How to Use the Kaseya MCP in Google ADK

Q: Can Google ADK filter the Kaseya tools my agent sees?

Yes. You provide an array to the toolnames argument when setting up the toolset. This restricts the agent to specific functions like getagentdetails while hiding automation endpoints.

Q: How does Google ADK protect Kaseya system information and privacy?

The getsysteminfo tool accesses OS versions, CPU architectures, and memory states. Vinkius handles authentication securely behind the scenes. Your managed MCP instance runs in an isolated sandbox that spins up on demand and destroys itself after the session ends, leaving no persistent data behind.

Connect Kaseya VSA 10 to Google ADK to analyze endpoint data alongside BigQuery and Vertex AI.

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Connect Kaseya MCP to Google ADK

Create your Vinkius account to connect Kaseya 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 Kaseya with Google ADK

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Map Kaseya MCP Server Data to BigQuery

The `list_assets` and `get_system_info` tools pull hardware inventory directly into your Gemini agent's context. You configure the server using McpToolset and pass it to your LlmAgent. From there, the agent can cross-reference physical endpoint specs with enterprise datasets already sitting in Google Cloud. Gemini models handle over a million tokens of context natively. Your agent can dump the entire output of `list_agents` into memory at once. It reads the raw device statuses, compares them against historical BigQuery logs, and generates infrastructure reports without chunking or external vector databases.

Analyze Endpoints with Google ADK

The `list_audit_logs` tool feeds directly from the MCP server into Vertex AI for deep anomaly detection. Instead of manually parsing thousands of rows, you let the agent scan the logs for unusual admin logins or failed script executions. It identifies patterns that human operators miss during routine checks. You restrict which operations the agent sees using the tool_names filter. If a specific pipeline only needs to monitor alerts, you expose just `list_alarms`. The framework ignores the rest of the endpoints, keeping the agent focused strictly on incident response.

Automate Responses Across Organizations

The `list_organizations` and `list_groups` tools let your agent map your entire multi-tenant hierarchy. When an alert fires in a specific tenant, the agent knows exactly which machine group is affected. It plots the blast radius before taking any action. Once the scope is clear, the agent checks available responses using `list_scripts`. It drafts a remediation plan based on the specific hardware constraints of that group. You review the proposed fix in your terminal or HTTP transport, approve it, and move on.

Setup guide

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

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Common questions about Kaseya MCP in Google ADK

You install google-adk via pip. Wrap your Vinkius endpoint URL inside StreamableHttpServerParameters, then pass that into McpToolset.

Yes. You provide an array to the tool_names argument when setting up the toolset. This restricts the agent to specific functions like get_agent_details while hiding automation endpoints.

It works perfectly. The agent can fetch thousands of records using list_assets and process them simultaneously. You do not need to build complex pagination logic.

The framework supports both Stdio and HTTP transports. Vinkius provides an HTTP endpoint, so you will use the streamable HTTP configuration.

The get_system_info tool accesses OS versions, CPU architectures, and memory states. Vinkius handles authentication securely behind the scenes. Your managed MCP instance runs in an isolated sandbox that spins up on demand and destroys itself after the session ends, leaving no persistent data behind.

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