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vmware-skills

io.github.zw008/vmware-debug

case_timeline

Correlate evidence already submitted to a case into a timeline, detect spikes, and rank root-cause hypotheses. Reproducible from the case folder alone, with no new events required.

Instructions

[WRITE] Correlate everything this case has collected — steps 04/05.

WHEN: once evidence is in. Unlike incident_timeline, this takes no events: it reads the payloads already submitted, so the result is reproducible from the case folder alone months later, on a machine with access to nothing.

RETURNS: {event_count, window, binning, classification, spikes, spikes_total, hypotheses, evidence_without_events, evidence_without_events_detail, rejected, note} and writes timeline.md.

GOTCHAS: note distinguishes three states that all show zero events — no evidence submitted at all, evidence that carried none, and a genuinely quiet window — and names which items carried none, with what they held instead. rejected names any row that could not be read, with the evidence item it came from; dropping those silently would shrink the picture the conclusion rests on. Submit a read tool's raw result as payload for its events to reach here — a summary of the result carries no rows, and case_submit_evidence says so at the time.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
top_nNoHow many ranked hypotheses come back (default 5). Spikes are capped separately at 20, true count in 'spikes_total'.
case_idYesThe case whose submitted payloads are correlated (from case_open/case_list). Unlike incident_timeline this takes no events — everything comes from the case folder.
bin_secondsNoTime-bin width in seconds. Omit and it is chosen from event density (ladder 1..86400, finest width still averaging 4 events per bin); `binning` reports which was used.
z_thresholdNoStandard deviations above the mean bin count that mark a spike (default 2.0). Under 3 bins, or a flat series, yields none at any threshold.

Schema Changelog

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

  1. Addedv1.11.1

TDQS

A4.7/5.0
Behavior5/5

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

The description discloses that this is a write operation producing timeline.md, lists the full return object, and explains subtle behaviors around 'note' and 'rejected' that could otherwise mislead analysis. Annotations here are mostly negative/false, so the description carries the transparency burden and handles it thoroughly.

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?

Although long, the description is deliberately structured with WHEN, RETURNS, and GOTCHAS sections, and every sentence adds operational value. The front-loaded purpose statement and the clear sectioning make the length justified rather than bloated.

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 complex tool with no output schema, the description provides the full return field list, the file side effect, the main failure modes, and the expected input semantics. An agent has enough information to select, invoke, and interpret this tool without further inference.

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%, so the schema already documents all four parameters well. The description adds useful context about reproducibility and the payload requirement, but does not materially expand on individual parameter semantics beyond what the schema states.

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 action (correlate everything this case has collected) and explicitly contrasts itself with incident_timeline, which takes events rather than reading from submitted payloads. This makes the tool's scope and its distinction from siblings immediately clear.

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 gives an explicit WHEN condition ('once evidence is in') and clarifies that unlike incident_timeline, no events should be passed because everything comes from the case folder. It also instructs the agent to submit a read tool's raw result as 'payload', with a warning that summaries carry no rows.

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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