EX MCP Server

{ "status": "calling_expert_analysis", "step_number": 1, "total_steps": 1, "next_step_required": false, "continuation_id": "04428def-fad6-4e0a-a9dc-65bb77e945c9", "file_context": { "type": "fully_embedded", "files_embedded": 1, "context_optimization": "Full file content embedded for expert analysis" }, "expert_analysis": { "status": "analysis_complete", "raw_analysis": "## Executive Overview\nThe refactor tool demonstrates a well-structured, systematic approach to code analysis with comprehensive state tracking and expert validation integration. Its strengths lie in the flexible workflow design, multi-type refactoring support, and confidence-based optimization. However, significant architectural complexity, rigid step enforcement, and potential UX friction points create maintainability risks and could hinder adoption. The tool shows promise but requires strategic simplification and flexibility enhancements to achieve long-term viability.\n\n## Strategic Findings (Ordered by Impact)\n\n### 1. Architectural Overengineering\n**Insight:** Excessive abstraction layers and customization hooks create unnecessary complexity that impedes maintainability without clear current need.\n**Evidence:** The `RefactorTool` class contains 15+ override methods (lines 506-688) for minor behavioral variations like `get_completion_status`, `get_skip_reason`, and `customize_workflow_response`. The field description dictionary spans 80+ lines (lines 36-119), and the input schema builder requires complex override mappings (lines 237-317).\n**Impact:** High maintenance burden due to scattered customization logic; steep learning curve for new developers; increased risk of inconsistencies when modifying behavior. The tool's complexity appears disproportionate to its core refactoring analysis purpose.\n**Recommendation:** Consolidate customization logic into configuration-driven approach rather than method overrides. Reduce field descriptions to essential guidance. Implement a plugin system for future extensions instead of built-in hooks.\n**Effort vs. Benefit:** Medium effort; High payoff in maintainability and developer velocity.\n\n### 2. Rigid Workflow Enforcement\n**Insight:** Mandatory step-by-step investigation with forced pauses creates UX friction and inefficiency for experienced users.\n**Evidence:** Step guidance contains imperative language like \"MANDATORY: DO NOT call the refactor tool again immediately\" (lines 599-600) and \"STOP! Do NOT call refactor again yet\" (lines 612-613). The workflow requires manual progression through steps even for simple refactoring tasks.\n**Impact:** Significant productivity loss for routine refactoring; user frustration from artificial pacing; potential abandonment of tool for time-sensitive tasks. The rigidity contradicts the tool's goal of improving development efficiency.\n**Recommendation:** Introduce \"express mode\" for experienced users that bypasses forced pauses while maintaining analysis quality checks. Allow batch processing of multiple investigation steps with consolidated reporting.\n**Effort vs. Benefit:** Low effort; High payoff in user adoption and productivity.\n\n### 3. Cognitive Overload in Input Schema\n**Insight:** Excessive required fields and verbose descriptions create high cognitive friction during tool interaction.\n**Evidence:** The `RefactorRequest` model requires 15 fields per step (lines 122-170), with field descriptions averaging 5+ lines each. The `step` field description alone spans 14 lines (lines 37-50) with complex nested instructions.\n**Impact:** High barrier to entry; increased error rates in field completion; reduced tool utilization due to perceived complexity. The verbose guidance may paradoxically reduce comprehension by burying critical information.\n**Recommendation:** Implement progressive disclosure - show only essential fields initially with optional advanced fields. Replace lengthy descriptions with concise bullet points + contextual help links. Introduce field validation with real-time feedback.\n**Effort vs. Benefit:** Medium effort; High payoff in usability and error reduction.\n\n### 4. Fragile Confidence Threshold Logic\n**Insight:** The confidence-based expert analysis bypass lacks validation mechanisms, risking incomplete analyses.\n**Evidence:** The `should_call_expert_analysis` method (lines 363-382) skips expert validation solely based on `confidence == \"complete\"` without verifying analysis completeness. No objective metrics validate the user's confidence assessment (e.g., minimum files examined, issue coverage).\n**Impact:** Potential for premature analysis termination; missed refactoring opportunities; false confidence in suboptimal results. This undermines the tool's core value proposition of thorough analysis.\n**Recommendation:** Implement objective completeness checks (e.g., minimum file coverage, issue distribution across types) before allowing confidence override. Add confidence calibration through historical accuracy tracking.\n**Effort vs. Benefit:** Medium effort; Critical payoff in analysis reliability.