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k8s-interactive-mcp

by TaichiHo
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JavaScript
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k8s-interactive-mcp

A MCP server that can run Kubernetes commands with a given kubeconfig path and provide interpretation of the commands.

Result

Related MCP server: kubernetes-mcp-server

Features

  • Run kubectl commands through MCP tools

  • Flexible command line piping

  • Automatic kubectl installation check

  • Support for custom kubeconfig paths

  • Error handling and helpful messages

Usage

  1. Install dependencies:

npm install

Build the server:

npm run build

For development with auto-rebuild:

npm run watch

Installation

To use with Claude Desktop, add the server config:

On MacOS: ~/Library/Application Support/Claude/claude_desktop_config.json On Windows: %APPDATA%/Claude/claude_desktop_config.json

{
  "mcpServers": {
    "k8s-interactive": {
      "command": "/path/to/k8s-interactive/build/index.js"
    }
  }
}

Debugging

Since MCP servers communicate over stdio, debugging can be challenging. We recommend using the MCP Inspector, which is available as a package script:

npm run inspector

The Inspector will provide a URL to access debugging tools in your browser.

Available Tools

1 tool
run_kubectl_commandC

Run a kubectl command against the cluster pointed to by the current kubeconfig

ParametersJSON Schema
NameRequiredDescriptionDefault
commandYesThe kubectl command to run. It should also include the 'kubectl' prefix.
kubeconfigYesPath to the kubeconfig file

TDQS

C2.9/5.0
Behavior2/5

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It mentions running a command against a cluster but fails to disclose critical traits like whether this is a read-only or destructive operation, potential security implications, error handling, or output format. This leaves significant gaps in understanding the tool's behavior.

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, efficient sentence that directly states the tool's function without unnecessary words. It is appropriately sized and front-loaded, with every part contributing essential information, earning a top score for conciseness.

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

Completeness2/5

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

Given the complexity of running kubectl commands (which can be destructive or require specific permissions), the lack of annotations and output schema, and the description's failure to address behavioral traits, the description is incomplete. It does not provide enough context for safe or effective use by an AI agent.

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?

The schema description coverage is 100%, meaning the input schema already documents both parameters thoroughly. The description adds no additional meaning beyond what the schema provides, such as examples or constraints on the command format. Thus, it meets the baseline score of 3 for high schema coverage.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the action ('Run a kubectl command') and the target ('against the cluster pointed to by the current kubeconfig'), providing a specific verb+resource combination. However, since there are no sibling tools mentioned, it cannot demonstrate differentiation from alternatives, which prevents a score of 5.

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 provides no guidance on when to use this tool versus alternatives or any prerequisites for its use. It lacks explicit context about scenarios where this tool is appropriate, such as for administrative tasks or debugging, leaving the agent without usage direction.

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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TDQS

B3.1/5.0
Disambiguation5/5

With only one tool, there is no possibility of ambiguity or overlap between tools. The tool's purpose is clearly defined as running kubectl commands, making it impossible for an agent to confuse it with another tool.

Naming Consistency5/5

Since there is only one tool, naming consistency is inherently perfect. The tool name 'run_kubectl_command' follows a clear verb_noun pattern, and there are no other tools to create inconsistency.

Tool Count2/5

A single tool is too few for a Kubernetes management server, as it lacks coverage for common operations like listing resources, applying configurations, or checking cluster status. This forces agents to rely solely on raw kubectl commands, which is inefficient and error-prone for the domain.

Completeness2/5

The tool surface is severely incomplete for Kubernetes management. While 'run_kubectl_command' provides a generic entry point, it lacks dedicated tools for essential operations such as get, apply, delete, describe, or logs, leaving significant gaps that will cause agent failures in automated workflows.

Maintenance

ActivityInactive
ResponsivenessSyncing

Resources

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