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run_kali_command

Execute commands in a Kali Linux container for penetration testing tasks like network scanning, vulnerability assessment, and security testing.

Instructions

Execute a command inside the Kali Linux container

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
commandYesThe command to execute in Kali container
workdirNoWorking directory for the command (optional)/root
Behavior2/5

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 states the action ('Execute a command') but fails to describe critical traits: whether this is a read-only or destructive operation, what permissions are required, how output is returned, error handling, or execution limits (e.g., timeouts). For a command execution tool in a security context, this lack of transparency is a significant gap.

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 a single, direct sentence with zero wasted words. It front-loads the core action ('Execute a command') and specifies the context ('inside the Kali Linux container') efficiently. Every word earns its place, making it easy to parse and understand at a glance.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness2/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the complexity of executing commands in a Kali container (a security-focused environment with potential for destructive actions), the description is incomplete. No annotations exist to clarify safety or permissions, and there's no output schema to describe return values. The description lacks context on execution environment, constraints, or error cases, leaving critical gaps for an AI agent to navigate.

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

Parameters3/5

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

Schema description coverage is 100%, with clear documentation for both parameters ('command' and 'workdir'). The description adds no additional parameter semantics beyond what the schema provides—it doesn't explain command syntax, security considerations, or valid workdir paths. This meets the baseline score of 3 since the schema adequately covers parameter details.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the verb ('Execute') and resource ('a command inside the Kali Linux container'), making the purpose immediately understandable. It distinguishes itself from siblings like 'install_kali_package' or 'start_kali_container' by focusing on command execution rather than package management or container lifecycle operations. However, it doesn't specify what types of commands are appropriate or the execution context beyond the container.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides no guidance on when to use this tool versus alternatives. It doesn't mention prerequisites (e.g., whether the container must be running), exclusions (e.g., unsafe commands), or relationships to siblings like 'kali_container_status' for checking container state. The agent must infer usage from the tool name alone, which is insufficient for informed selection.

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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