transformer 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 Dot Object Transformer with Google ADK

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Flattening data for Google ADK agents

Use `transform_dot_object` to convert nested BigQuery records into a format your Google ADK agent can actually read. Flattening eliminates the depth issues that often crash agents during context window ingestion. This tool processes the JSON response from your cloud infrastructure and outputs a flat dictionary. It’s the fastest way to get nested records ready for your agent's reasoning process.

Unflattening for Google ADK API calls

When your agent needs to write data back to a complex API, unflattening is mandatory. Pass your dot-notation keys to `transform_dot_object` to restore the nesting requirements of your destination schema. Your Google ADK agent calls this tool before triggering any write operation. It ensures the payload matches the expected JSON structure perfectly.

Native integration for Google ADK

This MCP Server plugs directly into your Google ADK infrastructure using standard transport protocols. It is designed to sit alongside your existing BigQuery and Vertex AI workflows. Deployment is straightforward. You define the server connection once, and the tool remains available for every agent call throughout your enterprise pipeline.

Setup guide

Set up Dot Object Transformer 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 Dot Object Transformer 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="Dot Object Transformer_agent",
    model="gemini-2.0-flash",
    instruction="You have access to Dot Object Transformer tools via MCP.",
    tools=mcp_tools,
)

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Common questions about Dot Object Transformer MCP in Google ADK

You instantiate an `McpToolset` pointing to the Vinkius endpoint and add it to your `LlmAgent` configuration. This makes the transformation tool available to the agent as a native capability.

Absolutely. It flattens massive objects into concise strings, which helps your agent stay within token limits while maintaining access to all data points.

It uses standard dot-notation as the default delimiter for consistent mapping. This provides the most compatibility with standard Google Cloud data types.

Data is processed in an ephemeral sandbox that is wiped immediately after the operation completes. No persistent storage exists for the JSON payloads sent through the server.

Yes. It preserves null values during both flattening and unflattening. Your agent will see the exact state of your data without missing key-value pairs.

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