KubeCraft MCP Server

Render GPU Metrics Sparkline (PNG)

render_gpu_sparkline

Generates a PNG sparkline from a Prometheus range query, defaulting to GPU utilization for the last hour, for Kubernetes cluster monitoring.

Instructions

PNG sparkline from a Prometheus range query (default DCGM_FI_DEV_GPU_UTIL, last hour). Requires KUBECRAFT_PROMETHEUS_URL.

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_sparkline"
  }
}

Input Schema

Name	Required	Description	Default
`step`	No	Range step (default 15s)	15s
`query`	No	PromQL for range query (default: DCGM_FI_DEV_GPU_UTIL)
`duration_seconds`	No	Window length in seconds (default 3600, max 86400)

Schema Changelog

Changes observed during successful MCP inspections.

First observedv0.1.0Jul 13, 2026

TDQS

A4/5.0

Behavior4/5

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 tool produces a PNG image and requires a specific environment variable. This adds value beyond the schema, alerting the agent to a dependency. However, it does not mention possible errors or rate limits.

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, front-loaded sentence that efficiently conveys purpose and a key requirement. Every word earns its place, with no redundancy.

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?

Given the tool has 3 parameters with full schema coverage, no output schema, and no annotations, the description adequately covers the core functionality and a critical dependency. It could be more complete by explicitly stating the output format (PNG) and that it is read-only.

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 description coverage is 100%, so baseline is 3. The description mentions default metric and 'last hour' but does not add new meaning beyond the schema defaults. It does not explain the query parameter further or provide PromQL examples.

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?

Description clearly states it renders a PNG sparkline from a Prometheus query, specifying default metric (DCGM_FI_DEV_GPU_UTIL) and time window (last hour). This distinguishes it from sibling tools like render_gpu_metrics and render_gpu_dashboard, which likely produce more detailed visualizations.

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 required environment variable KUBECRAFT_PROMETHEUS_URL, which is a prerequisite. However, it does not provide guidance on when to use this tool over alternatives like render_gpu_metrics or render_gpu_dashboard, nor does it specify 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.

Deploy Server