EX MCP Server

{ "status": "calling_expert_analysis", "step_number": 1, "total_steps": 1, "next_step_required": false, "continuation_id": "6029145e-932e-48eb-8908-113b970b01b0", "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 planner tool demonstrates a well-structured architecture for sequential planning with sophisticated features like branching, revision, and deep thinking pauses. While it effectively addresses complex planning scenarios through its workflow-based design, it exhibits significant architectural complexity in schema management and tight coupling to the base workflow framework. The tool's self-contained nature enhances stability, but its extensive field exclusions and metadata handling create maintainability risks that could impede future evolution.\n\n## Strategic Findings (Ordered by Impact)\n\n### 1. Schema Management Overengineering\n**Insight:** The input schema implementation suffers from excessive complexity due to extensive field exclusions and overrides, creating a maintenance burden.\n**Evidence:** Lines 181-250 show 15 excluded fields across two categories (`excluded_workflow_fields` and `excluded_common_fields`) plus 6 planner-specific overrides. The `WorkflowSchemaBuilder.build_schema()` call requires manual exclusion lists that must be maintained in sync with base class changes.\n**Impact:** High maintenance cost and fragility - any base workflow schema change risks breaking the planner's input validation. This violates the DRY principle and increases regression testing scope.\n**Recommendation:** Refactor to use composition over inheritance for schema building. Create a dedicated `PlannerSchemaBuilder` that inherits core functionality but explicitly defines only relevant fields, eliminating exclusion lists.\n**Effort vs. Benefit:** Medium effort, High payoff (reduces maintenance burden by ~70%).\n\n### 2. State Management Brittleness\n**Insight:** In-memory state management without persistence creates session fragility and limits scalability.\n**Evidence:** Branch state is stored in `self.branches = {}` (line 129) without serialization. Work history relies entirely on `self.work_history` from the base class with no backup mechanism.\n**Impact:** Planning sessions are lost on server restart, preventing recovery of complex multi-step plans. This creates operational risk for long-running planning tasks and limits horizontal scaling.\n**Recommendation:** Implement a pluggable persistence layer (e.g., SQLite or Redis) with automatic checkpointing after each planning step. Add session resumption capability via continuation IDs.\n**Effort vs. Benefit:** Medium effort, High payoff (enables session recovery and horizontal scaling).\n\n### 3. Metadata Duplication Inconsistency\n**Insight:** Critical metadata is constructed in multiple locations with overlapping but not identical logic, creating inconsistency risks.\n**Evidence:** Metadata is built separately in `build_base_response()` (lines 356-364) and `customize_workflow_response()` (lines 451-462). The `step_history_length` appears in both with identical calculation but separate implementations.\n**Impact:** High risk of metadata divergence during maintenance, potentially causing incorrect step tracking or branch state corruption. Violates single-source-of-truth principle.\n**Recommendation:** Extract metadata construction into a dedicated `_build_planner_metadata()` helper method called by both response builders. Implement unit tests verifying metadata consistency.\n**Effort vs. Benefit:** Low effort, Medium payoff (eliminates duplication and consistency bugs).\n\n### 4. Deep Thinking Pause UX Complexity\n**Insight:** Mandatory deep thinking pauses, while valuable for complex plans, create cognitive friction and workflow interruption risks.\n**Evidence:** Lines 384-422 implement forced pauses with strict instructions like \"MANDATORY: DO NOT call the planner tool again immediately\" (line 391). The pauses trigger for any plan ≥5 steps regardless of actual complexity.\n**Impact:** Potential user frustration from artificial workflow interruptions, especially for moderately complex plans. May reduce adoption for time-sensitive planning scenarios.\n**Recommendation:** Make deep thinking pauses configurable with complexity thresholds. Add a \"quick planning\" mode that skips pauses for users who prefer continuous flow. Include visual progress indicators during pauses.\n**Effort vs. Benefit:** Low effort, Medium payoff (improves UX flexibility).\n\n### 5. Validation Logic Gaps\n**Insight:** Field validation is incomplete for critical planning state transitions, risking inconsistent plan evolution.\n**Evidence:** Validators exist for `step_number` and `total_steps` (lines 98-110) but not for branching/revision fields. No validation ensures `revises_step_number` < current step or that `branch_from_step` references existing steps.\n**Impact:** Potential for corrupted plan states with invalid revisions or orphaned branches. Could cause planning session failures with confusing error messages.\n**Recommendation:** Add Pydantic validators for all planning state transitions. Implement a `validate_plan_state()` method that checks branching/revision consistency before processing steps.\n**Effort vs. Benefit:** Low effort, High payoff (prevents state corruption bugs).\n\n## Quick Wins\n- Consolidate metadata construction into a single helper method (lines 356-364 and 451-462)\n- Add missing validators for branching/revision fields (lines 70-75)\n- Implement basic logging for branch operations to aid debugging\n- Add unit tests for schema exclusion lists to catch base class changes early\n\n## Long-Term Roadmap Suggestions\n1. **Phase 1 (0-3 months):** Address immediate technical debt - metadata consolidation, validation gaps, and logging improvements\n2. **Phase 2 (3-6 months):** Implement persistence layer and session recovery capabilities\n3. **Phase 3 (6-12 months):** Architectural refactoring - replace inheritance-based schema building with composition\n4. **Phase 4 (12+ months):** Enhanced UX features - configurable deep thinking, visual planning timeline, collaborative planning support\n\n## Next Steps\n1. **Immediate:** Consolidate metadata construction logic and add missing field validators to improve stability\n2. **Short-term:** Implement basic persistence using SQLite for session recovery and operational resilience\n3. **Medium-term:** Initiate architectural refactoring to decouple schema management from base workflow framework\n4. **Validation:** Create comprehensive test suite covering all branching/revision scenarios and schema edge cases\n\nThe planner tool shows strong architectural intent but requires focused refactoring to reduce complexity and improve maintainability. Prioritizing schema management and state persistence will provide the highest ROI for long-term stability and scalability.", "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 planner tool implementation for flaws, inefficiencies, instability, and UX complexity risks.", "steps_taken": 1, "files_examined": [], "relevant_files": [ "C:\\Project\\EX-AI-MCP-Server\\tools\\planner.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" } }