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KubeGuard-MCP

KubeGuard-MCP

by Lokesh-Guttikonda
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Local

Validate a Kubernetes manifest

validate_manifest
Read-onlyIdempotent

Lint Kubernetes YAML/JSON or docker-compose manifests to catch incident-causing defects and risky settings; returns findings with line numbers and a 0–100 score.

Instructions

Lint Kubernetes YAML/JSON (or docker-compose) for incident-causing defects: missing memory limits, unpinned images, privileged/root containers, host mounts, plaintext credentials, Service targetPort drift, JVM heaps above the memory limit and risky RBAC. Returns findings with line numbers and a 0-100 score.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "manifest": "<manifest>"
    },
    "name": "validate_manifest"
  }
}

Input Schema

NameRequiredDescriptionDefault
filenameNomanifest.yaml
manifestYesManifest text (multi-document YAML allowed)

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv1.0.0

TDQS

A3.9/5.0
Behavior4/5

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

Annotations already declare readOnly, idempotent, non-destructive, closed-world behavior, so the safety profile is covered. The description adds real value beyond that by disclosing the specific defect categories inspected and the output format (findings with line numbers plus a 0-100 score), which tells the agent what to expect and how to interpret results.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

A single front-loaded sentence with the verb and scope first, followed by the enumerated checks and return format. The defect list is dense but each item carries selection value; nothing is wasteful, though the enumeration could be trimmed slightly for readability.

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?

With no output schema, the description correctly compensates by stating that findings include line numbers and a 0-100 score, and it scopes accepted input formats. The only meaningful gap is clarification of the 'filename' parameter's effect, which is minor for a two-parameter lint tool.

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 coverage is 50%: the 'manifest' param is documented as allowing multi-document YAML, while 'filename' has only a default and no described purpose. The description's mention of docker-compose hints at accepted input but does not clarify what 'filename' affects (likely labeling in findings) or format constraints.

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 opens with a specific verb+resource ('Lint Kubernetes YAML/JSON (or docker-compose)'), enumerates the exact defect classes checked, and names the return shape. It is clearly distinguishable from siblings like diagnose_pod and audit_cluster_risks, which operate on live clusters rather than manifest text.

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?

Usage is implied by 'for incident-causing defects' and the accepted input formats, giving an agent a reasonable sense of when to call it. However, there is no explicit when-to-use versus when-not guidance relative to sibling cluster-audit tools, and no statement of prerequisites such as needing the manifest text in hand.

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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