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

# 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

| Type | Focus | |------|-------| | full | Comprehensive: quality, security, performance, architecture | | security | Security vulnerabilities and auth issues | | performance | Bottlenecks and optimization | | quick | Fast pass for obvious issues |

Validation Types

| Type | Description | |------|-------------| | external | Uses expert model for validation (default) | | internal | Local-only review, no external validation |

Required Parameters

| Parameter | Type | Description | |-----------|------|-------------| | step | string | Review narrative | | step_number | int | Current step | | total_steps | int | Total steps (usually 2 for external) | | next_step_required | bool | More review needed? | | findings | string | Review findings |

Optional Parameters

| Parameter | Type | Description | |-----------|------|-------------| | review_type | enum | full/security/performance/quick | | review_validation_type | enum | external/internal | | severity_filter | enum | critical/high/medium/low/all | | focus_on | string | Specific emphasis (e.g., "threading") | | standards | string | Coding standards to enforce | | relevant_files | list | Files under review | | issues_found | list | Issues with severity | | continuation_id | string | Continue session |

Issue Severity Levels

issues_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

mcp__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