EX MCP Server

{ "status": "calling_expert_analysis", "step_number": 1, "total_steps": 1, "next_step_required": false, "continuation_id": "1683e2db-9245-4cce-ac6e-842138749942", "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 tracer tool is a self-contained, workflow-driven CLI utility that orchestrates step-by-step code-tracing sessions. \nIt is well-layered (Pydantic models, base workflow mixin, mode-specific renderers) and keeps I/O and LLM calls outside its boundary, which aids testability. \nMain strategic risks: (1) over-constrained UX that forces rigid step-wise pauses even for trivial traces, (2) 100 % deterministic output templates that will break on multi-language/poly-repo codebases, (3) no evidence of incremental result caching or partial-resume, so long traces pay repeated file-I/O cost, (4) implicit assumption that a single “confidence” string is enough to decide when the user is “done”.\n\n## Strategic Findings (Ordered by Impact)\n\n### 1. Over-Specified Rendering Templates\n**Insight:** 570-line string literals in `_get_precision_rendering_instructions` and `_get_dependencies_rendering_instructions` mandate exact ASCII diagrams, arrow glyphs, and column alignment. \n**Evidence:** Lines 567-671 and 673-749 hard-code “↓”, “→”, “┐”, “┤” and indentation rules. \n**Impact:** Any repo with non-ASCII method names, wide Unicode glyphs, or different line-widths will produce mis-aligned output; maintenance burden is high (one formatting tweak touches 200+ lines). \n**Recommendation:** Replace monolithic strings with a tiny internal DSL + renderer (e.g., `TracerFormatter` protocol) so each trace mode only declares nodes/edges and the renderer worries about alignment. \n**Effort vs. Benefit:** Medium / High\n\n### 2. Rigid Step-Wise Pauses Reduce Throughput\n**Insight:** `handle_work_continuation` (lines 412-479) always returns `pause_for_tracing` and a human-oriented “STOP!” message—even for tiny codebases. \n**Evidence:** Lines 443-450, 454-459, 462-469 use capitalised “MANDATORY/WAIT!” language and forbid immediate re-entry. \n**Impact:** CI or headless environments must fake user acknowledgement; power users suffer friction when they already know the code. \n**Recommendation:** Add an optional `--auto` flag (or `trace_mode=auto`) that lets the caller stream steps back-to-back until confidence ≥ high; keep interactive mode as default. \n**Effort vs. Benefit:** Low / High\n\n### 3. No Incremental Caching or Resumability\n**Insight:** Every step re-scans the same files because `files_checked` is only a list of paths, not a content-hash cache. \n**Evidence:** `TracerRequest` keeps `files_checked: list[str]` (line 117) but no checksum or AST snapshot. \n**Impact:** Re-tracing after adding one file repeats full I/O; large mono-repos pay seconds-to-minutes penalty each step. \n**Recommendation:** Store a lightweight cache keyed by `(file_path, mtime, size)` and skip unchanged files; expose `continuation_id` to resume prior trace session. \n**Effort vs. Benefit:** Medium / Medium\n\n### 4. Single “confidence” String Is Brittle Control Flow\n**Insight:** Business logic (`get_required_actions`, `handle_work_continuation`) branches on string equality of `confidence` which is typed as `Optional[str]`. \n**Evidence:** Lines 313-339 compare `confidence in [\"exploring\", \"low\"]`; a typo or locale change silently alters behaviour. \n**Impact:** Runtime mis-classification leads to wrong action list; hard to unit-test. \n**Recommendation:** Convert to `Enum` (`ConfidenceLevel`) and enforce via Pydantic. \n**Effort vs. Benefit:** Low / Medium\n\n### 5. Hidden Coupling to `systemprompts.TRACER_PROMPT`\n**Insight:** Line 31 imports a module-level constant that is not versioned alongside the tool. \n**Evidence:** `get_system_prompt()` (line 208) returns the constant verbatim. \n**Impact:** Prompt drift or length changes can break token budgets without code review. \n**Recommendation:** Move prompt text into a `resources/` file hashed at startup; fail fast if hash changes unexpectedly (configuration-as-code). \n**Effort vs. Benefit:** Low / Medium\n\n### 6. Duplicated Exclusion Lists\n**Insight:** Same field names are excluded in three places: `TracerRequest` (lines 138-148), `get_input_schema` (lines 265-277), and `prepare_step_data` (lines 368-370). \n**Evidence:** `\"issues_found\", \"hypothesis\", \"temperature\"` etc. repeated. \n**Impact:** Adding one new base-tool field requires three edits; easy to miss. \n**Recommendation:** Define one `TRACER_EXCLUDED_FIELDS` tuple and reuse everywhere. \n**Effort vs. Benefit:** Low / Low\n\n## Quick", "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 tracer tool implementation for flaws, inefficiencies, instability, and UX complexity risks.", "steps_taken": 1, "files_examined": [], "relevant_files": [ "C:\\Project\\EX-AI-MCP-Server\\tools\\tracer.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" } }