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Enrique-S-J

sysops-mcp

by Enrique-S-J

get_host_summary

Read-only

Get an instant host health snapshot covering hostname, uptime, CPU, RAM, swap, and disk usage. Identify system issues at a glance and decide which follow-up checks are needed.

Instructions

One-call health snapshot: hostname, uptime, CPU, RAM, swap, fullest disk.

The right first tool to call when asked "how is this box doing?" — it aggregates the key signals and gives a plain-language assessment, so follow-up tools only need to be called where something looks off.

Returns: str: Markdown summary or JSON object with keys: hostname, boot_time, uptime_hours, cpu_percent, memory_percent, swap_percent, max_disk_percent, assessment.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
response_formatNo'markdown' for human-readable output, 'json' for structured data.markdown

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. First observedv0.1.0

TDQS

A4.9/5.0
Behavior5/5

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

Annotations already declare readOnlyHint=true and destructiveHint=false, so the safety profile is covered. The description adds meaningful behavioral context: it aggregates key signals, produces a plain-language assessment, and explains the returned format and keys, which is exactly the kind of beyond-annotation disclosure that helps an agent.

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 efficient and well-structured: the core value is front-loaded, the usage guidance is in a short paragraph, and the Returns section is compact but informative. No sentence is wasted.

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?

For a read-only tool with one optional parameter, the description fully covers what it does, when to use it, what the output looks like, and how it relates to sibling tools. The annotations cover safety, and the schema covers the parameter, so nothing essential is missing.

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?

Schema coverage is 100% and the schema already documents response_format with an enum and description. The description adds value by tying the parameter to concrete outputs: 'Markdown summary or JSON object with keys: hostname, boot_time, uptime_hours, ...' which clarifies what the caller will receive.

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 opens with a clear verb and resource: 'One-call health snapshot' listing the exact signals (hostname, uptime, CPU, RAM, swap, fullest disk). It also distinguishes itself from siblings by framing itself as the aggregated first tool, so an agent can tell it apart from get_cpu_status, get_memory_status, etc.

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

Usage Guidelines5/5

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

The description explicitly says it is 'the right first tool to call when asked how is this box doing?' and instructs that follow-up tools should only be called 'where something looks off.' This provides clear when-to-use guidance and implicitly names the alternative follow-up tools.

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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