\n\n### 5. Limited Automation Integration\n**Insight:** Manual investigation focus misses opportunities for automated code analysis integration.\n**Evidence:** Required actions emphasize manual processes like \"Read and understand the code files\" and \"Examine the overall structure\" (lines 324-330). No integration points for linters, static analyzers, or code quality metrics exist in the workflow.\n**Impact:** Inefficient use of developer time; inconsistent analysis quality; missed opportunities for scalable codebase assessment. The tool fails to leverage existing automation ecosystem.\n**Recommendation:** Integrate with static analysis tools (e.g., linters, complexity analyzers) to auto-generate initial findings. Add plugin architecture for custom analysis tools. Provide automated dependency mapping for decomposition analysis.\n**Effort vs. Benefit:** High effort; High payoff in analysis efficiency and consistency.\n\n## Quick Wins\n- Simplify field descriptions to 1-2 concise sentences each with expandable details\n- Add visual progress indicators showing analysis completion percentage\n- Implement keyboard shortcuts for common workflow actions\n- Provide pre-filled templates for common refactoring scenarios\n- Add export functionality for findings in multiple formats (JSON, Markdown, PDF)\n\n## Long-Term Roadmap Suggestions\n1. **Phase 1:** Implement express mode and progressive disclosure to reduce UX friction\n2. **Phase 2:** Integrate automated analysis tools and build plugin architecture\n3. **Phase 3:** Develop ML-assisted refactoring recommendations based on historical patterns\n4. **Phase 4:** Create collaborative analysis features for team-based refactoring projects\n\n## Next Steps\n1. **Prioritize architectural simplification** by consolidating customization hooks and reducing method overrides to improve maintainability\n2. **Implement express mode workflow** within 2 sprints to address UX friction points and improve adoption\n3. **Develop automated analysis integration roadmap** with specific tool integration milestones for the next quarter\n\nThe refactor tool shows strong conceptual foundation but requires strategic simplification and flexibility enhancements to achieve sustainable adoption. Focus should shift from comprehensive feature coverage to streamlined user experience and maintainable architecture.", "parse_error": "Response was not valid JSON" }, "next_steps": "ANALYSIS IS COMPLETE. You MUST now summarize and present ALL analysis findings organized by strategic impact (Critical → High → Medium → Low), specific architectural insights with code references, and exact recommendations for improvement. Clearly prioritize the top 3 strategic opportunities that need immediate attention. Provide concrete, actionable guidance for each finding—make it easy for a developer to understand exactly what strategic improvements to implement and how to approach them.\n\nIMPORTANT: Analysis from an assistant model has been provided above. You MUST thoughtfully evaluate and validate the expert insights rather than treating them as definitive conclusions. Cross-reference the expert analysis with your own systematic investigation, verify that architectural recommendations are appropriate for this codebase's scale and context, and ensure suggested improvements align with the project's goals and constraints. Present a comprehensive synthesis that combines your detailed analysis with validated expert perspectives, clearly distinguishing between patterns you've independently identified and additional strategic insights from expert validation.", "important_considerations": "IMPORTANT: Analysis from an assistant model has been provided above. You MUST thoughtfully evaluate and validate the expert insights rather than treating them as definitive conclusions. Cross-reference the expert analysis with your own systematic investigation, verify that architectural recommendations are appropriate for this codebase's scale and context, and ensure suggested improvements align with the project's goals and constraints. Present a comprehensive synthesis that combines your detailed analysis with validated expert perspectives, clearly distinguishing between patterns you've independently identified and additional strategic insights from expert validation.", "analysis_status": { "files_checked": 0, "relevant_files": 1, "relevant_context": 0, "issues_found": 0, "images_collected": 0, "current_confidence": "low", "insights_by_severity": {}, "analysis_confidence": "low" }, "complete_analysis": { "initial_request": "Assess the refactor tool implementation for flaws, inefficiencies, instability, and UX complexity risks.", "steps_taken": 1, "files_examined": [], "relevant_files": [ "C:\\Project\\EX-AI-MCP-Server\\tools\\refactor.py" ], "relevant_context": [], "issues_found": [], "work_summary": "=== ANALYZE WORK SUMMARY ===\nTotal steps: 1\nFiles examined: 0\nRelevant files identified: 1\nMethods/functions involved: 0\nIssues found: 0\n\n=== WORK PROGRESSION ===\nStep 1: " }, "analysis_complete": true, "metadata": { "tool_name": "analyze", "model_used": "glm-4.5", "provider_used": "unknown" } }