How to Use the Kaseya MCP in LangChain

Q: Can a LangChain agent run Kaseya scripts automatically?

It can read them. The server exposes listscripts to show available payloads. You still need a separate execution step to fire them off, which keeps agents from accidentally wiping a production server.

Q: What is the best way to trace Kaseya agent queries in LangChain?

Use LangSmith. Every call to listagents or getagentdetails shows up in your tracing dashboard. You track latency and token usage for every API hit.

Build multi-step diagnostic pipelines across your Kaseya endpoints using LangChain and this MCP Server.

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Connect Kaseya MCP to LangChain

Create your Vinkius account to connect Kaseya to LangChain 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 LangChain

Ask AI about this MCP

ChatGPT

Claude

Perplexity

Chain Kaseya MCP Server tools into diagnostic loops

The `list_alarms` tool feeds active system warnings directly into your LangChain ReAct agent. You build chains that catch an alert, extract the machine ID, and immediately run `get_agent_details` to check device status. The agent decides the next step based on the exact error code. LangSmith traces every call. You see exactly how long the VSA 10 API takes to respond and how many tokens your agent spends deciding whether to trigger a reboot. If an endpoint stops checking in, the agent runs `list_audit_logs` to find out who touched it last.

Map infrastructure across organizations

The `list_organizations` tool pulls every tenant in your VSA 10 instance. Your agent takes that list and loops through `list_groups` to map the entire hierarchy. You skip clicking through the web interface to see how machines are grouped. You pass this organizational map into your next chain. When a client calls about a downed server, your AI client already knows where it lives. It calls `list_assets` to pull the hardware specs and dumps the summary into your ticketing system.

Automate script deployment decisions

The `list_scripts` tool exposes your entire library of agent payloads to your LangChain setup. Your pipeline checks `get_system_info` to verify the VSA 10 environment version before deciding which script to push. The agent evaluates the target OS and picks the right executable. You hook this up to `list_workflows` to see what automations are already running. If a machine is stuck in a patching loop, the agent sees the conflict. It stops the chain and alerts you instead of stacking another script on a dying box.

Setup guide

Set up Kaseya MCP in LangChain

Prerequisites

Python 3.10+ installed
langchain-mcp-adapters + langgraph packages
Active Vinkius subscription with a valid endpoint token

1

Install dependencies
Run pip install langchain-mcp-adapters langgraph langchain-openai. The MCP adapters package converts MCP tools into native LangChain BaseTool objects.
2

Connect via HTTP transport
Use MultiServerMCPClient with "transport": "http" pointing to your Vinkius endpoint. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com.
3

Create a ReAct agent
Pass the discovered tools to create_react_agent() from LangGraph. The agent automatically routes Kaseya tool calls through the MCP protocol.
4

Run with any LLM
Swap ChatOpenAI for ChatAnthropic, ChatGoogleGenerativeAI, or any LangChain-compatible model. The MCP tools work identically across all providers.

agent.py

from langchain_mcp_adapters.client import MultiServerMCPClient
from langgraph.prebuilt import create_react_agent
from langchain_openai import ChatOpenAI

async with MultiServerMCPClient({
    "kaseya-mcp": {
        "transport": "http",
        "url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp",
    }
}) as client:
    tools = client.get_tools()

    agent = create_react_agent(
        ChatOpenAI(model="gpt-4o"),
        tools,
    )
    result = await agent.ainvoke({
        "messages": "List recent Kaseya transactions"
    })
    print(result["messages"][-1].content)

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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 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 LangChain

Install the langchain-mcp-adapters package. Point MultiServerMCPClient at your VSA 10 endpoint URL. Call get_tools() and pass the array to your agent.

It can read them. The server exposes list_scripts to show available payloads. You still need a separate execution step to fire them off, which keeps agents from accidentally wiping a production server.

Use LangSmith. Every call to list_agents or get_agent_details shows up in your tracing dashboard. You track latency and token usage for every API hit.

Yes. LangChain is stateless by default. Use client.session() to keep context between a system check and a follow-up query.

Your AI client accesses exact machine identifiers, IP addresses, and user activity logs via list_audit_logs. Vinkius isolates this connection within an ephemeral V8 sandbox. Zero trust applies. The token you provide only accesses the specific organizations you explicitly authorize in the VSA 10 panel.

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