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vmware-skills

io.github.zw008/vmware-debug

by vmware-skills
OverviewSchemaRelated ServersScoreDiscussions
Python

incident_timeline

Read-onlyIdempotent

Correlate fetched VMware events into a unified timeline to identify spikes, rank root-cause hypotheses, and suggest next diagnostic steps.

Instructions

[READ] Correlate already-fetched VMware events into one incident view.

WHEN: use this after you've pulled events for an incident from the data-source skills (vmware-monitor get_events/get_alarms, vmware-aria list_alerts/list_anomalies, vmware-log-insight log_search/log_aggregate, vmware-nsx) — feed them here to find what correlates and where to look next. Not sure which events to pull? Run list_symptom_categories first. This tool does NOT fetch anything itself.

RETURNS: {event_count, window, binning, classification, spikes (strongest anomalous bins), spikes_total, hypotheses (ranked root-cause candidates, each with a suggested_check), next_checks (which skill/tool to run next)}. Read binning for the resolution you were given, and classification for how much of the stream matched nothing — the ranking describes only the part that did.

GOTCHAS: read-only, stateless, no network — nothing is executed. Remediation routes to vmware-aiops (single fix) or vmware-pilot (multi-step). A malformed event returns {error, hint} naming the offending index.

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'.
eventsYesEvent envelopes, each {ts, source, severity, entity, text, fields}. ts may be ISO-8601, epoch seconds or epoch millis and is required; severity is normalised onto critical/error/warning/ info/unknown, so vendor spellings (fatal, red, warn, yellow, notice, green) are accepted. An entry that cannot be normalised is refused with its index, not skipped.
bin_secondsNoTime-bin width in seconds. Omit and it is chosen from event density off the ladder 1/10/60/300/900/3600/21600/86400, taking the finest width still averaging 4 events per bin.
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 — empty spikes is not "calm".

Schema Changelog

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

  1. Changed5 schema fields changedv1.11.1
    • addedInput schema / additionalProperties
      Added value: +false
    • addedInput schema / properties / bin_seconds / description
      Added value: +"Time-bin width in seconds. Omit and it is chosen from event density off the ladder 1/10/60/300/900/3600/21600/86400, taking the finest width still averaging 4 events per bin."
    • addedInput schema / properties / events / description
      Added value: +"Event envelopes, each {ts, source, severity, entity, text, fields}. ts may be ISO-8601, epoch seconds or epoch millis and is required; severity is normalised onto critical/error/warning/ info/unknown, so vendor spellings (fatal, red, warn, yellow, notice, green) are accepted. An entry that cannot be normalised is refused with its index, not skipped."
    • addedInput schema / properties / top_n / description
      Added value: +"How many ranked hypotheses come back (default 5). Spikes are capped separately at 20, true count in 'spikes_total'."
    • addedInput schema / properties / z_threshold / description
      Added value: +"Standard 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 — empty spikes is not \"calm\"."
  2. First observedv1.8.8

TDQS

A4.7/5.0
Behavior5/5

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

Annotations already declare readOnlyHint, idempotentHint, and destructiveHint, but the description adds valuable context: 'read-only, stateless, no network — nothing is executed,' malformed-event error behavior, and remediation routing. It goes well beyond the annotations without contradicting them.

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 into [READ], WHEN, RETURNS, and GOTCHAS sections, front-loading the core purpose and making every section skimmable. Even though it is longer than average, each sentence carries distinct operational information with no filler.

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?

There is no output schema, so the detailed RETURNS section is essential and covers the full return shape, including spikes, hypotheses, next_checks, and caveats like reading binning and classification. Combined with schema, annotations, and edge-case handling, nothing an agent needs to invoke and interpret this tool is missing.

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 (events, bin_seconds, z_threshold, top_n) already has a thorough schema description including defaults and edge-case behavior. The tool description adds no additional parameter-level meaning beyond what the schema provides, 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 opens with a specific verb and resource: 'Correlate already-fetched VMware events into one incident view.' It clearly distinguishes this tool from data-fetching skills by stating 'This tool does NOT fetch anything itself,' and the scope is unambiguous.

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?

The WHEN section explicitly instructs when to use the tool (after pulling events from named data-source skills), what to do if unsure which events to pull (run list_symptom_categories first), and what the tool does not do. This is strong, actionable 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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