How to Use the Kaseya MCP in AutoGen

Q: How do I load the Kaseya tools into AutoGen?

Install autogen-extmcp. Use StreamableHttpServerParams to connect. Pass the resulting tool list directly into your AssistantAgent constructor.

Deploy AutoGen agents that debate Kaseya alerts and negotiate infrastructure fixes via this MCP Server.

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Connect Kaseya MCP to AutoGen

Create your Vinkius account to connect Kaseya to AutoGen 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 AutoGen

Ask AI about this MCP

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Perplexity

Multi-agent debate for active alarms

The `list_alarms` tool feeds system warnings to your AutoGen security agent. A performance agent reviews the same alert. They debate whether to isolate the machine or just restart the service. You get a consensus decision instead of a blind automated reaction. The agents use `get_agent_details` to pull specific metrics during their discussion. If the security agent claims the machine is compromised, the performance agent checks the CPU load to verify. The Kaseya MCP Server provides the raw facts they need to argue effectively.

Negotiate workflow deployments

The `list_workflows` tool shows your agents exactly which automations are scheduled. A compliance agent checks `list_audit_logs` to ensure recent changes meet policy. If a planned workflow violates a rule, the agents flag the conflict before anything executes. You build systems where changes require agreement. The compliance agent demands a rollback plan, while the operational agent uses `list_scripts` to find the right script for the job. They negotiate the exact sequence of events.

Cross-reference assets and groups

The `list_assets` tool gives your inventory agent a complete list of hardware. A separate tenant agent uses `list_organizations` and `list_groups` to verify billing alignment. They talk to each other to find unbilled machines or orphaned endpoints. When they find a mismatch, they stop and investigate. They actively query `list_agents` to see if the orphaned machine is still checking in. Your AI client presents you with a verified list of discrepancies and a recommended fix.

Setup guide

Set up Kaseya MCP in AutoGen

Prerequisites

Python 3.10+ installed
autogen-ext[mcp] package
Active Vinkius subscription with a valid endpoint token

1

Install AutoGen with MCP
Run pip install "autogen-ext[mcp]" autogen-agentchat. The MCP extension includes mcp_server_tools for stateless tool access.
2

Fetch tools from the MCP
Call mcp_server_tools(SseServerParams(url=...)) with your Vinkius endpoint. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com.
3

Run your agent
Pass the tools to AssistantAgent and call agent.run(). The agent invokes Kaseya tools and returns structured results.

agent.py

from autogen_ext.tools.mcp import SseServerParams, mcp_server_tools
from autogen_agentchat.agents import AssistantAgent
from autogen_ext.models.openai import OpenAIChatCompletionClient

server_params = SseServerParams(
    url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
)

tools = await mcp_server_tools(server_params)

agent = AssistantAgent(
    name="Kaseya_assistant",
    model_client=OpenAIChatCompletionClient(model="gpt-4o"),
    tools=tools,
)

result = await agent.run("List recent Kaseya data")
print(result.messages[-1].content)

Get your connection token →

Prerequisites

Python 3.10+ installed
autogen-ext[mcp] + autogen-agentchat
Active Vinkius subscription with a valid endpoint token

1

Install dependencies
Same packages as above. McpWorkbench is ideal when your agent needs stateful sessions across multiple tool calls.
2

Use McpWorkbench as context manager
Wrap your agent in async with McpWorkbench(...) to maintain shared state and resources. The workbench manages the full MCP session lifecycle.
3

Run with workbench
Pass workbench=workbench to your agent. State is preserved across multiple tool calls within the same session.

agent.py

from autogen_ext.tools.mcp import McpWorkbench, SseServerParams
from autogen_agentchat.agents import AssistantAgent
from autogen_ext.models.openai import OpenAIChatCompletionClient

server_params = SseServerParams(
    url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
)

async with McpWorkbench(server_params) as workbench:
    agent = AssistantAgent(
        name="Kaseya_assistant",
        model_client=OpenAIChatCompletionClient(model="gpt-4o"),
        workbench=workbench,
    )

    result = await agent.run("List recent Kaseya data")
    print(result.messages[-1].content)

Get your connection token →

Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by Kaseya. 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 Kaseya MCP in AutoGen

Install autogen-ext[mcp]. Use StreamableHttpServerParams to connect. Pass the resulting tool list directly into your AssistantAgent constructor.

Yes. The McpToolAdapter handles schema conversion for all agents in the chat. One agent can check alarms while another pulls asset lists simultaneously.

You configure the agents to share tool outputs in the chat history. If the security agent pulls system info, the performance agent reads the response from the chat instead of making a duplicate call.

It supports both standard input/output and Streamable HTTP. You pick the transport that fits your deployment environment.

These agents read live system metrics, active alerts, and automation configurations via list_workflows. Vinkius handles the connection through a stateless V8 Isolate Sandbox. Your credentials never persist on disk, and the session dies the moment the agents finish their debate.

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