EX MCP Server

{ "status": "calling_expert_analysis", "step_number": 1, "total_steps": 1, "next_step_required": false, "continuation_id": "dd516e95-d72e-40d2-a67c-9ba3c7e4efab", "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 pre-commit tool is a well-intentioned attempt to wrap systematic, multi-step validation behind a single CLI façade. \nStrengths: clear separation of concerns (schema, workflow, expert fallback) and extensive field-level documentation. \nRisks: the implementation is **over-orchestrated**—it couples UI guidance, state management, and business logic in one 700-line class, relies on brittle string-based confidence levels, and forces a rigid step-wise protocol that will **frustrate CI/automation** and **slow interactive use**. \nMaintainability and future extensibility are already strained: any change to validation rules or UX copy requires editing the monolithic tool, and the lack of tests or telemetry means regressions will be silent.\n\n---\n\n## Strategic Findings (Ordered by Impact)\n\n### 1. Monolithic God-Class Architecture\n**Insight:** `PrecommitTool` mixes schema generation, workflow orchestration, prompt templating, expert-analysis dispatch, and UI messaging in a single 700-line unit. \n**Evidence:** Lines 183–705 contain ~20 responsibilities (e.g., `get_required_actions`, `prepare_expert_analysis_context`, `customize_workflow_response`). \n**Impact:** High coupling → every feature change ripples through the class; onboarding new contributors requires understanding the entire file; impossible to unit-test in isolation. \n**Recommendation:** Split into four collaborating components: \n- `PrecommitOrchestrator` – pure workflow state machine \n- `PrecommitValidator` – pluggable validation rules (security, perf, style) \n- `PrecommitPresenter` – CLI/JSON output formatting \n- `ExpertAnalysisService` – optional LLM fallback \n**Effort vs. Benefit:** Medium effort; High payoff (sustained velocity, testability).\n\n### 2. String-Based Confidence & Control Flow\n**Insight:** Confidence is modeled as seven magic strings (`exploring` → `certain`) used both for UI guidance and **skip-expert-analysis logic** (`request.confidence == \"certain\"`). \n**Evidence:** Lines 96–100, 504. \n**Impact:** Typo-prone, non-extensible, and impossible to introspect programmatically; a single mis-cased string bypasses expert review silently. \n**Recommendation:** Replace with `IntEnum` (`ConfidenceLevel.EXPLORING … CERTAIN`) and expose explicit policy flags (`skip_expert: bool = Field(default=False)`). \n**Effort vs. Benefit:** Low effort; Medium payoff (eliminates whole class of silent bugs).\n\n### 3. Rigid Step-wise Protocol Blocks Automation\n**Insight:** The tool **mandates** human-driven pause-investigate-resume cycles (`get_required_actions` returns prose instructions that must be obeyed). \n**Evidence:** Lines 612–647 emit “MANDATORY: DO NOT call … again immediately.” \n**Impact:** Cannot be dropped into CI pipelines or IDE save-hooks; defeats the very purpose of a *pre-commit* gate. \n**Recommendation:** Provide an `auto` mode that internally executes the same investigations (git diff, file reads, rule checks) without CLI agent involvement; keep interactive mode opt-in. \n**Effort vs. Benefit:** Medium effort; High payoff (tool becomes universally usable).\n\n### 4. Over-Specified Pydantic Schema Duplication\n**Insight:** Field descriptions are copy-pasted into a 120-line dict (`PRECOMMIT_WORKFLOW_FIELD_DESCRIPTIONS`) and then re-declared as Pydantic fields—violating DRY and ballooning the file. \n**Evidence:** Lines 35–122 vs. 129–168. \n**Impact:** Drift between description dict and actual field defaults; harder to keep docs in sync. \n**Recommendation:** Use `Field(description=...)` directly and generate help text from the model’s `schema_json()`; drop the redundant dict. \n**Effort vs. Benefit:** Low effort; Low-Medium payoff (permanent reduction in boilerplate).\n\n### 5. Hidden Global State & Race Conditions\n**Insight:** `self.initial_request` and `self.git_config` are mutated during step handling but the class is registered as a **singleton** tool; concurrent pre-commit runs (e.g., two repos) will overwrite each other’s state. \n**Evidence:** Lines 195–196, 656–666. \n**Impact:** Non-deterministic context passed to expert analysis; possible leakage of sensitive repo paths. \n**Recommendation:** Move per-invocation state into a dataclass stored in a `contextvar` or returned explicitly—make the tool stateless. \n**Effort vs. Benefit:** Medium effort; High payoff (enables safe parallel usage).\n\n### 6. No Observability or Metrics\n**Insight:** Tool logs at `INFO` level only; no timing, success-rate, or issue-severity metrics. \n**Impact:** Impossible to detect which validation rules are noisy or slow; regress", "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": "kimi-k2-turbo-preview", "provider_used": "unknown" } }