EX MCP Server

{ "status": "calling_expert_analysis", "step_number": 1, "total_steps": 1, "next_step_required": false, "continuation_id": "5d21fdf3-f038-4e01-82c1-00cf74a127d0", "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 Challenge tool is a lightweight, self-contained “meta-prompt” generator that wraps a user statement in instructions forcing the CLI agent to reassess rather than reflexively agree. Architecturally it is sound: no external calls, deterministic output, minimal surface area. The main risks are UX complexity (an 85-line description that must be parsed by the orchestrator), brittle pattern-matching rules for auto-triggering, and the long-term maintainability of a 200-character prompt template that is duplicated in code and schema strings. Overall it is fit-for-purpose today, but the surrounding orchestration logic (not shown) is where instability will surface.\n\n---\n\n## Strategic Findings (Ordered by Impact)\n\n### 1. Over-Long, Human-Unfriendly Tool Description \n**Insight:** The `get_description()` block (lines 55-85) is 1 100+ characters of prose and bullet lists. Orchestrators or LLMs must digest this to decide invocation, creating token bloat and raising the odds of mis-parsing. \n**Evidence:** Line 65-84 concatenates seven bullet patterns and a 40-word “DO NOT AUTO trigger” clause. \n**Impact:** Prompt token cost ↑, parsing errors ↑, future edits risk breaking the regex/pattern matcher that relies on exact wording. \n**Recommendation:** Move the “when to auto-trigger” specification into a machine-readable YAML/JSON manifest (separate from human docs) and keep `get_description()` ≤ 250 characters. \n**Effort vs. Benefit:** Low / High \n\n### 2. Implicit Coupling to Orchestrator Pattern-Matching Logic \n**Insight:** The tool itself is stateless, but its value depends on an external agent correctly detecting “But …”, “I disagree…”, etc. That logic is not versioned with this file. \n**Evidence:** Lines 68-74 list trigger patterns only in a docstring; no code enforces them. \n**Impact:** If the orchestrator’s regex drifts, the tool silently stops auto-firing, degrading UX without error logs. \n**Recommendation:** Publish a shared `challenge_trigger_patterns.json` consumed by both the tool (for docs) and the orchestrator (for matching). Add a contract test. \n**Effort vs. Benefit:** Low / Medium \n\n### 3. Prompt Template Duplication & Brittleness \n**Insight:** The critical-reassessment wrapper is hard-coded in `_wrap_prompt_for_challenge()` (lines 204-211) and partially mirrored in the field description (line 27-28). \n**Evidence:** Any copy tweak requires editing two places; no single source of truth. \n**Impact:** Higher chance of inconsistent messaging; harder to A/B-test alternate phrasings. \n**Recommendation:** Store the template in a constant at the top of the file and interpolate with `textwrap.dedent()`; expose a unit-tested `render_template()` helper. \n**Effort vs. Benefit:** Low / Medium \n\n### 4. Unused but Required SimpleTool Overrides \n**Insight:** `prepare_prompt()` and `format_response()` must be implemented even though the tool never calls a model (lines 215-221). \n**Evidence:** They return empty strings yet remain part of the public surface. \n**Impact:** Future readers assume model flow is possible; base-class churn could force pointless updates. \n**Recommendation:** Split a new `StatelessTransformTool` mixin that omits these methods; inherit from it instead of `SimpleTool`. \n**Effort vs. Benefit:** Medium / Medium \n\n### 5. Temperature & ModelCategory Misdirection \n**Insight:** The class advertises `TEMPERATURE_ANALYTICAL` and `ToolModelCategory.FAST_RESPONSE` (lines 91-98) but `requires_model()` is False. \n**Evidence:** Lines 95-98 admit the value is “Default, but not used”. \n**Impact:** Confuses diagnostics/telemetry that rely on these fields to pick an endpoint. \n**Recommendation:** Return `None` for temperature and model category when `requires_model()` is False; update telemetry consumers to skip nulls. \n**Effort vs. Benefit:** Low / Low \n\n---\n\n## Quick Wins \n- Trim `get_description()` to one short paragraph; move trigger rules to JSON. \n- Extract `_wrap_prompt_for_challenge()` string into a module-level constant. \n- Add a 3-line doc-test showing example input/output for instant onboarding. \n- Log at `DEBUG` when the tool is executed; currently only errors are logged. \n\n---\n\n## Long-Term Roadmap Suggestions \n1. Create a family of “meta-prompt” tools (challenge, reflect, devil’s-advocate) that share a common trigger manifest and template engine. \n2. Move prompt templates into a CMS or feature-flag store so product teams can tune language without code deploys. \n3. Instrument orchestrator trigger accuracy (false-positive/negative rates) to validate that the 7 bullet patterns still", "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 challenge tool implementation for flaws, inefficiencies, instability, and UX complexity risks.", "steps_taken": 1, "files_examined": [], "relevant_files": [ "C:\\Project\\EX-AI-MCP-Server\\tools\\challenge.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" } }