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atlas_review

Review code across quality, security, performance, architecture, testing, and documentation. Receive grades, detailed findings, and actionable suggestions.

Instructions

AI code review assistant with multi-dimensional quality analysis: code quality, security, performance, architecture, testing, and documentation. Provides grades, detailed findings, and actionable suggestions.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
codeYesThe code to review
filePathNoOptional file path
languageYesProgramming language
frameworkNoFramework being used
checkTestsNoCheck test coverage (default: false)
changedFilesNoFiles changed in PR
checkQualityNoCheck code quality (default: true)
checkSecurityNoCheck security (default: true)
teamStandardsNoTeam coding standards
checkPerformanceNoCheck performance (default: true)
checkArchitectureNoCheck architecture (default: true)
checkDocumentationNoCheck documentation (default: true)
pullRequestContextNoPR context for review
Behavior2/5

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

With no annotations, the description carries full burden for behavioral disclosure. It mentions the tool 'provides grades, findings, and suggestions,' which describes outputs, but it does not state whether the tool is read-only, what side effects exist, or any permission requirements. For a review tool, non-destructive behavior is implied but not explicitly stated.

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 two sentences, both informative and free of filler. It front-loads the core purpose and immediately lists the analysis dimensions and output types, achieving maximum clarity in minimal space.

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?

Given the tool's complexity (13 parameters, no output schema, no annotations), the description is adequate but not complete. It explains what the tool does and what it returns, but it does not mention required inputs (code, language) beyond what's in the schema, nor does it describe the format of grades or findings. Some context is inferred from the tool name and schema.

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 baseline is 3. The description lists the check dimensions (quality, security, performance, etc.) which map to the boolean parameters, adding slight context. However, it does not elaborate on parameter usage or relationships beyond what the schema already provides.

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 tool is an AI code review assistant with multi-dimensional analysis (code quality, security, performance, etc.) and provides grades, findings, and suggestions. However, it does not explicitly distinguish itself from sibling tools like atlas_security or atlas_critique, which may overlap in function.

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?

No explicit guidance on when to use this tool versus alternatives. The description implies it is for code review, but there are no exclusions, prerequisites, or references to specialized sibling tools. For a tool with many similar siblings, this lack of differentiation is a gap.

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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