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KubeCraft MCP Server

KubeCraft MCP Server

by cloudcrafttech
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Hybrid

Render Cluster Topology (PNG)

render_cluster_topology

Render cluster topology as a PNG image with nodes, namespaces, and pod distribution to visually analyze cluster structure.

Instructions

Render the cluster topology diagram as a PNG image. Shows nodes, namespaces, and pod distribution as a visual image.

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_cluster_topology"
  }
}

Input Schema

NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

A3.8/5.0
Behavior2/5

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

No annotations are provided, so the description must disclose behavioral traits. It states what the diagram shows but does not mention whether it queries the live cluster, performance implications, permissions required, or output format details (e.g., file vs base64). The description adds some context but lacks depth.

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, no wasted words. The key information (output format and content) is front-loaded. Every sentence earns its place.

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 zero-parameter tool with no annotations and no output schema, the description adequately explains what the tool does and what it shows. However, it could mention how the output is returned (e.g., as a file or inline image) to be fully complete.

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?

There are zero parameters, so schema coverage is 100% trivially. The description does not need to add parameter semantics, earning a baseline of 4.

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 the cluster topology diagram as a PNG image, specifying the resource (cluster topology) and output format. It distinguishes from sibling visualization tools like render_app_architecture or render_gpu_dashboard by mentioning nodes, namespaces, and pod distribution.

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?

No explicit guidance on when to use this tool over alternatives. The description implies usage for viewing cluster topology visually, but does not mention when not to use it or provide sibling differentiation.

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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