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MCP-Connect — Kali Agent MCP v2

by asarlashmit

docker_compose

Execute Docker Compose actions (up, down, ps) on Kali Linux with configurable timing, timeout, and polling for reliable service orchestration.

Instructions

Kali Agent MCP tool: docker_compose Explicit execution timing is supported. Before calling, deliberately choose expected_runtime_seconds, timeout_seconds, check_after_seconds, poll_interval_seconds, and on_timeout. Use on_timeout='continue_background' for long work that should return a durable job_id for later job_status/job_logs/job_wait checks; use 'kill' or 'return_partial' for bounded synchronous work.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
pathYes
buildNo
actionNops
detachNo
servicesNo
on_timeoutNoreturn_partial
timeout_secondsNo
check_after_secondsNo
poll_interval_secondsNo
expected_runtime_secondsNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Behavior2/5

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

No annotations are provided, so the description must fully disclose behavior. It only covers timing behavior (expected_runtime, timeout, polling) but misses critical aspects: that it executes docker-compose commands, requirements for Docker availability, side effects like building images or starting containers, and 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.

Conciseness3/5

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

The description is moderately concise but contains redundancy ('Kali Agent MCP tool'). It front-loads the tool name but then dives into timing details without a clear structure. Could be streamlined.

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

Completeness1/5

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

With 10 parameters and only 1 required, the tool is complex. The description fails to explain the fundamental operation (docker-compose action), how to specify the compose file (path), or what actions the tool supports (default 'ps'). Even with an output schema, the description is severely incomplete for an AI agent to use effectively.

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

Parameters2/5

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

Schema description coverage is 0%, yet the description only explains timing parameters (on_timeout, timeout_seconds, etc.) without adding meaning to the docker-specific parameters (path, action, build, detach, services). The core parameters are left entirely to the schema, which minimally explains them.

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

Purpose2/5

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

The description begins with 'Kali Agent MCP tool: docker_compose' but never states what the tool actually does (e.g., manage Docker Compose deployments). It focuses entirely on timing parameters, leaving the core purpose vague. Sibling tools like docker_run and docker_compose are distinct, but no differentiation is provided.

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

Usage Guidelines3/5

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

Provides guidance on when to use 'continue_background' vs 'kill'/'return_partial' for on_timeout, which is useful. However, it fails to give any context on when to use docker_compose instead of sibling Docker tools (docker_build, docker_run, etc.), leaving the agent without selection criteria.

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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