EX MCP Server

{ "status": "calling_expert_analysis", "step_number": 1, "total_steps": 1, "next_step_required": false, "continuation_id": "09c1e0be-5703-4dc7-8423-e89b286c4953", "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 is a conversational, step-wise planning micro-service that piggy-backs on a generic “workflow tool” framework. It is stateless from the MCP-server’s point of view (all state lives in the orchestrator’s conversation memory) and therefore scales horizontally, but the implementation is already showing three systemic pressure points: \n1. Over-grown inheritance stack (≈ 550 lines, 30+ override hooks) that makes behaviour discovery hard. \n2. All plan history, branches and revisions are kept only in the orchestrator’s message array; nothing is persisted or version-controlled – a reliability & audit liability. \n3. UX complexity: 11 optional flags, deep-thinking pauses and branch bookkeeping create a heavy cognitive load for casual users while offering no programmatic API for power users.\n\nOverall the module works for its current scope, yet the architecture is drifting toward “framework for the sake of framework” and will slow future iteration once plan templates, collaboration or persistence are requested.\n\n---\n\n## Strategic Findings (Ordered by Impact)\n\n### 1. Framework Over-Engineering & Inheritance Explosion \n**Insight:** 18 abstract methods from `WorkflowTool` are overridden, 10 more hooks added; half of the code is meta-programming, not planning logic. \n**Evidence:** Lines 256-529 are almost pure glue; e.g. `get_skip_expert_analysis_status()` returns a constant string. \n**Impact:** New team members need >30 min to trace a single user story; refactors touch many unrelated override points → regression risk. \n**Recommendation:** Collapse planner-specific behaviour into a single 150-line “PlanningEngine” class; inherit only `execute_workflow()` and delegate to the engine. \n**Effort vs. Benefit:** Medium / High\n\n### 2. Implicit State in Chat Memory – No Persistence or Concurrency Control \n**Insight:** `self.branches` and `self.work_history` live inside the tool instance that is re-created per request; the real store is the LLM orchestrator’s message array. \n**Evidence:** `self.branches` (l.129) is populated but never serialised; on server restart or horizontal scale-out all branches vanish. \n**Impact:** Users cannot return to an earlier plan after session drop; no audit trail; impossible to share or version plans. \n**Recommendation:** Introduce a light “PlanSession” repository (file, SQLite or Redis) keyed by `continuation_id`; persist branches & revisions as JSON blobs; keep chat memory as cache only. \n**Effort vs. Benefit:** Medium / High\n\n### 3. UX Complexity & Parameter Proliferation \n**Insight:** 11 optional boolean/integer fields plus free-text `step` make the JSON interface error-prone; deep-thinking pauses add friction for short plans. \n**Evidence:** Schema builder (l.186-250) lists 8 planner-specific fields; prompt text (l.384-422) forces multi-minute pauses even for 5-step plans. \n**Impact:** Onboarding docs grow, first-time failure rate increases, prompt engineering effort balloons. \n**Recommendation:** \n- Hide branching flags behind an “advanced” flag; default path accepts only `step`, `step_number`, `total_steps`. \n- Make deep-thinking pause threshold configurable (default ≥8 steps) or opt-in. \n**Effort vs. Benefit:** Low / Medium\n\n### 4. Hidden Coupling to Orchestrator Prompt Templates \n**Insight:** `PLANNER_PROMPT` (l.32) is imported but not version-locked; orchestrator can change prompt semantics without code review in this repo. \n**Evidence:** Prompt lives outside the module; no checksum or interface test asserts expected behaviour. \n**Impact:** Silent regressions when prompt is tuned; planner may start emitting markdown tables while UI expects plain list. \n**Recommendation:** Store a frozen copy of the prompt in `planner.py` plus a unit test that hashes expected prompt; upgrade explicitly. \n**Effort vs. Benefit:** Low / High\n\n### 5. Missing Validation for Branch Integrity \n**Insight:** A branch can claim `branch_from_step: 999` even if only 3 steps exist; revisions can target future steps. \n**Evidence:** No cross-step validation in `PlannerRequest` or `customize_workflow_response`. \n**Impact:** Corrupted plan trees, confusing UI rendering, possible infinite loops if client auto-increments step numbers. \n**Recommendation:** Add a post-condition check in `prepare_step_data()` that `revises_step_number <= current_step_count` and `branch_from_step <= current_step_count`. \n**Effort vs. Benefit:** Low / Medium\n\n---\n\n## Quick Wins\n- Replace 30-line status-mapping block (l.490-500) with a 3-entry dict literal. \n- Add `max_length=100` validator to `branch_id` to prevent huge metadata blobs. \n- Log a warning", "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": "kimi-k2-turbo-preview", "provider_used": "unknown" } }