MCP Code Analysis Server

# Advanced Domain Analysis Tools The MCP Code Analysis Server provides advanced analysis tools that go beyond basic domain extraction to provide actionable insights about your codebase's architecture and health. ## Overview These tools analyze the relationships and patterns in your domain model to: - Detect architectural anti-patterns - Measure coupling between bounded contexts - Suggest improvements to your domain boundaries - Track how your domain model evolves over time ## Available Tools ### 1. Analyze Coupling Analyzes coupling between bounded contexts to identify architectural issues. ```json { "tool": "analyze_coupling", "parameters": { "repository_id": 123 // Optional, analyzes all if not specified } } ``` **Returns:** - Context coupling scores and metrics - High coupling pairs that need attention - Specific recommendations for reducing coupling - Distribution of coupling levels across contexts **Example Output:** ```json { "contexts": [ { "name": "Sales", "coupling_score": 3.5, "outgoing_dependencies": 42, "coupled_with": 3 } ], "high_coupling_pairs": [ { "source": "Sales", "target": "Inventory", "relationship_count": 15, "relationship_types": ["depends_on", "orchestrates"], "recommendation": "Consider using events or a saga pattern to reduce orchestration coupling" } ], "metrics": { "average_coupling": 2.3, "max_coupling": 5.2, "coupling_distribution": { "low": 5, "medium": 8, "high": 2 } } } ``` ### 2. Detect Anti-Patterns Identifies common Domain-Driven Design anti-patterns in your codebase. ```json { "tool": "detect_anti_patterns", "parameters": { "repository_id": 123 // Optional } } ``` **Detects:** - **Anemic Domain Models**: Entities with no business logic - **God Objects**: Entities with too many responsibilities - **Circular Dependencies**: Cyclic relationships between entities - **Missing Aggregate Roots**: Contexts without consistency boundaries - **Chatty Contexts**: Excessive inter-context communication - **Shared Kernel Abuse**: Entities shared by too many contexts **Example Output:** ```json { "anemic_domain_models": [ { "entity": "Customer", "type": "entity", "issue": "No business rules or invariants defined", "recommendation": "Add business logic to make this a rich domain model", "severity": "medium" } ], "god_objects": [ { "entity": "OrderService", "responsibility_count": 12, "issue": "Too many responsibilities", "recommendation": "Extract some responsibilities into domain services", "severity": "high" } ] } ``` ### 3. Suggest Context Splits Analyzes large bounded contexts and suggests how to split them based on cohesion patterns. ```json { "tool": "suggest_context_splits", "parameters": { "min_entities": 20, "max_cohesion_threshold": 0.4 } } ``` **Returns:** - Contexts that are candidates for splitting - Suggested new context boundaries - Estimated cohesion for each suggested split - Key entities and aggregate roots for each split **Example Output:** ```json [ { "context": "LargeOrderingContext", "current_size": 45, "cohesion_score": 0.35, "suggested_splits": [ { "suggested_name": "Order Context", "entity_count": 18, "aggregate_roots": ["Order", "Cart"], "cohesion_estimate": 0.85 }, { "suggested_name": "Payment Context", "entity_count": 12, "aggregate_roots": ["Payment", "Invoice"], "cohesion_estimate": 0.78 } ], "reasoning": "Low cohesion (0.35) indicates weak relationships between entity groups" } ] ``` ### 4. Analyze Domain Evolution Tracks how your domain model has evolved over time to identify trends and patterns. ```json { "tool": "analyze_domain_evolution", "parameters": { "repository_id": 123, "days": 30 } } ``` **Provides:** - New entities and contexts added - Entities that were removed or modified - Growth rate trends - Insights about domain development patterns **Example Output:** ```json { "time_period": "Last 30 days", "entity_changes": { "added": [ { "name": "RefundPolicy", "type": "value_object", "created": "2024-01-15T10:30:00Z" } ] }, "trends": { "entity_growth_rate": 15.5, "context_stability": 0.85, "coupling_trend": "increasing" }, "insights": [ "Rapid growth in domain entities indicates active feature development", "New bounded contexts suggest evolving domain understanding" ] } ``` ### 5. Get Domain Metrics Provides a comprehensive health report for your domain model. ```json { "tool": "get_domain_metrics", "parameters": { "repository_id": 123 // Optional } } ``` **Returns:** - Overall health score (0-100) - Key metrics about coupling and anti-patterns - Top issues to address - Actionable insights for improvement **Example Output:** ```json { "health_score": 72.5, "metrics": { "average_context_coupling": 2.3, "anti_pattern_counts": { "high": 3, "medium": 8, "low": 12 }, "total_contexts": 12, "high_coupling_pairs": 2 }, "insights": [ { "type": "high_coupling", "message": "High average coupling between contexts indicates potential architectural issues", "recommendation": "Consider introducing anti-corruption layers or event-driven communication" } ], "top_issues": [ { "type": "missing_aggregate_roots", "severity": "high", "description": "Context has entities but no aggregate root", "entity": "UserManagement" } ] } ``` ## Use Cases ### 1. Architecture Review Use these tools during architecture reviews to: - Identify architectural debt - Find opportunities for microservice extraction - Validate bounded context boundaries ### 2. Refactoring Planning Before major refactoring: - Detect which anti-patterns to address first - Understand coupling that needs to be broken - Plan context splits based on cohesion analysis ### 3. Team Education Help teams understand DDD principles by: - Showing concrete examples of anti-patterns in their code - Demonstrating the impact of high coupling - Tracking improvement over time ### 4. Continuous Monitoring Set up regular analysis to: - Track domain health metrics over time - Alert on new anti-patterns - Monitor coupling trends ## Best Practices 1. **Regular Analysis**: Run these tools regularly, not just during crises 2. **Focus on High Severity**: Address high-severity issues first 3. **Track Progress**: Use evolution analysis to ensure improvements 4. **Team Discussion**: Use results as input for architecture discussions 5. **Iterative Improvement**: Don't try to fix everything at once ## Integration with CI/CD These tools can be integrated into your CI/CD pipeline: ```bash # Example GitHub Action - name: Analyze Domain Health run: | mcp call get_domain_metrics --repository_id $REPO_ID - name: Check Anti-Patterns run: | mcp call detect_anti_patterns --repository_id $REPO_ID ``` ## Limitations - Analysis quality depends on having domain entities already extracted - Suggestions are heuristic-based and should be reviewed by architects - Some patterns may be intentional architectural decisions - Currently supports only Python codebases