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SapperMCP

SapperMCP

by MasterX1582
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Local

Get Cpu Usage

get_cpu_usage

Measure CPU usage percentage over a configurable sampling interval, with optional per-core results for diagnosing system load.

Instructions

CPU usage percent (samples over interval seconds).

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": "get_cpu_usage"
  }
}

Input Schema

NameRequiredDescriptionDefault
intervalNo
per_coreNo

Output Schema

NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

B3/5.0
Behavior3/5

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

With no annotations, the description carries the full burden. It does add one genuinely useful behavioral fact: the value is sampled over `interval` seconds, implying a measurement window rather than an instant read, and it states the unit (percent). It omits whether the call blocks for the interval duration, what permissions are needed, and how per-core results are shaped.

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?

A single front-loaded fragment with no filler; the unit and sampling window are stated immediately. It is terse to the point of being a fragment, but nothing is wasted.

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

Completeness3/5

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

An output schema exists, so return values need not be explained, and the sampling behavior is covered. However, for a two-parameter tool with zero schema coverage, the complete absence of any mention of `per_core` leaves a real gap in what an agent needs to call it correctly.

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%, so the description must compensate. It meaningfully explains `interval` as a sampling window, which the bare number type does not convey, but it never mentions `per_core`, leaving that parameter's effect on output entirely undocumented in both schema and prose.

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 names a specific resource and metric (CPU usage percent) with sampling semantics, which unambiguously separates it from siblings like get_memory_info or get_disk_usage. It doesn't explicitly distinguish itself from those siblings in text, but the resource name alone is decisive enough for selection.

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?

There is no when-to-use or when-not-to-use guidance, and no reference to alternatives such as get_hardware_info or get_process_info for related CPU questions. The agent must infer the calling context entirely from the tool name.

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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