How to Use the Kaseya MCP in LlamaIndex

Q: Can LlamaIndex cache Kaseya asset lists?

Yes. You pull the data using listassets and embed it into your vector store. Your agent queries the local index for fast lookups instead of hitting the VSA 10 API every time.

Q: How do I filter which Kaseya tools LlamaIndex can access?

Pass an allowedtools array when initializing the spec. You might give a read-only agent access to listagents while blocking listauditlogs.

Index your Kaseya endpoints and alarms into a searchable LlamaIndex knowledge base using this MCP Server.

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Connect Kaseya MCP to LlamaIndex

Create your Vinkius account to connect Kaseya to LlamaIndex 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 LlamaIndex

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Query your LlamaIndex RAG for active alarms

The `list_alarms` tool dumps active system alerts into your vector store. LlamaIndex reads the raw JSON from VSA 10 and converts it into semantic embeddings. You ask your AI client why a specific server farm is failing, and it checks the index instead of guessing. You skip writing complex API wrappers. The framework automatically parses the alerts and maps them against historical data. When a disk space warning fires, your agent cross-references it with previous outages to predict when the drive will actually fill up.

Index full asset inventories

The `list_assets` tool pulls every managed device into your application. LlamaIndex indexes the hardware specs, OS versions, and IP assignments. You query your RAG setup to find every machine running an outdated Windows build. The framework combines this with `list_agents` to track availability status. If an asset exists but the agent is offline, the system flags the discrepancy. Your queries return hard data directly from the Kaseya MCP Server.

Build organizational knowledge graphs

The `list_organizations` tool maps your tenant structure while `list_groups` defines the machine clusters. LlamaIndex uses these tools to build a relationship graph of your entire managed infrastructure. You ask your agent which client owns a specific subnet, and it traces the path. You enrich this graph with `list_workflows`. The index knows which automation rules apply to which machine groups. When a client asks for an audit of their patching schedule, your agent generates the report from indexed API responses.

Setup guide

Set up Kaseya MCP in LlamaIndex

Prerequisites

Python 3.10+ installed
llama-index-tools-mcp package
Active Vinkius subscription with a valid endpoint token

1

Install dependencies
Run pip install llama-index-tools-mcp llama-index-llms-openai. The MCP tools package provides BasicMCPClient and McpToolSpec.
2

Connect with BasicMCPClient
Point BasicMCPClient to your Vinkius endpoint URL. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com. Supports SSE and Streamable HTTP transports.
3

Convert to LlamaIndex tools
Call mcp_tool_spec.to_tool_list_async() to convert all Kaseya MCP tools into native FunctionTool objects that any LlamaIndex agent can use.
4

Run with any LLM
Create a FunctionAgent with the tools and your preferred LLM. Swap OpenAI for Anthropic, Gemini, or any LlamaIndex-supported provider.

agent.py

from llama_index.tools.mcp import BasicMCPClient, McpToolSpec
from llama_index.core.agent.workflow import FunctionAgent
from llama_index.llms.openai import OpenAI

# Connect to the MCP
mcp_client = BasicMCPClient(
    "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
)
mcp_tool_spec = McpToolSpec(client=mcp_client)

# Convert MCP tools to LlamaIndex tools
tools = await mcp_tool_spec.to_tool_list_async()

# Create and run the agent
agent = FunctionAgent(
    tools=tools,
    llm=OpenAI(model="gpt-4o"),
    system_prompt="You have access to Kaseya tools.",
)
response = await agent.run("List recent Kaseya data")

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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 LlamaIndex

Install llama-index-tools-mcp. Connect BasicMCPClient to the server URL. Use McpToolSpec to convert the endpoints into a tool list for your FunctionAgent.

Yes. You pull the data using list_assets and embed it into your vector store. Your agent queries the local index for fast lookups instead of hitting the VSA 10 API every time.

Pass an allowed_tools array when initializing the spec. You might give a read-only agent access to list_agents while blocking list_audit_logs.

Live API calls tell you what is broken right now. RAG tells you if it broke last month. Combining active alerts with indexed documentation gives your agent context to actually solve the problem.

The server pulls hardware specs, internal IP addresses, and group structures via list_groups. Vinkius routes these requests through an ephemeral, zero-trust connection. The indexer only sees the data authorized by the specific VSA 10 API token you provide.

Use it with your favorite AI tools

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