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KubeCraft MCP Server

KubeCraft MCP Server

by cloudcrafttech
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Hybrid

Render GPU Utilization Chart (PNG)

render_gpu_metrics

Render a PNG bar chart showing GPU utilization from Prometheus DCGM metrics to visualize GPU usage in your Kubernetes cluster.

Instructions

Render a PNG bar chart from Prometheus DCGM metric DCGM_FI_DEV_GPU_UTIL. Requires KUBECRAFT_PROMETHEUS_URL and DCGM exporter.

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": "render_gpu_metrics"
  }
}

Input Schema

NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

A3.5/5.0
Behavior2/5

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

No annotations are provided, so the description carries full burden. It mentions prerequisites but does not disclose behavioral traits such as whether it performs live queries, what happens if the metric is unavailable, or any side effects. The agent has limited understanding of tool behavior beyond the basic render action.

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, concise sentence of 13 words. It front-loads the key purpose ('Render a PNG bar chart...') and includes necessary prerequisites without any fluff.

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?

Given no parameters, no output schema, and no annotations, the description is minimal but somewhat adequate. It specifies the metric source and prerequisites, but lacks details such as return format (though PNG is implied), axis labels, failure modes, or performance characteristics. It meets the minimum for a simple tool but has gaps.

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 input schema is empty (zero parameters), so there is nothing to describe. By mentioning the environment variable KUBECRAFT_PROMETHEUS_URL, the description implicitly explains why no parameters are needed, which adds value beyond the empty schema. Baseline for zero parameters is 4, and this context justifies that score.

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 action ('Render'), the output format ('PNG bar chart'), and the specific data source ('Prometheus DCGM metric DCGM_FI_DEV_GPU_UTIL'). It distinguishes from sibling tools like render_gpu_dashboard or render_gpu_sparkline by being specific to a bar chart of GPU utilization.

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?

The description only mentions prerequisites (environment variable and DCGM exporter) but provides no guidance on when to use this tool versus alternatives, nor any conditions or exclusions. The agent is left to infer usage context.

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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