How to Use the Hevo Data (ETL & Data Pipeline) MCP in Google ADK

Give your Gemini agents on the Google ADK real-time visibility into your data warehouse pipelines via this MCP Server.

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Connect Hevo Data (ETL & Data Pipeline) MCP to Google ADK

Create your Vinkius account to connect Hevo Data (ETL & Data Pipeline) 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 Hevo Data (ETL & Data Pipeline) with Google ADK

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Track active data pipelines in Google ADK

`list_pipelines` exposes your active Hevo integrations directly to your Gemini model. Your agent reads the current status of every data source to confirm that your BigQuery tables are actually fresh. If a sync stalls, the agent triggers a diagnostic lookup with `get_pipeline`. You get the exact failure details inside your enterprise chat interface without opening another dashboard.

Map target destinations from your agent

`list_destinations` shows your agent exactly where your ETL pipelines dump their payloads. This lets the agent verify that incoming raw files map to the correct production databases. It prevents your agent from querying empty tables. By checking the target status first, your team saves compute costs on dead queries.

Audit active models using this MCP Server

`list_models` retrieves the transformation structures defined in your ETL account. Your agent reads these configurations to understand how raw source fields map to destination columns. It pairs with `list_workflows` to verify the execution order of your data cleanups. Your agent flags out-of-order runs before they corrupt your business intelligence dashboards.

Setup guide

Set up Hevo Data (ETL & Data Pipeline) 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 Hevo Data (ETL & Data Pipeline) 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="Hevo Data (ETL & Data Pipeline)_agent",
    model="gemini-2.0-flash",
    instruction="You have access to Hevo Data (ETL & Data Pipeline) tools via MCP.",
    tools=mcp_tools,
)

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Common questions about Hevo Data (ETL & Data Pipeline) MCP in Google ADK

Initialize the toolset with your server parameters and pass it to your agent constructor. The SDK handles the connection automatically, exposing the pipeline tools directly to Gemini's reasoning engine.

No, it cannot. The toolset only supports monitoring actions like listing pipelines and checking workflow configurations to prevent accidental changes to your production infrastructure.

Yes. The agent uses the destination listing tool to identify BigQuery targets, making it easy to confirm that your pipeline data is landing in the right cloud warehouse.

You can pass a specific list of tool names to the toolset parameters when initializing your setup. This restricts the agent to only the monitoring endpoints you want it to access.

All database credentials and raw table contents remain secure inside your cloud project. This MCP endpoint only reads pipeline operational metadata, ensuring your access keys never leak.

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