EX MCP Server

{ "status": "calling_expert_analysis", "step_number": 1, "total_steps": 1, "next_step_required": false, "continuation_id": "5a51d692-4fbb-4d63-bf08-7a4419a3ba74", "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 debug tool is a well-intentioned attempt to codify a disciplined, hypothesis-driven investigation workflow. It succeeds in forcing agents to pause, collect evidence, and back-track when dead-ends appear. However, the implementation is over-engineered: 650+ lines of mostly string templating, brittle state machines, and hard-coded confidence heuristics that will drift out of sync with real-world usage. The UX surface is enormous (25+ optional fields, 7 confidence levels, 4 step archetypes) and the tight coupling to a single-file inheritance tree makes future evolution expensive. The architecture is serviceable for a narrow internal audience today, but will become a maintenance tax as soon as the team needs multi-turn parallelism, streaming, or human-in-the-loop review.\n\n## Strategic Findings (Ordered by Impact)\n\n### 1. Over-Specified DSL for Prompting \n**Insight:** 115 lines of prose frozen in Python constants (`DEBUG_INVESTIGATION_FIELD_DESCRIPTIONS`) duplicate what the LLM already knows about debugging. \n**Evidence:** Lines 35-114 contain 4 000+ characters of micro-instructions such as “CRITICAL: Remember that reported symptoms might originate from code far from where they manifest.” \n**Impact:** Every tweak to phrasing requires a code change, review, and redeploy; the prompt cannot be A/B-tested or personalised without a release. \n**Recommendation:** Collapse to a one-sentence field hint and move the long-form coaching into a versioned system-prompt file or prompt-management service. \n**Effort vs. Benefit:** Low effort, High payoff (faster iteration, non-engineers can tune voice).\n\n### 2. Confidence Enumeration Proliferation \n**Insight:** Seven ordinal confidence buckets (`exploring` → `certain`) are manually mapped to behaviour in a dozen places; the distinctions are subjective and will not survive contact with heterogeneous models. \n**Evidence:** `get_required_actions()` (lines 279-320) and `should_call_expert_analysis()` (lines 322-337) both re-implement the same ladder; `certain` short-circuits expert review (line 513) creating a blind spot. \n**Impact:** Agents will game the level that gives the fastest path; “certain” becomes a cheat code to skip oversight, reducing debug quality. \n**Recommendation:** Replace with a continuous 0-1 score and let the orchestrator decide when to escalate; keep a single threshold knob in config. \n**Effort vs. Benefit:** Medium effort, High payoff (simpler code, better telemetry, model-agnostic).\n\n### 3. State Machine Hidden in String Templates \n**Insight:** Workflow progression is enforced by injecting ever-longer “MANDATORY: DO NOT call … again yet” strings instead of immutable state flags. \n**Evidence:** `get_step_guidance()` returns natural-language orders (lines 436-486) that the consuming agent must parse and obey; there is no programmatic gate. \n**Impact:** A mildly creative agent can ignore the prose and loop forever; failures will be detected only by human review or token-quota alarms. \n**Recommendation:** Expose a boolean `can_proceed` flag in the response schema and let the orchestrator hard-block recursive calls. \n**Effort vs. Benefit:** Low effort, Medium payoff (robust loop prevention, clearer metrics).\n\n### 4. Monolithic Inheritance Tree \n**Insight:** All workflow variants (debug, refactor, test, etc.) inherit from `WorkflowTool`, which in turn inherits from `BaseTool`; each leaf class re-implements the same 10–12 hook methods with minor string changes. \n**Evidence:** `DebugIssueTool` overrides 15 hook methods just to rename JSON keys (`investigation_status`, `complete_investigation`, …). \n**Impact:** Cross-cutting changes (e.g., adding telemetry) require touching every subclass; newcomers must learn the subtle differences between `get_completion_status()` and `get_completion_data_key()`. \n**Recommendation:** Compose behaviour with small policy objects (strategy pattern) and keep tool classes to <200 lines. \n**Effort vs. Benefit:** High effort, High payoff (accelerates future tools, reduces merge conflicts).\n\n### 5. File-Content Loading Inside Prompt Builder \n**Insight:** `prepare_expert_analysis_context()` (lines 387-395) reads arbitrary files from disk and embeds them in the prompt at full length, with no size cap or token budget. \n**Evidence:** `_prepare_file_content_for_prompt()` is called unconditionally for every path in `relevant_files`. \n**Impact:** A single 2 MB log file will blow the context window, crash the call, and stall the debugging session; no pagination or summarisation fallback exists. \n**Recommendation:** Wrap file loading behind a tokenizer-aware service that truncates, summarises, or prompts for explicit user consent beyond a threshold. \n**Effort vs. Benefit:** Medium effort", "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 debug tool implementation for flaws, inefficiencies, instability, and UX complexity risks.", "steps_taken": 1, "files_examined": [], "relevant_files": [ "C:\\Project\\EX-AI-MCP-Server\\tools\\debug.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" } }