EX MCP Server

{ "status": "calling_expert_analysis", "step_number": 1, "total_steps": 1, "next_step_required": false, "continuation_id": "ede824fc-97cb-4278-84df-44266e624fb0", "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 precommit tool demonstrates a sophisticated workflow architecture for systematic pre-commit validation, offering comprehensive change analysis and expert integration. However, its excessive complexity, state management risks, and overengineered schema generation create significant maintainability burdens. While the tool excels in providing structured validation guidance, its current implementation poses scalability challenges and introduces unnecessary UX friction that could hinder adoption in fast-paced development environments.\n\n## Strategic Findings\n\n### 1. Overengineered Schema Generation\n**Insight:** Manual JSON schema construction creates maintenance overhead and inconsistency risks.\n**Evidence:** The `get_input_schema` method (lines 243-338) manually defines a 100+ line schema with hardcoded field descriptions, duplicating Pydantic model metadata. For example:\n```python\n\"step\": {\n \"type\": \"string\",\n \"description\": PRECOMMIT_WORKFLOW_FIELD_DESCRIPTIONS[\"step\"],\n},\n```\n**Impact:** Schema changes require dual updates (Pydantic model + manual schema), increasing bug risk. This violates DRY principles and makes evolution cumbersome.\n**Recommendation:** Replace manual schema generation with Pydantic's built-in `model_json_schema()` using field overrides only where necessary.\n**Effort vs. Benefit:** Medium effort, High benefit.\n\n### 2. Excessive State Management Complexity\n**Insight:** Uncontrolled state variables create thread-safety risks and cognitive overhead.\n**Evidence:** The tool maintains multiple stateful properties without clear lifecycle management:\n```python\nself.initial_request = None # Line 195\nself.git_config = {} # Line 196\nself.consolidated_findings # Referenced in multiple methods\n```\n**Impact:** State persistence across invocations risks data corruption in concurrent scenarios. The lack of explicit state reset mechanisms could cause memory leaks in long-running processes.\n**Recommendation:** Implement immutable state containers with clear scoping and add explicit state reset methods between workflow runs.\n**Effort vs. Benefit:** High effort, High benefit.\n\n### 3. Workflow Brittleness Through Over-Prescription\n**Insight:** Excessive step enforcement creates UX friction without proportional value.\n**Evidence:** The tool mandates rigid step sequences with forceful language:\n```python\nnext_steps = (\n f\"MANDATORY: DO NOT call the {self.get_name()} tool again immediately. \"\n f\"CRITICAL AWARENESS: You need to discover all git repositories...\"\n) # Lines 613-621\n```\n**Impact:** Overly prescriptive guidance frustrates experienced developers and reduces tool adoption. The \"forced pause\" approach (line 6) contradicts modern CI/CD efficiency needs.\n**Recommendation:** Introduce a \"quick mode\" for experienced users that bypasses step enforcement while maintaining validation rigor.\n**Effort vs. Benefit:** Low effort, Medium benefit.\n\n### 4. Configuration Sprawl and Maintainability Debt\n**Insight:** Massive configuration dictionaries create cognitive load and update risks.\n**Evidence:** The `PRECOMMIT_WORKFLOW_FIELD_DESCRIPTIONS` dictionary (lines 35-122) contains 22 verbose descriptions, each with multi-line strings. For example:\n```python\n\"step\": (\n \"Describe what you're currently investigating for pre-commit validation by thinking deeply...\"\n # 11 additional lines of description\n),\n```\n**Impact:** Configuration changes require navigating 87 lines of tightly coupled text, increasing error risk. This violates single responsibility principles.\n**Recommendation:** Extract field descriptions into a structured YAML/JSON configuration file with validation.\n**Effort vs. Benefit:** Medium effort, High benefit.\n\n### 5. Hidden Dependencies and Framework Coupling\n**Insight:** Tight coupling to undocumented framework components reduces portability.\n**Evidence:** Critical dependencies like `WorkflowTool` (line 30), `WorkflowRequest` (line 28), and `WorkflowSchemaBuilder` (line 245) are imported without context. The tool assumes framework-specific behaviors:\n```python\nreturn WorkflowSchemaBuilder.build_schema(\n tool_specific_fields=precommit_field_overrides,\n model_field_schema=self.get_model_field_schema(),\n auto_mode=self.is_effective_auto_mode(),\n tool_name=self.get_name(),\n) # Lines 333-338\n```\n**Impact:** Framework changes could break the tool. The lack of abstraction layers makes testing and reuse difficult.\n**Recommendation:** Introduce adapter patterns for framework interactions and document all external dependencies.\n**Effort vs. Benefit:** High effort, Medium benefit.\n\n## Quick Wins\n- **Simplify Confidence Levels**: Reduce 7 confidence states (line 283) to 3 (low/medium/high) to decrease decision overhead\n- **Add Input Validation**: Implement Pydantic validators for critical fields like `path` (line 178) beyond basic presence checks\n- **Extract Magic Strings**: Replace hardcoded status strings (e.g., \"validation_complete_ready_for_commit\" at line 514) with enums\n- **Add State Reset Method**: Implement `reset_state()` to clear `initial_request` and `git_config` between runs\n- **Optimize Field Descriptions**: Cut verbose descriptions by 50% while preserving critical guidance\n\n## Long-Term Roadmap Suggestions\n1. **Phase 1: Decoupling** (2-3 weeks)\n - Extract configuration to external files\n - Implement adapter pattern for framework dependencies\n - Add comprehensive state management\n\n2. **Phase 2: UX Modernization** (3-4 weeks)\n - Develop \"quick mode\" for experienced users\n - Create progressive disclosure for complex options\n - Implement workflow templates for common scenarios\n\n3. **Phase 3: Performance Optimization** (4-6 weeks)\n - Add incremental analysis for large repositories\n - Implement caching for repeated validations\n - Introduce parallel processing for multi-repo analysis\n\n## Next Steps\n1. **Immediate Actions** (1-2 days):\n - Replace manual schema generation with Pydantic's `model_json_schema()`\n - Add `reset_state()` method to clear instance variables\n - Create enums for status strings and confidence levels\n\n2. **Short-Term Improvements** (1-2 weeks):\n - Extract field descriptions to YAML configuration\n - Implement basic input validation beyond presence checks\n - Add comprehensive logging for state transitions\n\n3. **Strategic Assessment** (2-3 weeks):\n - Conduct user interviews to identify workflow pain points\n - Prototype \"quick mode\" functionality\n - Develop framework adapter interfaces\n\nThis analysis reveals that while the precommit tool addresses a critical need for systematic validation, its current implementation prioritizes comprehensiveness over maintainability and usability. The recommended changes focus on reducing complexity while preserving the tool's core strengths in structured validation guidance.", "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 precommit tool implementation for flaws, inefficiencies, instability, and UX complexity risks.", "steps_taken": 1, "files_examined": [], "relevant_files": [ "C:\\Project\\EX-AI-MCP-Server\\tools\\precommit.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" } }