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vulnforge_audit_container

Audits Dockerfiles and Kubernetes manifests (Pods, Deployments, RBAC Roles, ClusterRoles) for root container execution, missing multi-stage builds, unpinned mutable images, hardcoded secrets, dangerous capabilities, host namespace sharing…

Instructions

Audits Dockerfiles and Kubernetes manifests (Pods, Deployments, RBAC Roles, ClusterRoles) for root container execution, missing multi-stage builds, unpinned mutable images, hardcoded secrets, dangerous capabilities, host namespace sharing, and unconstrained RBAC wildcards.

Input Schema

NameRequiredDescriptionDefault
contentNoDockerfile or Kubernetes YAML manifest content to analyze
filePathNoPath or identifier of the container manifest (e.g. Dockerfile, deployment.yaml)
manifestTypeNoManifest type override (auto-detected if omitted)
dockerfileContentNoExplicit Dockerfile content (optional)
k8sManifestContentNoExplicit Kubernetes YAML manifest content (optional)

Schema Changelog

Changes observed during successful MCP inspections.

  1. Addedv1.19.0

TDQS

A3.5/5.0
Behavior3/5

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

No annotations exist, so the description carries the whole burden; it does disclose substantive behavior via the enumerated detection categories (root execution, unpinned images, RBAC wildcards, etc.), which is real value. However, it omits key operational facts: that all 5 parameters are optional (so the agent must infer that content/filePath/dockerfileContent/k8sManifestContent supplies the input) and what the audit returns.

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?

A single front-loaded sentence with the resource scope first and the defect list second; no filler and nothing repeated from the schema.

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?

With no output schema and no annotations, the description should say more about input expectations (at least one content field is needed) and result shape. The detection list is complete for what it covers, making it adequate but with clear gaps.

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 the schema already explains content, filePath, manifestType, dockerfileContent, and k8sManifestContent. The description adds nothing about parameter usage or precedence between content and the explicit content fields, so baseline 3 applies.

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?

Names a specific verb (audits) and precise resources (Dockerfiles, Kubernetes Pods/Deployments/Roles/ClusterRoles), then enumerates the exact defect classes checked. That resource-and-check specificity cleanly separates it from siblings like vulnforge_audit_iam or vulnforge_audit_cicd.

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?

There is no when-to-use or when-not-to-use guidance and no mention of the obvious alternatives (vulnforge_full_scan, other audit_* tools). Nothing tells the agent whether to reach for this versus the broader scan.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.