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build_range_from_description

Creates custom cyber range configurations from natural language descriptions by automatically generating VMs, network rules, and SIEM monitoring for security testing environments.

Instructions

Build a custom range configuration from a natural language description.

This tool intelligently parses your description and automatically builds a complete range configuration with appropriate VMs, network rules, and SIEM.

Args: description: Natural language description of the desired range/scenario siem_type: SIEM type to include (wazuh, splunk, elastic, security-onion, none) resource_profile: Resource allocation profile (minimal, recommended, maximum) include_siem: Whether to include SIEM monitoring

Returns: Complete range configuration ready for deployment

Examples: # Simple AD lab "Create an Active Directory lab with 2 workstations and a file server"

# Red team lab
"Build a red team lab with a domain controller, 3 workstations, SQL server, and Kali attacker"

# Web application lab
"Create a web application testing lab with a web server, database, and Kali attacker"

# Complex enterprise
"Build an enterprise environment with AD domain corp.local, 5 workstations, file server, Exchange server, and Wazuh monitoring"

The tool automatically: - Detects AD/domain requirements and adds domain controller - Adds appropriate number of workstations - Adds servers based on keywords (file server, SQL, web, Exchange) - Adds Kali attacker if mentioned - Configures network rules for attacker access - Adds SIEM monitoring if requested

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
descriptionYes
siem_typeNowazuh
resource_profileNorecommended
include_siemNo
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 of behavioral disclosure. It effectively describes what the tool does: parsing descriptions, building configurations with VMs, network rules, and SIEM, and automating tasks like detecting requirements and adding components. It covers key behaviors such as automatic detection of AD/domain needs and SIEM inclusion, though it lacks details on permissions, rate limits, or error handling.

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 well-structured and appropriately sized, starting with a clear purpose, followed by sections for args, returns, examples, and automation details. Each sentence adds value, such as explaining parameter roles and providing illustrative examples. It could be slightly more concise by integrating some details, but overall, it is front-loaded and efficient.

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 complexity of a 4-parameter tool with no annotations and no output schema, the description is mostly complete. It explains the tool's purpose, usage, parameters, and automation behaviors, and includes examples to guide input. However, it lacks details on the return format (e.g., structure of the 'complete range configuration') and potential limitations or errors, which would enhance completeness.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters5/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 0%, so the description must compensate. It adds significant meaning beyond the schema: it explains that 'description' is a natural language input with examples, 'siem_type' specifies SIEM types like 'wazuh', 'resource_profile' defines allocation levels, and 'include_siem' controls monitoring inclusion. This clarifies parameter purposes and usage, fully compensating for the lack of schema descriptions.

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 tool's purpose: 'Build a custom range configuration from a natural language description.' It specifies the verb 'build' and resource 'range configuration,' distinguishing it from siblings like 'build_range_from_prompt' or 'build_range_from_scratch' by emphasizing natural language input. The examples further illustrate its unique capability to interpret descriptive requests.

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 when to use this tool: when the user has a natural language description of a desired range/scenario. It implicitly distinguishes from siblings by focusing on automated parsing from descriptions, though it does not explicitly state when not to use it or name specific alternatives like 'build_range_from_scratch' for manual configuration.

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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