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Python
Remote

vsan_capacity

Read-onlyIdempotent

Retrieve vSAN datastore space usage for a cluster, including total, used, free, and usage percentage, to support capacity planning.

Instructions

[READ] Get space usage of a cluster's vSAN datastore for capacity planning.

Returns {cluster_name, vsan_enabled, datastore_name, accessible, total_gb, used_gb, free_gb, usage_pct}. When the vSAN datastore is inaccessible (accessible=false) the four figures are null with a message, not 0 — vCenter answers 0 for a datastore it cannot reach, and 0 GB used reads as a healthy empty datastore. accessible=null means the summary did not say. If vSAN is not enabled on the cluster, returns vsan_enabled=false with an explanatory message rather than an error; errors only if the cluster name is not found. Use vsan_health for disk-group layout and host details; use list_all_datastores for non-vSAN (VMFS/NFS) datastore usage. No side effects.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
targetNoOptional vCenter/ESXi target name from config.
cluster_nameYesCluster name exactly as shown in vCenter. Errors if the cluster is not found.

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed3 schema fields changedv1.8.15
    • addedInput schema / additionalProperties
      Added value: +false
    • addedInput schema / properties / cluster_name / description
      Added value: +"Cluster name exactly as shown in vCenter. Errors if the cluster is not found."
    • addedInput schema / properties / target / description
      Added value: +"Optional vCenter/ESXi target name from config."
  2. First observedv1.3.2

TDQS

A4.9/5.0
Behavior5/5

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

Annotations already declare readOnlyHint, idempotentHint, and destructiveHint=false, but the description adds rich behavioral nuance: inaccessible datastores return null figures instead of 0, accessible=null means the summary was silent, and vSAN-disabled clusters produce an explanatory message. It also explicitly states 'No side effects'.

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 dense but every sentence earns its place: purpose, return shape, edge-case semantics, error behavior, sibling routing, and side-effect confirmation. It is front-loaded with the core purpose and keeps related details grouped logically.

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 description correctly takes on the burden of documenting return fields and edge cases. It covers the expected behavior, error conditions, null semantics, and alternative tools, making it complete for an agent to invoke 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 input schema already documents both parameters, including the exact-match requirement for cluster_name. The description adds valuable behavioral context: it clarifies that cluster_name errors only if not found, while vSAN-disabled clusters return a message instead, and it reinforces the meaning of edge-case return values.

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 begins with a clear verb and resource: 'Get space usage of a cluster's vSAN datastore for capacity planning.' It names the specific output fields and explicitly distinguishes itself from siblings by recommending vsan_health for disk-group details and list_all_datastores for non-vSAN datastores.

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 explicitly states when to use this tool and when to use alternatives: 'Use vsan_health for disk-group layout and host details; use list_all_datastores for non-vSAN (VMFS/NFS) datastore usage.' It also clarifies the error boundary (cluster name not found) and that vSAN-disabled clusters return a message rather than an error.

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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