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io.github.AIops-tools/proxmox-aiops

io.github.AIops-tools/proxmox-aiops

by AIops-tools
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Local

guest_health_rca

Read-only

Scan Proxmox VMs and containers for stopped guests, memory saturation, and disks near full, then report worst-first findings with remediation steps.

Instructions

[READ] Scan VMs and containers for stopped guests, memory saturation, and disks near full.

Pulls the /cluster/resources guest view (qemu + lxc) and reports worst-first findings plus the list of stopped guests, each finding citing the measured number and a concrete remediation.

Args: target: Proxmox target name from config; omit to use the default.

Tool call template

This tool declares no required parameters. Add optional parameters from the input schema as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {},
    "name": "guest_health_rca"
  }
}

Input Schema

NameRequiredDescriptionDefault
targetNo

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.8.0

TDQS

A4.1/5.0
Behavior4/5

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

With readOnlyHint=true already covering the safety profile, the description adds real behavioral context: it sources from the /cluster/resources guest view, covers both qemu and lxc, orders findings worst-first, and emits a separate stopped-guest list with measured numbers and remediation. It stops short of stating pagination or result limits, but the output shape is well disclosed.

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

Conciseness4/5

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

Front-loaded with the purpose sentence, then the mechanism, then the arg. The 'Args:' block is slightly formal for one parameter but every sentence carries information; no filler.

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?

No output schema exists, but the description compensates by describing the return shape (worst-first findings, stopped-guest list, per-finding measurement and remediation). For a single-optional-parameter read tool this is close to complete; only volume/format specifics are missing.

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?

Schema description coverage is 0%, so the description carries the burden, and it does: 'target' is defined as a Proxmox target name from config, with the omit-to-use-default behavior made explicit (consistent with the schema's null default). It is a single optional parameter, so little more is needed.

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?

States a specific verb and resource ('Scan VMs and containers') plus exactly what it looks for: stopped guests, memory saturation, disks near full. This is clearly distinguishable from sibling list/status tools like vm_list or cluster_resources.

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 '[READ]' marker and the diagnostic framing imply when to reach for it, but no explicit alternatives are named — notably node_pressure_rca, which covers the analogous node-level scan and is never referenced. Usage is inferable rather than stated.

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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