How to Use the Kaseya MCP in Pydantic AI

Q: How do I connect the Kaseya MCP server to Pydantic AI?

Install the pydantic-ai-slimmcp package. Create an MCPToolset pointing to your Vinkius HTTP URL. Pass that toolset into your Agent definition.

Q: How does Pydantic AI handle Kaseya audit logs and privacy?

The listauditlogs tool reads administrative actions, login attempts, and script executions. Your managed infrastructure on Vinkius operates on a zero-trust model. The connection requires a single endpoint token, and the temporary execution environment guarantees no logs persist after the request finishes.

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Connect Kaseya MCP to Pydantic AI

Create your Vinkius account to connect Kaseya to Pydantic AI 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 Pydantic AI

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Type-Safe Kaseya MCP Server Queries

The `list_agents` and `list_alarms` tools return structured JSON that Pydantic AI validates instantly. You configure the server using MCPToolset and attach it to your agent. If the VSA 10 API changes a field name or returns an unexpected string, the framework throws a validation error immediately. This strictness prevents silent failures in production. An agent won't hallucinate a missing IP address or misinterpret an alarm severity level. You get exactly the data schema you expect, or the execution stops before causing damage.

Model-Agnostic Infrastructure Scans

The `get_system_info` and `get_agent_details` tools work identically whether you use a commercial API or a local model. Pydantic AI strips away vendor lock-in. You can test your agent logic with a cheap local model and switch to a heavy reasoning model for production deployment. Your code does not change when swapping providers. The MCP standard keeps the inputs and outputs consistent. The framework handles the translation layer, letting you focus entirely on how the agent interprets the hardware data.

Safe Execution for Automation Workflows

The `list_workflows` and `list_scripts` tools expose powerful automation capabilities through the MCP server that require careful handling. Because Pydantic AI enforces strict type checks on every parameter, your agent cannot pass a malformed argument to a critical script. The framework catches bad inputs before the request ever hits the network. You dictate exactly how the agent interacts with the server. By requiring specific Pydantic models for every decision, you force the agent to justify its actions. It checks the target machine group via `list_groups` and confirms a match before proceeding.

Setup guide

Set up Kaseya MCP in Pydantic AI

Prerequisites

Python 3.10+ installed
pydantic-ai-slim[fastmcp] package
Active Vinkius subscription with a valid endpoint token

1

Install Pydantic AI with FastMCP
Run pip install "pydantic-ai-slim[fastmcp]". The FastMCP toolset replaces the deprecated MCPServerHTTP class with full protocol support.
2

Configure the FastMCPToolset
Pass a JSON-style config dict to FastMCPToolset with your Vinkius URL. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com. Supports Streamable HTTP, SSE, and Stdio transports.
3

Create and run your agent
Pass the toolset to Agent(toolsets=[toolset]) and call agent.run(). Swap openai:gpt-4o for any supported model — Anthropic, Google, Mistral, or Groq.

agent.py

from pydantic_ai import Agent
from pydantic_ai.toolsets.fastmcp import FastMCPToolset

toolset = FastMCPToolset({
    "mcpServers": {
        "kaseya-mcp": {
            "url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
        }
    }
})

agent = Agent(
    "openai:gpt-4o",
    toolsets=[toolset],
    system_prompt="You have access to Kaseya tools.",
)

result = await agent.run("List recent Kaseya transactions")
print(result.output)

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Common questions about Kaseya MCP in Pydantic AI

Install the pydantic-ai-slim[mcp] package. Create an MCPToolset pointing to your Vinkius HTTP URL. Pass that toolset into your Agent definition.

No. The framework unified its connection approach. You just pass the HTTP URL directly into MCPToolset, and it handles the underlying transport automatically.

The framework fails loudly. If an endpoint omits a required field, the runtime throws a strict validation error instead of passing corrupted data to your agent.

Yes. The framework is completely model-agnostic. You can run a local open-weight model to query the server just as easily as you would use a commercial API.

The list_audit_logs tool reads administrative actions, login attempts, and script executions. Your managed infrastructure on Vinkius operates on a zero-trust model. The connection requires a single endpoint token, and the temporary execution environment guarantees no logs persist after the request finishes.

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