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io.github.zw008/vmware-debug

by vmware-skills
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Python

case_record_gap

Record unavailable evidence in a VMware investigation to keep conclusions honest. Specify what was unattainable, why, and the next action to close the gap.

Instructions

[WRITE] Record something the investigation could NOT obtain.

WHEN: any time a fetch failed, was refused, returned nothing, or the data simply does not exist in this environment. This is the tool that keeps a case honest: an unrecorded gap makes it look better supported than it is.

RETURNS: {case_id, gap_id, grade, reasons}.

GOTCHAS: recording a gap does not punish the case for the evidence it does have — a missing confirmation caps the grade, it does not demote it. Writing gaps down is meant to be free.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
whyYesWhy it could not be had — refused, unreachable, collected by nothing in this family, absent from this environment.
whatYesThe observation that could not be obtained — the thing you wanted, not the error you got.
blocksNoHypothesis ids (H1, H2, …) this gap holds up; they must already exist via case_hypotheses. Empty is fine.
case_idYesThe case this gap belongs to (from case_open/case_list).
how_to_closeYesThe next action that would close it, even outside this system ("open a vendor SR"). A gap with no stated next action reads like a to-do and gets skipped.
could_falsifyNoWould OBTAINING this be able to prove the hypothesis WRONG? false (default) is the ordinary case — missing corroboration, capping the case below Confirmed. true means it could overturn the hypothesis, holding it at Candidate.

Schema Changelog

Changes observed during successful MCP inspections.

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

TDQS

A4.5/5.0
Behavior5/5

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

Beyond the annotations' bare flags, the description discloses the write nature with '[WRITE]', the return shape {case_id, gap_id, grade, reasons}, and a key side-effect nuance: a recorded gap caps the grade but does not demote it. This meaningfully helps the agent predict consequences beyond what annotations alone provide. No contradiction with the 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 tightly organized with WHEN, RETURNS, and GOTCHAS sections, front-loading the core action and keeping every line purposeful. It is concise without losing essential behavioral nuance.

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 mutating tool with no output schema, the description supplies the return fields and the critical grade-cap behavior. Combined with the fully documented input schema, an agent has everything needed to invoke the tool correctly and understand its consequences.

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?

Schema description coverage is 100% and each parameter already has rich semantic text, so the baseline is 3. The description adds little parameter-level meaning beyond the schema; its RETURNS line helps with output understanding but not with parameter semantics.

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 opens with a specific verb and resource: 'Record something the investigation could NOT obtain.' This clearly distinguishes it from sibling tools like case_submit_evidence, which record evidence the case does have, and case_grade, which grades the case.

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?

Explicit when-to-use guidance is provided: 'any time a fetch failed, was refused, returned nothing, or the data simply does not exist in this environment.' It does not explicitly name alternatives or when-not-to-use cases, but the context is clear enough to route an agent correctly.

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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