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list_vm_skeletons

Retrieve available VM skeleton templates to use as starting points for building custom virtual machines. Templates include domain controllers, workstations, servers, attacker VMs, monitoring systems, and vulnerable applications.

Instructions

List all available VM skeleton templates.

Returns a dictionary of VM skeleton names and their descriptions. These skeletons can be used as starting points for building custom VMs.

Categories include:

  • Domain Controllers (dc-2022, dc-2019, secondary-dc)

  • Workstations (ws-win11, ws-win10)

  • Windows Servers (file-server, sql-server, exchange, web-iis, ca)

  • Linux Servers (ubuntu, debian, rocky, docker)

  • Attacker VMs (kali, parrot, commando)

  • SIEM/Monitoring (wazuh, splunk, elastic, security-onion)

  • Vulnerable Apps (dvwa, juice-shop, metasploitable, vulnhub)

Returns: Dictionary with skeleton names as keys and descriptions as values

Example: skeletons = await list_vm_skeletons() # Returns: {"dc-2022": "Windows Server 2022 Domain Controller", ...}

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Behavior4/5

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

With no annotations provided, the description carries the full burden and effectively discloses key behaviors: it's a read-only operation (implied by 'List'), returns a dictionary structure, and includes example output. However, it lacks details on error handling, rate limits, or authentication requirements.

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 efficiently structured: it states the purpose upfront, explains the output format, provides usage context with categorized examples, and includes a clear return example. Every sentence adds value without redundancy.

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 zero-parameter read tool with no annotations or output schema, the description is complete: it explains what the tool does, what it returns (including structure and examples), and how the output can be used, covering all necessary context for an agent to invoke it 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 has 0 parameters with 100% coverage, so no parameter documentation is needed. The description appropriately focuses on output semantics, adding value by explaining the return structure and providing an example, exceeding the baseline of 3 for high schema coverage.

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 clearly states the specific action ('List all available VM skeleton templates') and resource ('VM skeleton templates'), distinguishing it from siblings like 'list_range_skeletons' or 'get_vm_skeleton' by focusing on templates rather than deployed ranges or individual skeletons.

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

Usage Guidelines4/5

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

The description provides clear context for usage ('These skeletons can be used as starting points for building custom VMs'), but does not explicitly state when not to use it or name alternatives like 'list_range_skeletons' or 'get_vm_skeleton' for different contexts.

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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