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vmware-vks

vmware-vks

by vmware-skills
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Remote

list_vm_classes

Read-onlyIdempotent

Retrieve available VM classes for sizing Tanzu Kubernetes cluster nodes, including CPU, memory, and GPU counts, before provisioning a TKC cluster.

Instructions

[READ] List VM classes available for sizing TKC cluster nodes.

Returns the list envelope: items of {id (e.g. 'best-effort-large'), cpu_count, memory_mb, gpu_count (vGPU + DirectPath I/O; 0 if none)} plus returned/total/truncated — one call returns them all, so truncated is always false. Call this before create_tkc_cluster and pass the chosen 'id' as its vm_class; 'guaranteed-' classes reserve resources, 'best-effort-' do not.

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": "list_vm_classes"
  }
}

Input Schema

NameRequiredDescriptionDefault
targetNovCenter in config.yaml; omit for the default.

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed2 schema fields changedv1.8.14
    • addedInput schema / additionalProperties
      Added value: +false
    • addedInput schema / properties / target / description
      Added value: +"vCenter in config.yaml; omit for the default."
  2. Addedv1.8.9
  3. Removedv1.8.8
  4. Addedv1.5.26
  5. Removedv1.5.23
  6. First observedv1.3.2

TDQS

A4.7/5.0
Behavior5/5

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

Annotations already mark this as read-only and safe, and the description adds meaningful behavior beyond that: it explains the exact list envelope shape, confirms that one call returns all results and truncated is always false, and discloses the resource-reservation distinction between class prefixes. No contradiction with annotations exists.

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 front-loaded with the tool's core purpose and contains no filler. Every sentence contributes: what it lists, what the response contains, why the list is complete, how to use the result, and the meaningful class-prefix distinction. It is compact despite carrying substantial information.

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?

For a simple read-only list tool with no output schema, the description fully compensates by specifying the envelope structure, item fields, pagination behavior, and downstream usage. It gives an agent everything needed to invoke the tool and interpret its result correctly.

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

Parameters3/5

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

The only parameter, target, is fully documented in the schema with 100% coverage, and the description does not add new meaning about it. The description's guidance about the returned id being used as vm_class is useful for output consumption, but it does not deepen understanding of the target parameter itself, so the baseline score of 3 is appropriate.

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 states a specific verb and resource: 'List VM classes available for sizing TKC cluster nodes.' It clearly identifies what the tool returns and connects it to a concrete use case. It also distinguishes itself from sibling operations like create_tkc_cluster by framing the call as a prerequisite.

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?

It explicitly instructs when to use this tool: 'Call this before create_tkc_cluster and pass the chosen id as its vm_class.' It also provides decision-relevant semantics by explaining that 'guaranteed-*' classes reserve resources while 'best-effort-*' do not, which helps an agent choose the appropriate class.

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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