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Python
Hybrid

cross_vcenter_attention

Read-onlyIdempotent

Aggregate cluster-health summaries from all vCenters into one ranked top-issues list. Identify what needs attention now, with each issue tagged by vCenter for direct drill-down.

Instructions

[READ] "What needs attention now?" across EVERY configured vCenter — one list.

Start here when the estate has more than one vCenter. Rolls every configured target's cluster-health summary into one globally ranked top_issues list (worst first, each tagged with its vcenter) plus a per-target rollup. Use this instead of calling cluster_health_summary once per target and merging yourself. Returns a rollup, not the list envelope; lead with top_issues.

Degrades gracefully: an unreachable target is listed under unreachable with a reason and the rest still aggregate. Point-in-time — no trending. Overlap is removed by identity: a host reached through a vCenter and as its own ESXi target counts once (hardware UUID), a datastore seen through both is one issue with the other view's figure under also_seen_via (datastore URL), and totals separates vcenters, esxi_targets and unidentified_targets. Then drill in with vm_investigation_bundle, host_investigation_bundle or datastore_investigation_bundle against the vcenter the issue names.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
top_nNoCap the merged top_issues list (default 10); ``issues_total`` is the pre-cap count.
cluster_filterNoCase-insensitive cluster substring applied to every target (None = all clusters).

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed3 schema fields changedv1.9.2
    • addedInput schema / additionalProperties
      Added value: +false
    • addedInput schema / properties / cluster_filter / description
      Added value: +"Case-insensitive cluster substring applied to every target (None = all clusters)."
    • addedInput schema / properties / top_n / description
      Added value: +"Cap the merged top_issues list (default 10); ``issues_total`` is the pre-cap count."
  2. Addedv1.7.6

TDQS

A4.7/5.0
Behavior5/5

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

Despite annotations already declaring readOnlyHint and idempotentHint, the description goes far beyond them by disclosing distinctive behaviors: graceful degradation for unreachable targets, point-in-time semantics (no trending), deduplication identity rules, and the rollup return shape (lead with top_issues). This is substantial added context beyond what annotations provide.

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 long but every sentence carries operational value: scope, return shape, degradation behavior, deduplication rules, and follow-up tools. It is front-loaded with the top-line purpose and alternatives, making the key decision information immediately visible.

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 tool with no output schema, the description compensates thoroughly: it names the primary output fields (top_issues, unreachable, also_seen_via, totals), explains scope and exclusions (no trending, one vCenter tag), and routes to sibling investigation tools. No critical behavioral or return-shape information seems 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?

The input schema already documents both parameters with full coverage, including defaults and filtering semantics. The description adds no parameter-level meaning beyond what the schema already states, 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?

Opens with an explicit READ verb and a clear resource: "What needs attention now? across EVERY configured vCenter — one list." It distinguishes itself from cluster_health_summary by stating it aggregates all targets into a single ranked list, so an agent can immediately tell this is the estate-wide aggregate.

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 description gives explicit when-to-use instructions: "Start here when the estate has more than one vCenter" and directs the agent away from the alternative: "Use this instead of calling cluster_health_summary once per target and merging yourself." It also points to drill-in tools (vm_investigation_bundle, host_investigation_bundle, datastore_investigation_bundle), which is 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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