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vmware-skills

io.github.zw008/vmware-debug

case_readiness

Read-onlyIdempotent

Assess what conclusion strength your VMware environment can reach before investigating. Reveals per-class availability, symptom ceilings, and unrecognized skills to avoid misdirected installs.

Instructions

[READ] What strength of conclusion can this environment reach?

WHEN: before starting an investigation, or when a case will not go higher and you want to know whether that is fixable. Answering this first is worth far more than discovering it halfway through.

RETURNS: {classes, categories, unrecognised_skills, note}. A name that matched no skill in the catalogue comes back in unrecognised_skills rather than being absorbed into "not installed" — otherwise a typo reads as advice to install something you already have. Per evidence class: whether it is available, through which tools, and if not, how_to_supply. Per symptom category (storage, network, compute, ha_drs, configuration, accelerator, kubernetes, hardware): a ceiling and the independent_sources behind it. There is deliberately no single score — "readiness 78%" cannot be acted on, "storage reaches Probable, hardware reaches Candidate" can.

GOTCHAS: two classes served by the SAME skill count as one source, so two available classes do not always mean Probable. The hardware class is unavailable no matter what is installed — nothing in this family reaches below ESXi — and the knowledge class becomes available only when entries are mounted under $OPS_HOME/knowledge/.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
available_skillsNoThe skills actually installed and configured, in either of the family's spellings — "monitor" and "vmware-monitor" name the same thing. Omit to assume all of them, which reports the ceiling imposed by the family itself rather than by this install. A name matching no catalogued skill comes back in `unrecognised_skills` rather than being read as "not installed".

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. Addedv1.11.1

TDQS

A4.5/5.0
Behavior5/5

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

The description goes well beyond the readOnly/idempotent annotations by explaining non-obvious behavior: unmatched skills are returned in unrecognised_skills instead of being treated as absent, two classes served by the same skill count as one source, hardware is always unavailable, and knowledge requires a specific mount path. These are exactly the kind of gotchas an agent needs to interpret results correctly.

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 structured with clear labels (READ, WHEN, RETURNS, GOTCHAS) and is front-loaded with its core purpose. Every section adds necessary decision-making or behavioral detail, and no sentence is filler or redundant.

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?

Given there is no output schema, the RETURNS section sufficiently explains the response shape and semantics, while GOTCHAS covers the edge cases that would otherwise surprise a caller. The tool is simple enough (one optional parameter) and the description fully equips an agent to invoke it 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 schema covers the single parameter 100% with a rich description including spelling variants and the omission behavior. The main description reinforces the unrecognised_skills semantics and adds the 'same skill counts as one source' caveat, but most of the parameter meaning is already in the schema, so the baseline of 3 applies.

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 starts with a precise question: 'What strength of conclusion can this environment reach?' and distinguishes this readiness assessment from the sibling tools by stating it is used before an investigation begins. It clearly identifies the resource (environment conclusion strength) and the analytical output it provides.

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

Usage Guidelines4/5

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

The WHEN clause explicitly states two concrete situations: before starting an investigation, or when a case will not go higher and you need to know if that is fixable. This gives clear contextual guidance, though it does not explicitly name alternative tools or state when not to use it.

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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