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KubeCraft MCP Server

KubeCraft MCP Server

by cloudcrafttech
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Hybrid

Get Resource Usage

get_resource_usage

Fetch CPU and memory usage for pods in a specified Kubernetes namespace using the metrics API. Requires metrics-server to be installed.

Instructions

Fetch CPU/memory usage for pods via the metrics API (requires metrics-server).

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_resource_usage"
  }
}

Input Schema

NameRequiredDescriptionDefault
namespaceNoNamespace (default: 'default')default

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

A3.8/5.0
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 discloses the dependency on the metrics-server but omits other behavioral traits like rate limits, data freshness, or response structure. The dependency clue is useful but incomplete.

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 sentence, directly conveys the core purpose and a key prerequisite, and is front-loaded with the action and resource. Every word is justified.

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

Completeness4/5

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

For a simple tool with one parameter and no output schema, the description provides enough context: what it returns (CPU/memory usage), the source (metrics API), and a dependency. Minor gap: no mention of return format, but this is standard for such tools.

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 coverage is 100% with a single parameter (namespace) already well-described in the schema. The description adds no extra meaning beyond what the schema provides, so a baseline 3 is appropriate.

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 clearly states the verb 'fetch' and the resource 'CPU/memory usage for pods', and distinguishes from sibling tools like list_pods or describe_resource by specifying the metrics API source.

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?

The description mentions a prerequisite ('requires metrics-server') but does not provide explicit guidance on when to use this tool versus alternatives, nor does it include exclusions or context cues for when not to use it.

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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