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list_range_skeletons

Browse and select pre-configured cyber range templates for security training and testing. Provides ready-to-deploy lab environments including AD setups, red team scenarios, SOC training, web pentest targets, and malware analysis labs.

Instructions

List all available range skeleton templates.

Returns a dictionary of complete range configurations for common scenarios. These provide fully-configured lab environments ready for deployment.

Available range skeletons:

  • basic-ad: Basic AD lab (1 DC, workstations, optional attacker/SIEM)

  • enterprise-ad: Enterprise AD with CA, servers, multiple workstations

  • red-team: Red team training with DMZ, AD, and network segmentation

  • soc-training: SOC analyst training with SIEM and monitored endpoints

  • web-pentest: Web app pentest with DVWA, Juice Shop, WebGoat

  • malware-analysis: Isolated malware RE lab with FlareVM and REMnux

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

Example: ranges = await list_range_skeletons() # Returns: {"basic-ad": "Basic AD lab...", ...}

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. It discloses that the tool returns a dictionary with skeleton names as keys and descriptions as values, and includes an example of the return format. It also lists specific skeleton types (e.g., 'basic-ad', 'enterprise-ad'), which adds context about available options. However, it does not mention potential side effects, rate limits, or authentication needs, leaving some behavioral aspects uncovered.

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 well-structured and front-loaded, starting with the core purpose, followed by details on returns and examples. Every sentence adds value: the first states the action, the second explains the output, the third lists available skeletons, and the fourth clarifies the return format with an example. There is no wasted text, and it efficiently conveys necessary information.

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?

Given the tool's low complexity (0 parameters, no annotations, no output schema), the description is largely complete. It explains what the tool does, what it returns, and provides an example. However, it could be more complete by explicitly stating that this is a read-only operation with no side effects, which is implied but not confirmed without annotations. The lack of output schema is compensated by the detailed return description.

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 tool has 0 parameters, and schema description coverage is 100% (empty schema). The description does not need to add parameter semantics, but it appropriately notes there are no inputs by not discussing parameters. Since there are no parameters to document, a baseline of 4 is applied as the description focuses on output and usage without unnecessary parameter details.

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 verb 'List' and the resource 'range skeleton templates', specifying it returns 'complete range configurations for common scenarios' and 'fully-configured lab environments ready for deployment'. It distinguishes from siblings like 'list_range_templates' or 'list_vm_skeletons' by focusing specifically on pre-configured skeleton templates rather than general templates or VM 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 implies usage by listing available skeletons for deployment scenarios, but does not explicitly state when to use this tool versus alternatives like 'build_range_from_skeleton' or 'get_range_skeleton'. It provides clear context about what the skeletons are for (e.g., 'common scenarios', 'lab environments'), but lacks explicit exclusions or comparisons to sibling tools.

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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