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explain_range_design_decisions

Explains design rationale and best practices for cyber range configurations to help users understand why specific architectural choices are made for security testing scenarios.

Instructions

Explain the design decisions and best practices for a range configuration request.

This tool helps users understand WHY certain choices are made when building a cyber range, providing educational value beyond just generating configs.

Args: prompt: The range description or scenario you want explained

Returns: Dictionary with: - design_rationale: Why specific VMs/networks are suggested - best_practices: Industry best practices applied - learning_objectives: What skills can be practiced - alternative_approaches: Other ways to achieve similar goals - security_considerations: Security implications of design choices

Examples: # Understand AD design result = await explain_range_design_decisions( "Why do I need a domain controller AND workstations " "for an AD lab?" )

# Network segmentation rationale
result = await explain_range_design_decisions(
    "Explain why the attacker VM should be on a separate VLAN"
)

# SIEM placement
result = await explain_range_design_decisions(
    "Where should I place the SIEM server and why?"
)

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
promptYes
Behavior3/5

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

No annotations are provided, so the description carries the full burden. It describes the tool as explanatory and educational, which implies it's a read-only, non-destructive operation. However, it lacks details on behavioral traits such as rate limits, authentication needs, or response format beyond the return dictionary structure. The description adds some context (educational focus) but does not fully compensate for the absence of annotations, leaving gaps in behavioral disclosure.

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 educational value, args, returns, and examples. Each sentence earns its place by clarifying usage, parameters, or outputs without redundancy. It is appropriately sized for a tool with one parameter and detailed return expectations, avoiding unnecessary verbosity.

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 (explanatory with one parameter) and lack of annotations or output schema, the description is mostly complete. It covers purpose, usage, parameter semantics, and return structure in detail. However, it does not specify output format (e.g., JSON structure) or potential errors, which could be useful given no output schema. The examples enhance completeness but slight gaps remain in full behavioral context.

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 input schema has 0% description coverage (parameter 'prompt' is undocumented in schema), but the description compensates fully. It defines 'prompt' as 'The range description or scenario you want explained' and provides three concrete examples (e.g., 'Why do I need a domain controller AND workstations for an AD lab?'), adding clear meaning beyond the bare schema. This effectively addresses the low schema coverage with detailed semantic context.

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 explicitly states the tool's purpose: 'Explain the design decisions and best practices for a range configuration request' and clarifies it 'helps users understand WHY certain choices are made when building a cyber range, providing educational value beyond just generating configs.' This is a specific verb ('explain') + resource ('design decisions and best practices') that clearly distinguishes it from sibling tools focused on building, deploying, or managing ranges (e.g., build_range_from_description, deploy_range, get_range_config).

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 the tool: to understand 'why' choices are made in range design, offering 'educational value beyond just generating configs.' It implies usage for learning or rationale explanation rather than operational tasks. However, it does not explicitly state when NOT to use it (e.g., for actual configuration generation) or name specific alternatives among siblings, though the distinction from tools like generate_config_from_description is implied.

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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