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itunified-io

mcp-opnsense

by itunified-io

opnsense_tailscale_service_status

Check whether the Tailscale service (tailscaled) is running on OPNsense to confirm VPN connectivity status.

Instructions

Check if the Tailscale service (tailscaled) is running.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Implementation Reference

  • Handler for opnsense_tailscale_service_status: calls client.get('/tailscale/service/status') and returns the JSON result.
    case "opnsense_tailscale_service_status": {
      const result = await client.get("/tailscale/service/status");
      return { content: [{ type: "text", text: JSON.stringify(result, null, 2) }] };
    }
  • Schema definition for opnsense_tailscale_service_status: no input parameters required.
      {
        name: "opnsense_tailscale_service_status",
        description: "Check if the Tailscale service (tailscaled) is running.",
        inputSchema: {
          type: "object" as const,
          properties: {},
        },
      },
    ];
  • src/index.ts:69-69 (registration)
    Registration: tool definition added to tailscaleToolDefinitions, which is iterated and mapped to handleTailscaleTool in the toolHandlers map.
    for (const def of tailscaleToolDefinitions) toolHandlers.set(def.name, handleTailscaleTool);
Behavior3/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations are provided, so the description must convey behavioral traits. It states the action is a check, implying no side effects, but it does not describe the return format (e.g., boolean, string) or any edge cases. This is acceptable for a simple read operation but leaves some ambiguity.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single, concise sentence that immediately communicates the tool's function. It is front-loaded and wastes no words.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the simplicity of the tool (no parameters, no output schema), the description is nearly complete. However, it would benefit from briefly indicating what the output looks like, e.g., 'returns whether the service is running or not.' This minor gap prevents a perfect score.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The tool has no parameters, so the description does not need to add meaning beyond the schema. The baseline score for 0 parameters is 4, and the description provides no unnecessary detail.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool's purpose: to check if the Tailscale service is running. It uses a specific verb ('Check') and resource ('Tailscale service'), and it distinguishes itself from the sibling tool 'opnsense_tailscale_service_control' which controls the service.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description does not provide explicit guidance on when to use this tool versus alternatives. While the sibling tool for service control implies a distinction, the description itself lacks contextual cues or recommendations for typical usage flows.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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