EX MCP Server

{ "status": "calling_expert_analysis", "step_number": 1, "total_steps": 1, "next_step_required": false, "continuation_id": "2d1b7eae-8ef1-438e-975b-dde2a3186af3", "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 is a well-intentioned attempt to wrap an interactive, multi-step code-quality investigation inside a single CLI command. It succeeds at forcing agents to pause, inspect, and document findings, but the implementation is over-engineered: 700+ lines of configuration, string templating, and inheritance hooks that duplicate logic already expressed in doc-strings and Pydantic validators. The result is high UX friction (agents must learn a 19-field schema and a 4-state confidence machine), high maintenance cost (every new workflow needs a parallel class hierarchy), and latent runtime risk (validators that only fire on step 1, silent temperature overrides, and file-path handling that assumes POSIX). The architecture is not intrinsically flawed, yet its complexity grows linearly with every new workflow type, making long-term evolution expensive.\n\n---\n\n## Strategic Findings (Ordered by Impact)\n\n### 1. Configuration Overload & UX Complexity\n**Insight:** 19 request fields, 14 of them mandatory per step, force agents to act as state machines instead of engineers. \n**Evidence:** `RefactorRequest` (lines 122-171) plus `REFACTOR_FIELD_DESCRIPTIONS` (lines 36-119) contain ~4 000 characters of guidance that must be re-read each call. \n**Impact:** Onboarding friction, high token burn, and prompt-engineering errors that surface only at runtime. \n**Recommendation:** Collapse to four fields: `files`, `intent`, `findings`, `done?`. Derive the rest (step counter, confidence, issue list) inside the tool. \n**Effort vs. Benefit:** Medium / High\n\n### 2. Inheritance Depth & Parallel Hierarchy Explosion\n**Insight:** Every new workflow clones 30+ hook methods (`get_completion_status`, `get_completion_data_key`, …) that differ only by a noun. \n**Evidence:** `RefactorTool` overrides 18 abstract methods; `WorkflowTool` base already contains 25. \n**Impact:** Adding the next workflow (e.g., “security-audit”) will duplicate another 700-line module; divergence is inevitable. \n**Recommendation:** Replace inheritance with composition: a single `WorkflowEngine` class plus a JSON manifest per workflow (fields, prompts, required-actions). \n**Effort vs. Benefit:** High / High\n\n### 3. Silent Temperature & Model Override\n**Insight:** `temperature` is excluded from the schema (line 159) but still honoured via `get_default_temperature()` (line 217), creating an invisible contract. \n**Evidence:** If a caller passes `temperature=0.2` it is silently dropped; the agent believes it is using 0.2 but receives `TEMPERATURE_ANALYTICAL`. \n**Impact:** Non-deterministic behaviour that is almost impossible to debug from the outside. \n**Recommendation:** Either surface `temperature` in the schema or remove the override entirely; do not hide runtime parameters. \n**Effort vs. Benefit:** Low / Medium\n\n### 4. Step-1-Only Validation Hole\n**Insight:** `validate_step_one_requirements()` (lines 164-170) guarantees `relevant_files` only on step 1; later steps can submit empty file lists without error. \n**Evidence:** Validator is decorated with `model_validator(mode=\"after\")` but checks `step_number == 1`. \n**Impact:** Agents can “complete” an analysis that references zero files, producing vacuous expert calls. \n**Recommendation:** Make `relevant_files` mandatory for every step, or relax the constraint and allow the tool to re-use previous step’s list. \n**Effort vs. Benefit:** Low / Medium\n\n### 5. File-Path Assumption & Cross-Platform Risk\n**Insight:** All path fields are typed `list[str]` with no further validation; Windows agents can pass mixed separators or glob patterns that break downstream embedders. \n**Evidence:** Doc-strings insist on “absolute paths, do not clip” (line 73) but no `Path.resolve()` or `Path.is_absolute()` check exists. \n**Impact:** Runtime file-not-found errors that surface only during expert analysis embedding. \n**Recommendation:** Add `BeforeValidator` that converts to `Path` objects and normalises; reject relative paths early. \n**Effort vs. Benefit:** Low / High\n\n### 6. Confidence State Machine Leakage\n**Insight:** The four-state confidence enum (`exploring`, `incomplete`, `partial`, `complete`) is duplicated in three places: model field, schema enum, and hard-coded strings in `should_call_expert_analysis()`. \n**Evidence:** Lines 139-140, 272-273, 374-375. \n**Impact:** Adding a new state requires touching no less than six locations; inevitable skew. \n**Recommendation:** Define a single `Confidence` StrEnum and import everywhere; remove string literals. \n**Effort vs. Benefit:**", "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": "kimi-k2-turbo-preview", "provider_used": "unknown" } }