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generate_config_from_description

Create Ludus cyber range configurations from natural language descriptions, automatically generating YAML files for deployment with clarification requests for ambiguous inputs.

Instructions

Generate a complete Ludus range configuration from natural language description.

This is an enhanced AI-powered version of build_range_from_description that provides:

  • Better natural language understanding beyond simple regex matching

  • Intelligent clarification requests for ambiguous or incomplete inputs

  • Multiple configuration suggestions and alternatives

  • Educational explanations of design decisions and best practices

Args: description: Natural language description of the desired cyber range. Can be conversational and doesn't need to follow strict formatting. include_suggestions: Whether to include enhancement suggestions (default: True) include_clarifications: Whether to request clarifications for missing info (default: True)

Returns: Dictionary containing: - status: "success" or "needs_clarification" - configuration: Complete Ludus YAML configuration dict - metadata: VM count, complexity, resource profile info - parsed_requirements: Structured understanding of the prompt - suggestions: Optional enhancements (if include_suggestions=True) - educational_notes: Explanations of design choices - clarifications: Questions to ask if critical info missing - next_steps: What to do with the generated config

Examples: # Simple Active Directory lab result = await generate_config_from_description( "I need an Active Directory lab with a domain controller " "and 3 Windows 10 workstations for practicing lateral movement" )

# Red team training environment
result = await generate_config_from_description(
    "Build a red team training lab with:
    - AD domain corp.local
    - 2 domain controllers for redundancy
    - 5 workstations (Windows 10 and 11)
    - SQL server with database
    - File server with shares
    - Exchange server for email
    - Kali Linux attacker machine
    - Wazuh SIEM for blue team practice"
)

# Web application security lab
result = await generate_config_from_description(
    "Create a web app pentest lab with Ubuntu web server, "
    "MySQL database, and Kali for attacking. Include Splunk "
    "for monitoring the attacks."
)

# Blue team SOC lab
result = await generate_config_from_description(
    "I want to build a SOC training lab with multiple endpoints "
    "to monitor, a SIEM (preferably Wazuh), and an attacker VM "
    "to simulate threats"
)

Notes: - If the description is ambiguous or missing critical info, status will be "needs_clarification" with specific questions - You can then provide answers and call this function again with more details added to the description - The generated config is ready to deploy with deploy_range() - Consider reviewing suggestions for additional capabilities

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
descriptionYes
include_suggestionsNo
include_clarificationsNo
Behavior4/5

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

With no annotations provided, the description carries full burden and does well. It discloses key behavioral traits: the tool can return 'needs_clarification' status with specific questions, provides educational explanations, multiple suggestions, and clarification requests. It also notes the generated config is ready for deployment with 'deploy_range()' and suggests reviewing enhancements. However, it doesn't mention rate limits, authentication needs, or error handling specifics.

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

Conciseness3/5

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

The description is well-structured with clear sections (Args, Returns, Examples, Notes), but it's lengthy with extensive examples and notes that could be condensed. While informative, some sentences like the detailed example scenarios might be excessive for a concise tool definition, though they do illustrate usage effectively.

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 complexity (AI-powered configuration generation), no annotations, and no output schema, the description does a good job. It explains the return dictionary structure in detail, provides multiple usage examples, and notes integration with 'deploy_range()'. However, it doesn't cover potential limitations or edge cases beyond ambiguity handling, leaving some gaps for a fully comprehensive understanding.

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?

The schema description coverage is 0%, so the description must compensate fully. It provides detailed semantics for all three parameters: 'description' as natural language input that can be conversational, 'include_suggestions' for enhancement suggestions with default True, and 'include_clarifications' for requesting missing info with default True. This adds significant value beyond the bare schema.

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: 'Generate a complete Ludus range configuration from natural language description.' It specifies the verb ('generate'), resource ('Ludus range configuration'), and distinguishes from sibling 'build_range_from_description' by highlighting enhanced AI capabilities like better natural language understanding, clarification requests, and multiple suggestions.

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: for generating configurations from natural language descriptions, especially when users want AI-powered enhancements. It explicitly compares to 'build_range_from_description' as an 'enhanced AI-powered version,' but doesn't specify when NOT to use it or mention other alternatives like 'build_range_from_scratch' or 'build_range_from_skeleton' from the sibling list.

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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