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