PAL Analyze - Code Analysis

Systematic code analysis covering architecture, performance, maintainability, and patterns.

When to Use

• Understanding unfamiliar codebases
• Architectural review and assessment
• Performance analysis and optimization
• Code quality evaluation
• Pattern identification
• Technical debt assessment

Quick Start

# Start architecture analysis
result = mcp__pal__analyze(
    step="Analyzing authentication system architecture",
    step_number=1,
    total_steps=2,
    next_step_required=True,
    findings="Beginning architecture review",
    analysis_type="architecture",
    output_format="detailed",
    relevant_files=[
        "/app/auth/service.py",
        "/app/auth/middleware.py"
    ],
    confidence="exploring"
)

Analysis Types

Type	Focus
architecture	System design, patterns, modularity
performance	Bottlenecks, optimization opportunities
security	Vulnerabilities, auth issues
quality	Code smells, maintainability
general	Comprehensive overview

Output Formats

Format	Description
summary	High-level overview
detailed	In-depth analysis
actionable	Prioritized recommendations

Required Parameters

Parameter	Type	Description
step	string	Analysis narrative
step_number	int	Current step
total_steps	int	Estimated total
next_step_required	bool	More analysis needed?
findings	string	Discoveries and insights

Optional Parameters

Parameter	Type	Description
analysis_type	enum	architecture/performance/security/quality/general
output_format	enum	summary/detailed/actionable
confidence	enum	exploring → certain
relevant_files	list	Files under analysis
files_checked	list	All files examined
issues_found	list	Issues with severity
continuation_id	string	Continue session
model	string	Override model

Example: Performance Analysis

mcp__pal__analyze(
    step="Identifying performance bottlenecks in data processing pipeline",
    step_number=1,
    total_steps=2,
    next_step_required=True,
    findings="Scanning for N+1 queries, inefficient loops, missing caching",
    analysis_type="performance",
    output_format="actionable",
    relevant_files=[
        "/app/services/data_processor.py",
        "/app/models/report.py"
    ],
    confidence="exploring"
)

What to Document in Findings

Include both strengths and concerns:

• Architecture: Patterns used, coupling, cohesion
• Performance: Complexity, caching, query patterns
• Security: Auth flows, input validation, secrets
• Quality: Duplication, naming, test coverage

Best Practices

1. Be systematic - Cover all relevant aspects
2. Document strengths - Not just problems
3. Prioritize issues - By severity and impact
4. Consider context - Team size, timeline, constraints
5. Provide evidence - Reference specific code