EX MCP Server

{ "status": "calling_expert_analysis", "step_number": 1, "total_steps": 1, "next_step_required": false, "continuation_id": "28ac0eeb-420a-4630-a3cb-97f804feccdd", "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 consensus tool is a well-intentioned multi-model “committee” workflow, but it couples orchestration, prompt templating, model I/O, and state management into one 900-line class. The design is **synchronous-by-nature** (one model at a time), **stateful-by-default** (instance variables mutated on every step), and **chatty-over-the-wire** (every step is a fresh MCP call that must re-transmit the entire accumulated history). These traits create a trifecta of **scalability bottlenecks**, **runtime fragility**, and **user-experience complexity** that will worsen as model count, payload size, or invocation rate grows. The code is readable and unit-tested, yet the macro-architecture is not ready for production traffic or long-term feature evolution without a phased refactor.\n\n---\n\n## Strategic Findings (Ordered by Impact)\n\n### 1. Stateful Singleton Orchestrator\n**Insight:** The tool inherits from `WorkflowTool` but keeps **all progress in `self.*` instance variables** (`models_to_consult`, `accumulated_responses`, `original_proposal`, …). \n**Evidence:** Lines 167-171, 442, 472-473, 487. \n**Impact:** \n- Concurrent users overwrite each other’s state → non-deterministic results. \n- Horizontal scaling (multiple replicas) is impossible without sticky sessions. \n- A crash between steps loses the entire workflow—no recovery path. \n**Recommendation:** Persist workflow state in an external, versioned store (Redis, Dynamo, Postgres) keyed by a short-lived `workflow_id` passed in every MCP request. \n**Effort vs. Benefit:** Medium / High.\n\n### 2. Chatty Synchronous Model Fan-out\n**Insight:** Models are consulted **sequentially inside the same MCP invocation** (line 484). Each step waits for the previous model to finish. \n**Evidence:** `execute_workflow` loop lines 478-555. \n**Impact:** \n- Latency grows **linearly** with model count (4 models ≈ 4× latency). \n- Down-stream providers are idle while peer models run—wastes quota & money. \n- Users stare at a spinner for minutes on multi-model debates. \n**Recommendation:** Fire models in **parallel** with `asyncio.gather` (or a job queue) and stream partial results back via Server-Sent Events or WebSocket. \n**Effort vs. Benefit:** Medium / High.\n\n### 3. Payload Amplification & Network Churn\n**Insight:** Every step re-uploads **all accumulated responses** as JSON text (line 554). A 5-model, 4 kB response each becomes 20 kB on step 5—**quadratic growth**. \n**Evidence:** `json.dumps(response_data, indent=2, ensure_ascii=False)`. \n**Impact:** \n- MCP message size balloons → hitting LLM context window or gRPC/HTTP limits. \n- Slower UI renders, especially over CLI or mobile SSH. \n**Recommendation:** Return only **deltas** (`model_id`, `stance`, `verdict`) plus a `workflow_id`; let the client pull full history when needed. \n**Effort vs. Benefit:** Low / Medium.\n\n### 4. Hidden Router Coupling & Runtime Fail-fast\n**Insight:** Step-1 pre-flight (line 643-714) **blocks the entire workflow** if any model is unavailable. \n**Evidence:** `unavailable` list → `ValueError` raised. \n**Impact:** \n- A single deprecated model name aborts a 10-minute consensus. \n- No graceful degradation (skip & annotate, or fallback to “neutral” stance). \n**Recommendation:** Convert hard fails into **warnings**; allow optional “best-effort” mode that continues with available models. \n**Effort vs. Benefit:** Low / High.\n\n### 5. Over-engineered Prompt Templating\n**Insight:** 200-line `_get_stance_enhanced_prompt` duplicates ethical guardrails already present in system cards. \n**Evidence:** Lines 723-791. \n**Impact:** \n- Prompts drift out of sync with model provider updates. \n- Token burn increases cost. \n**Recommendation:** Replace stance injection with a **single meta-instruction** (“You are asked to argue FOR/AGAINST the following…”) and let the model’s native alignment handle ethics. \n**Effort vs. Benefit:** Low / Medium.\n\n### 6. File-Context Leakage Risk\n**Insight:** Temporary `ModelContext` is attached to `self` then explicitly deleted (lines 576-594), but any exception between 579-586 leaves `_model_context` alive. \n**Evidence:** `try/finally` pattern is correct yet fragile under future edits. \n**Impact:** \n- Could leak file handles or auth tokens across model calls. \n**Recommendation:** Use `", "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 consensus tool implementation for flaws, inefficiencies, instability, and UX complexity risks.", "steps_taken": 1, "files_examined": [], "relevant_files": [ "C:\\Project\\EX-AI-MCP-Server\\tools\\consensus.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" } }