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review_infra_config

Audit Kubernetes, Docker, Terraform, Helm, or CloudFormation configurations for security, correctness, reliability, and cost. Returns anchored findings and remediation steps.

Instructions

Review Kubernetes, Docker, Terraform, Helm, or CloudFormation configuration for correctness, security, reliability, and cost. Returns a structured prompt with anchored findings and remediation.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
configYesRaw config file content.
targetEnvNoTarget environment: dev / staging / prod, region, scale tier.
configTypeYesType of configuration being reviewed.

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.1

TDQS

A3.8/5.0
Behavior3/5

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

With no annotations, the description carries the full burden. It discloses that the return is a structured prompt containing anchored findings plus remediation, which is genuinely useful output-shape context for an analysis tool, but it says nothing about determinism, permission/auth requirements, size limits, or how the review is performed.

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 tight sentences with no filler. The capability scope leads and the return contract follows, so an agent gets the essentials in the first sentence.

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 three-parameter, no-annotation, no-output-schema analysis tool, the description covers supported inputs and the shape of the result adequately. It falls short only on operational constraints such as input size limits or multi-file handling, which an agent might need before invoking it.

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% and all three parameters (config, configType, targetEnv) carry their own descriptions and an enum, so the schema does the heavy lifting. The description adds no format, size, or syntax guidance beyond what the schema already states, making the baseline 3 appropriate.

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?

Specific verb (review) plus a precisely enumerated resource scope: Kubernetes, Docker, Terraform, Helm, and CloudFormation configurations, across four named quality dimensions (correctness, security, reliability, cost). This distinguishes it from generic siblings like review_code and review_architecture without the agent needing to open any schema.

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?

The scope implies when the tool applies (you have an IaC/config file to audit), but there is no explicit when-to-use, when-not-to-use, or routing guidance versus review_code, review_cicd_pipeline, or deployment_readiness_check. Usage is inferable rather than stated.

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