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vmware-skills

VMware-Monitor

get_vm_info

Read-onlyIdempotent

Fetch detailed static configuration for one VMware VM—CPU, memory, disks, NICs, and snapshots—by exact name to support read-only inventory and monitoring.

Instructions

[READ] Get detailed information about one VM (CPU, memory, disks, NICs, snapshots).

Returns a single detail dict, not the list envelope. Static configuration only — for live CPU/memory use get_vm_performance, and for the same VM correlated with its host, alarms and events use investigate_vm.

Use this when you already know the exact name; get it from list_virtual_machines first, since an unknown name returns a teaching error rather than a match. Reconfiguring the VM belongs to vmware-aiops.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
targetNovCenter/ESXi target from config (default if omitted).
vm_nameYesExact name of the virtual machine.

Schema Changelog

Changes observed during successful MCP inspections.

  1. Addedv2.2.0

TDQS

A4.8/5.0
Behavior5/5

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

Annotations already declare readOnly/idempotent/non-destructive, but the description adds behavior beyond them: the return is a single detail dict rather than a list envelope, coverage is static configuration only, and an unknown name yields a teaching error instead of an empty match. That error semantics disclosure is exactly the kind of thing annotations cannot convey.

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 verb+resource, then routing, then the input constraint; every sentence carries routing or error information. The '[READ]' prefix is mildly redundant with readOnlyHint=true, keeping it just short of a 5.

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

Completeness5/5

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

With no output schema, the description explicitly describes the return shape (single detail dict, not a list envelope) and what it does and does not cover (static config vs. live performance). Everything an agent needs to call this correctly is present.

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 coverage is 100%, so the baseline is 3; the description goes further by stressing that vm_name must be the exact name and by describing the failure mode for a wrong name, which materially shapes how the agent supplies the argument. It says nothing extra about target, but that parameter is already documented as an optional config default.

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 (Get) and resource (one VM) and enumerates the returned fields (CPU, memory, disks, NICs, snapshots). It explicitly distinguishes itself from get_vm_performance, investigate_vm, and list_virtual_machines, so an agent can route correctly without opening any schema.

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

Usage Guidelines5/5

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

Gives explicit when-to-use ('when you already know the exact name'), a prerequisite workflow ('get it from list_virtual_machines first'), named alternatives for other needs, and a when-not clause ('Reconfiguring the VM belongs to vmware-aiops'). Nothing is left to inference.

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