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io.github.zw008/vmware-debug

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Python

case_knowledge

Read-onlyIdempotent

Determine which knowledge formats are accepted and verify whether mounted entries apply decisively to a VMware case, based on version applicability rather than similarity.

Instructions

[READ] What the knowledge layer accepts, and what is mounted.

WHEN: when someone asks what can be added to make conclusions stronger, or when a case will not reach Confirmed and you need to say why in terms they can act on. This is the answer to "which knowledge formats do you take" — the first question anyone mounting a library asks.

RETURNS: {root, sections, entries, with_applies_to, by_source, unreadable, unsupported, formats, needs_conversion, note} — plus {applicable, decisive_here} when a case_id is given.

formats lists every extension read and how each carries its metadata: Markdown front-matter, YAML/JSON whole-file, JSONL per line, CSV/TSV per row, and plain text with a sibling .yaml. needs_conversion names the ones that must become Markdown first (PDF, DOCX, PPTX, HTML).

GOTCHAS: an entry is decisive ONLY if its applies_to block was checked against the case scope and passed — matching is by version applicability, never by similarity, because an entry written for the wrong build reads exactly like the right one. An entry with no applies_to can support a hypothesis but can never make a case Confirmed. A constraint the case scope cannot answer is not a match either: silence is not a pass.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
case_idNoOmit to describe the knowledge layer itself — what formats are read and what is mounted. Pass a case id and every mounted entry is additionally version-checked against THAT case's product_versions, adding `applicable` and `decisive_here` with the reason each entry did or did not qualify.

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed1 schema field changedv1.13.0
    • changedInput schema / title
      Previous value: -"_case_knowledge_implArguments"New value: +"case_knowledgeArguments"
  2. Addedv1.11.1

TDQS

A4.7/5.0
Behavior5/5

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

Beyond the readOnly/idempotent annotations, the description exposes subtle behavior: matching is by version applicability, never similarity; an entry without applies_to can support but never Confirm; and unanswered constraints are not matches ('silence is not a pass'). These gotchas materially change how an agent should interpret results.

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 organized with [READ], WHEN, RETURNS, and GOTCHAS sections, front-loads the most important facts, and each sentence contributes either usage context, return shape, or an operational caveat. It is dense but not padded.

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 compensates by listing return fields, format categories, conversion needs, and caveats about decisiveness. It also explains the optional case_id behavior and when the extra fields appear, making the tool safe to call correctly.

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 schema already documents case_id at 100% coverage, but the description reinforces the effect of passing it and adds the decisive-here criteria and no-silence rule. This adds practical meaning beyond the schema's version-check wording.

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 identifies the tool as the read-only view of the knowledge layer: 'What the knowledge layer accepts, and what is mounted.' It distinguishes case_knowledge from case tools like case_get or case_hypotheses by focusing on knowledge formats, mounting, and applicability rather than incident or case state.

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?

It gives an explicit WHEN: use it when someone asks what can be added to strengthen conclusions, when explaining why a case cannot reach Confirmed, or when mounting a knowledge library. It does not explicitly name sibling tools as alternatives or state when not to use it, so it falls just short of full routing guidance.

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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