---

name: pal-codereview description: Systematic code review covering quality, security, performance, and architecture using PAL MCP. Use for pull request reviews, code audits, or pre-commit validation. Triggers on review requests, PR reviews, or code quality checks.

PAL Code Review

Structured code review with expert validation across quality, security, performance, and architecture.

When to Use

• Pull request reviews
• Pre-merge code audits
• Security-focused reviews
• Performance reviews
• Code quality assessments

Quick Start

Copy# Start a code review
result = mcp__pal__codereview(
    step="Reviewing authentication changes for security and quality",
    step_number=1,
    total_steps=2,
    next_step_required=True,
    findings="Beginning comprehensive review",
    review_type="full",
    relevant_files=[
        "/app/auth/login.py",
        "/app/auth/tokens.py"
    ],
    confidence="exploring"
)

Review Types

Validation Types

Required Parameters

Optional Parameters

Issue Severity Levels

Copyissues_found=[
    {"severity": "critical", "description": "SQL injection vulnerability"},
    {"severity": "high", "description": "Missing authentication check"},
    {"severity": "medium", "description": "Inefficient database query"},
    {"severity": "low", "description": "Inconsistent naming convention"}
]

Example: Security Review

Copymcp__pal__codereview(
    step="Security-focused review of payment processing code",
    step_number=1,
    total_steps=2,
    next_step_required=True,
    findings="Scanning for injection, auth bypass, data exposure",
    review_type="security",
    severity_filter="medium",  # Report medium and above
    relevant_files=[
        "/app/payments/processor.py",
        "/app/payments/validation.py"
    ],
    focus_on="input validation, PCI compliance",
    confidence="exploring"
)

Review Checklist

Quality

• Code readability and clarity
• Appropriate abstractions
• Error handling
• Test coverage

Security

• Input validation
• Authentication/authorization
• Secrets management
• Injection prevention

Performance

• Database query efficiency
• Caching strategy
• Algorithm complexity
• Resource cleanup

Architecture

• Separation of concerns
• Dependency management
• API design
• Scalability considerations

Best Practices

1. Cover all aspects - Quality, security, performance, architecture
2. Prioritize findings - Critical issues first
3. Be constructive - Suggest improvements, not just problems
4. Reference code - Use file paths and line numbers
5. Consider context - Time pressure, team experience