EX MCP Server

{ "status": "calling_expert_analysis", "step_number": 1, "total_steps": 1, "next_step_required": false, "continuation_id": "7480bb7b-86e3-451a-bee8-27f18f728cab", "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 docgen tool is a self-contained workflow orchestrator that guides a CLI agent through systematic code-documentation. While it enforces disciplined, file-by-file coverage and provides rich configuration knobs, the implementation is over-engineered: 650+ lines of mostly boiler-plate, inheritance hooks, and string templating that duplicate base-class behaviour, creating a high maintenance surface with no clear runtime benefit. The UX is also high-friction—users must walk through N+1 discrete steps—and the tool has no visibility into nested directories, multi-language repos, or existing doc generators (Sphinx, Javadoc, DocC, etc.), limiting its usefulness at scale.\n\n## Strategic Findings (Ordered by Impact)\n\n### 1. Excessive Configuration & Inheritance Layers\n**Insight:** 40+ field descriptors, 15+ hook methods, and a 300-line schema builder are used to describe a simple “discover → document each file → finish” flow. \n**Evidence:** Lines 38-114 (field text), 224-301 (schema builder), 518-647 (hook overrides). \n**Impact:** Onboarding cost rises (devs must learn the meta-framework), and every future change touches multiple mirrored definitions. \n**Recommendation:** Collapse to a single Pydantic model with plain field descriptions; drop all hook overrides that merely rename keys. \n**Effort vs. Benefit:** Low effort, High payoff.\n\n### 2. Single-Directory Restriction\n**Insight:** Required actions (lines 308-318) explicitly limit discovery to “current directory (not nested)”, silently ignoring sub-modules. \n**Evidence:** “Discover ALL files in the current directory (not nested)”. \n**Impact:** Any real-world repo will need manual iteration per folder or external scripting, defeating the “automated” claim. \n**Recommendation:** Replace flat glob with `pathlib.rglob('*.py')` (or language-aware equivalent) and track visited paths in state. \n**Effort vs. Benefit:** Low effort, High payoff.\n\n### 3. High-Friction Step-wise UX\n**Insight:** Users must issue one CLI call per file (+1 discovery step); no batch or head-less mode exists. \n**Evidence:** `get_required_actions` enforces per-file confirmation; `handle_work_completion` blocks early exit. \n**Impact:** Docs for a 50-file package require 51 interactive prompts—unworkable in CI or large codebases. \n**Recommendation:** Provide a `--auto` flag that loops internally and emits a single summary; keep interactive mode optional. \n**Effort vs. Benefit:** Medium effort, High payoff.\n\n### 4. No Integration with Language Ecosystem Tools\n**Insight:** Tool hard-codes comment styles (line 45) but does not parse existing Sphinx/Google/NumPy docstrings, nor does it emit formats that `mkdocs`, `javadoc`, `swift-doc`, etc. can consume. \n**Impact:** Generated docs live as inline comments only; no cross-references, HTML, or type-linked output. \n**Recommendation:** Add output adapters (Markdown, reST, DocC, etc.) and ingest existing docstrings to avoid duplication. \n**Effort vs. Benefit:** Medium effort, Medium payoff.\n\n### 5. Bug-Freeze Policy Creates Deadlock Risk\n**Insight:** Lines 69-73 mandate an immediate halt if *any* bug is spotted, waiting for user confirmation. \n**Impact:** A one-line typo in a third-party file can block the entire documentation run; agent may loop indefinitely. \n**Recommendation:** Log issues to a “docgen-issues.md” and continue by default; add `--halt-on-bug` opt-in flag. \n**Effort vs. Benefit:** Low effort, Medium payoff.\n\n### 6. Hidden Scalability Ceiling\n**Insight:** All file content is embedded into prompts every step; no diffing, caching, or incremental mode. \n**Evidence:** `prepare_step_data` stuffs full file list into context each call. \n**Impact:** O(N²) token usage; large repos will hit context limits and incur unnecessary cost. \n**Recommendation:** Cache file hashes, document only changed files, stream diffs to the model. \n**Effort vs. Benefit:** High effort, High payoff (essential for monorepos).\n\n## Quick Wins\n- Replace flat-directory glob with recursive scan (3-line change). \n- Add `--auto` CLI flag to skip per-step prompts; loop inside `execute_workflow`. \n- Delete unused hook methods that only rename JSON keys (lines 518-647). \n- Emit a single “issues-found.md” instead of hard-stopping on bugs. \n- Document the existing JSON schema in README—users currently reverse-engineer prompts.\n\n## Long-Term Roadmap Suggestions\n1. Incremental mode: store last-run git hash → doc only changed files. \n2. Plugin architecture for language parsers (Python, TS, Swift,", "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 docgen tool implementation for flaws, inefficiencies, instability, and UX complexity risks.", "steps_taken": 1, "files_examined": [], "relevant_files": [ "C:\\Project\\EX-AI-MCP-Server\\tools\\docgen.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" } }