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KubeCraft MCP Server

KubeCraft MCP Server

by cloudcrafttech
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Hybrid

Render GPU Allocation Dashboard (PNG)

render_gpu_dashboard

Render a PNG dashboard showing GPU resource allocation per node, comparing allocatable resources to pod requests. Helps identify GPU utilization and potential overcommitment.

Instructions

Render a PNG dashboard of GPU extended resources per node (allocatable) vs sum of pod requests. Requires nodes with GPU resources or workloads requesting GPUs.

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_dashboard"
  }
}

Input Schema

NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

A3.9/5.0
Behavior3/5

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

No annotations are provided, so the description carries full burden. It states the tool renders a PNG dashboard, implying a read-only operation, but does not elaborate on side effects, authentication needs, or performance impacts. The prerequisite is mentioned, but broader behavioral traits are absent.

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?

Two sentences: the first clearly states purpose and output format, the second adds a necessary prerequisite. Every sentence is essential; there is no 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 covers the basic purpose and a prerequisite. However, it does not explain how the output is delivered (e.g., image data vs. URL) or provide any usage examples. It is adequate but not fully complete.

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?

There are no parameters, so schema coverage is 100%. The baseline is 3 per guidelines. The description adds no parameter information because none exist, meeting the baseline.

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 it renders a PNG dashboard comparing GPU extended resources per node versus pod requests. It uses specific verbs ('Render') and resource ('dashboard'), and distinguishes from siblings like 'render_gpu_metrics' and 'render_gpu_sparkline' which serve different visualization purposes.

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

Usage Guidelines4/5

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

The description provides a clear prerequisite ('Requires nodes with GPU resources or workloads requesting GPUs'), guiding when to use the tool. While it does not explicitly mention when not to use or list alternatives, the context is sufficient for an agent to decide.

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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