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ros2-inspector

ros2-inspector

by XuChen-AI
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Local

machine_readiness

Assess if this machine can install or run ROS 2. It checks OS, hardware, network, and environment, then returns a readiness verdict with improvement hints.

Instructions

评估本机是否适合安装/运行 ROS2。无需安装 ROS 即可运行（纯 Python，零子进程）。

何时用：准备在这台机器上安装 ROS / 部署机器人软件之前的体检。 返回：操作系统与版本、内核、架构、CPU/内存/磁盘概况、网络接口、 主机名解析、locale、ROS_DOMAIN_ID，并对照官方支持矩阵给出参考结论 （verdict 字段）与改进建议（hints 列表）。

Tool call template

This tool declares no required parameters. Add optional parameters from the input schema as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {},
    "name": "machine_readiness"
  }
}

Input Schema

NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.1

TDQS

A4.1/5.0
Behavior4/5

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

With no annotations, the description carries the burden and does add real behavioral context: it runs without ROS installed, is pure Python with zero subprocesses, and produces a verdict plus hints. It does not explicitly state that it is read-only or disclose runtime cost, but the self-contained/no-subprocess note is genuinely useful for an agent deciding whether it is safe to call.

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?

Front-loads the purpose, then uses labeled 'when to use' and 'returns' blocks. The return enumeration is somewhat long but each item is informative rather than filler, so the structure earns its length.

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

Completeness5/5

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

With no output schema available, the description compensates by enumerating the returned fields (OS/kernel/arch, CPU/mem/disk, network, hostname resolution, locale, ROS_DOMAIN_ID) and calling out the verdict and hints outputs. An agent knows both what the tool does and what it will get back.

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

Parameters4/5

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

The tool takes zero parameters, so the baseline is 4; there is no parameter surface for the description to clarify.

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?

States a specific verb+resource: evaluating whether the local machine can install/run ROS2, with the added scoping detail that it is a pre-install health check. It implicitly separates itself from siblings like system_overview and health_check via the ROS support-matrix comparison, but never names those siblings directly.

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?

Gives an explicit usage context ('体检 before installing ROS / deploying robot software'), which tells the agent when to reach for this tool. It stops short of naming alternatives or stating when NOT to use it, so it is clear context without exclusions.

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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