Pal Refactor

PAL Refactor - Code Improvement

Analyze code for refactoring opportunities with systematic investigation.

When to Use

• Detecting code smells
• Breaking down large classes/functions
• Modernizing legacy code
• Improving code organization
• Reducing technical debt
• Preparing for feature work

Quick Start

result = mcp__pal__refactor(
    step="Scanning for code smells in data processing module",
    step_number=1,
    total_steps=2,
    next_step_required=True,
    findings="Beginning code smell analysis",
    refactor_type="codesmells",
    relevant_files=[
        "/app/services/data_processor.py"
    ],
    confidence="exploring"
)

Refactor Types

Required Parameters

Optional Parameters

Common Code Smells

Size Issues

• Long Method - Methods > 20 lines
• Large Class - Classes with too many responsibilities
• Long Parameter List - > 3-4 parameters

Complexity Issues

• Deep Nesting - More than 2-3 levels
• Complex Conditionals - Hard to follow logic
• Feature Envy - Method uses another class's data excessively

Duplication

• Copy-Paste Code - Repeated logic
• Parallel Inheritance - Mirror class hierarchies

Coupling Issues

• Inappropriate Intimacy - Classes know too much about each other
• Message Chains - a.b().c().d()

Example: Decomposition Analysis

mcp__pal__refactor(
    step="""
    Analyzing UserService for decomposition opportunities

    Current state:
    - 800 lines, 25 methods
    - Handles: auth, profile, preferences, notifications, billing
    """,
    step_number=1,
    total_steps=2,
    next_step_required=True,
    findings="""
    Decomposition opportunities:

    1. Extract AuthService (high priority)
       - login, logout, password_reset, token_refresh
       - 200 lines, 6 methods

    2. Extract NotificationService (medium)
       - send_email, send_sms, preferences
       - 150 lines, 5 methods

    3. Extract BillingService (high)
       - subscribe, cancel, invoice, payment_methods
       - 250 lines, 8 methods

    Remaining in UserService:
    - Core profile CRUD
    - 200 lines, 6 methods
    """,
    refactor_type="decompose",
    relevant_files=["/app/services/user_service.py"],
    issues_found=[
        {"severity": "high", "type": "decompose",
         "description": "UserService violates SRP - handles 5 concerns"},
        {"severity": "medium", "type": "codesmells",
         "description": "Methods averaging 40 lines - extract helpers"}
    ],
    confidence="partial"
)

Refactoring Patterns

Extract Method

# Before
def process_order(order):
    # validate
    if not order.items:
        raise ValueError("Empty order")
    if order.total < 0:
        raise ValueError("Invalid total")
    # ... more validation
    # process
    # ... processing logic

# After
def process_order(order):
    validate_order(order)
    execute_order_processing(order)

Replace Conditional with Polymorphism

# Before
def calculate_shipping(order):
    if order.type == "standard":
        return order.weight * 0.5
    elif order.type == "express":
        return order.weight * 1.5
    elif order.type == "overnight":
        return order.weight * 3.0

# After
class ShippingCalculator(ABC):
    @abstractmethod
    def calculate(self, order): pass

class StandardShipping(ShippingCalculator):
    def calculate(self, order):
        return order.weight * 0.5

Best Practices

1. Refactor in small steps - One change at a time
2. Keep tests passing - Run after each change
3. Commit frequently - Easy to revert
4. Document why - Not just what changed
5. Prioritize by impact - High-traffic code first