Skip to main content
Glama

create_custom_range_from_scenario

Customize existing cyber range scenarios by adding or removing virtual machines, adjusting specifications, and modifying network rules for tailored security testing environments.

Instructions

Create a custom range configuration based on an existing scenario.

This tool takes an existing scenario and allows you to customize it with your own modifications (add/remove VMs, change specs, modify network rules).

Args: scenario_key: Base scenario to start from (e.g., "ad-basic", "web-basic", "kerberoasting") customizations: Dictionary of customizations to apply siem_type: SIEM type to use ("wazuh", "splunk", "elastic", "security-onion", "none")

Returns: Customized range configuration ready for deployment

Customization options: - add_vms: List of VM configurations to add - remove_vms: List of VM names to remove - modify_vms: Dictionary of VM name to modifications - add_network_rules: List of network rules to add - modify_range_settings: Range-level settings to modify

Example: # Start with basic AD scenario and customize customizations = { "add_vms": [{ "vm_name": "my-web-server", "hostname": "webserver01", "template": "ubuntu-22.04-template", "vlan": 10, "ip_last_octet": 50, "ram_gb": 4, "cpus": 2 }], "modify_vms": { "ad-dc-win2022-server-x64": { "ram_gb": 16, # Increase RAM "cpus": 8 # Increase CPUs } }, "add_network_rules": [{ "name": "Allow web traffic", "vlan_src": 99, "vlan_dst": 10, "protocol": "tcp", "ports": 80, "action": "ACCEPT" }] } result = await create_custom_range_from_scenario( scenario_key="ad-basic", customizations=customizations, siem_type="wazuh" )

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
scenario_keyYes
customizationsYes
siem_typeNowazuh
Behavior3/5

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 describes the tool's purpose and customization capabilities well, but doesn't mention important behavioral aspects like whether this creates a new range configuration or modifies an existing one in-place, what permissions are required, or any rate limits. The example shows it returns a 'Customized range configuration ready for deployment' which helps, but more behavioral context would be valuable.

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 with clear sections (purpose, args, returns, customization options, example) and front-loads the core purpose. The example is comprehensive but necessary given the complexity. Some sentences could be more concise, but overall the structure is effective and information-dense without unnecessary fluff.

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 (3 parameters with nested objects, no annotations, no output schema), the description does an excellent job providing context. It explains the purpose, parameters, return value, and provides a detailed example. The main gap is lack of output schema details, but the description states what's returned ('Customized range configuration ready for deployment'). For a mutation tool with no annotations, this is quite comprehensive.

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?

With 0% schema description coverage, the description fully compensates by providing detailed parameter information. It explains all three parameters: scenario_key (base scenario to start from), customizations (dictionary of customizations), and siem_type (SIEM type to use). It even provides a comprehensive example showing exactly how to structure the customizations parameter with specific fields and values.

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: 'Create a custom range configuration based on an existing scenario' with specific customization actions (add/remove VMs, change specs, modify network rules). It distinguishes from siblings like 'build_range_from_scratch' or 'clone_range' by emphasizing modification of an existing scenario rather than building from scratch or simple cloning.

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 you want to customize an existing scenario with modifications. It implies usage by stating 'based on an existing scenario' and listing customization options. However, it doesn't explicitly state when NOT to use it or name specific alternatives like 'build_range_from_scratch' for starting from scratch.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Install Server

Other Tools

Latest Blog Posts

MCP directory API

We provide all the information about MCP servers via our MCP API.

curl -X GET 'https://glama.ai/api/mcp/v1/servers/tjnull/Ludus-FastMCP'

If you have feedback or need assistance with the MCP directory API, please join our Discord server