How to Use the Automate.io MCP in Google ADK

Q: Can I use Google ADK to analyze Automate.io logs?

Your agent calls listbotruns to ingest your execution history into the Gemini context. It then performs reasoning over millions of tokens to find failure patterns.

Connect your Automate.io workflows to your Google ADK agent for deep data analysis on GCP.

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Connect Automate.io MCP to Google ADK

Create your Vinkius account to connect Automate.io 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 Automate.io with Google ADK

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List integrations in Google ADK

Your agent triggers `list_apps` to see every supported integration current enabled. It maps these to your BigQuery datasets for cross-platform reporting. This gives your agent a full view of your automation ecosystem. It knows exactly what connections are available before it attempts to write data.

Automate.io bot management for Google ADK

Use `list_bots` to pull your full directory of automations into your agent's long-context window. The Gemini model analyzes these to spot redundant tasks. This interaction happens over the HTTP transport. Your agent processes the list and flags bots that haven't been modified in months.

Filter tools in Google ADK

You can restrict the agent by passing a tool_names filter to the McpToolset. This prevents the agent from calling tools like `get_usage` if you only want it to read bot lists. It keeps your agent focused on specific tasks. You define the boundaries of what the agent can do with your account data.

Setup guide

Set up Automate.io 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 Automate.io 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="Automate.io_agent",
    model="gemini-2.0-flash",
    instruction="You have access to Automate.io 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 Automate.io. 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 Automate.io MCP in Google ADK

You install the server via the McpToolset class and pass it into your LlmAgent. This allows your agent to query your automation data alongside your BigQuery tables.

The server supports both HTTP and Stdio transports. You choose the one that fits your deployment architecture on Google Cloud.

Your agent calls `list_bot_runs` to ingest your execution history into the Gemini context. It then performs reasoning over millions of tokens to find failure patterns.

The McpToolset automatically parses the server response to define the available tools. You just point the parameters to your Vinkius endpoint.

Your authentication token is the only piece of sensitive data required, which stays within your secure environment. The server processes requests in an isolated sandbox, preventing any exposure of your bot API keys.

Use it with your favorite AI tools

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