mcp-vmware

by Hokonoken

Overview Schema Source Related Servers Discussions Admin

Python

Hybrid

vmware_list_datastores

Read-onlyIdempotent

Retrieve VMware datastores listing type, capacity, free space, and accessibility for storage management.

Instructions

Lists the datastores: type, capacity, free space, accessibility.

In json: {total, count, ..., datastores:[{name, moid, type, capacity, free, free_pct, accessible, hosts, vms}]}. In markdown: table.

Tool call template

This tool declares no required parameters. Add optional parameters from the input schema as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {},
    "name": "vmware_list_datastores"
  }
}

Input Schema

Name	Required	Description	Default
`limit`	No	Maximum number of results
`offset`	No	Pagination offset
`response_format`	No	markdown (default, compact table) or json (full structure)	markdown

Output Schema

Name	Required	Description	Default
`result`	Yes

Schema Changelog

Changes observed during successful MCP inspections.

First observedv0.1.0Jul 14, 2026

TDQS

B3.2/5.0

Behavior3/5

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

Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, so the safety profile is clear. The description adds output format details (json/markdown structure) but does not disclose other behaviors like pagination limits or response size.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is short (two sentences) and front-loaded. The second sentence is slightly messy with ellipsis but overall efficient. Could be cleaner but effective.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a simple list tool with high schema coverage, annotations, and output structure hints, the description provides adequate context. It clarifies output format and field details, though it does not mention prerequisite connections or scope.

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% for all three parameters. The description does not add significant meaning beyond the schema; it reiterates output structure but provides no new parameter-level semantics.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states that it lists datastores with fields like type, capacity, free space, and accessibility. However, it does not differentiate from sibling listing tools (e.g., list_hosts, list_vms), leaving ambiguity for an AI agent.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

No guidance is provided on when to use this tool versus alternatives such as vmware_list_hosts or vmware_get_cluster_config. The description only explains what the tool does without any when-to-use or when-not-to-use advice.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.