EX MCP Server

{ "status": "calling_expert_analysis", "step_number": 1, "total_steps": 1, "next_step_required": false, "continuation_id": "0b98b512-19cf-4bcd-96bd-79b52e8db2b7", "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 `testgen` tool is a well-structured workflow orchestrator for AI-driven test generation, built on a solid inheritance hierarchy and clear separation of concerns. Its architecture promotes extensibility and consistent behavior across workflow tools. However, the implementation exhibits over-engineering in several areas—excessive configuration layers, speculative abstraction, and verbose documentation that obscures core logic—creating maintenance burden and cognitive overhead without proportional benefit. The tool’s UX complexity stems from an overly granular confidence model and forced step-wise progression that may frustrate users seeking rapid test generation.\n\n## Strategic Findings (Ordered by Impact)\n\n### 1. Over-Engineered Configuration & Documentation Layer\n**Insight:** 36-line field description dict (`TESTGEN_WORKFLOW_FIELD_DESCRIPTIONS`, lines 36-95) and 250-line schema builder indirection add verbosity without clear ROI. \n**Evidence:** \n```python\n\"step\": (\"What to analyze … after thinking carefully about what needs to be examined. \"\n \"Consider code structure, business logic, critical paths, edge cases …\")\n```\n**Impact:** Onboarding friction, slower refactors, and inflated codebase; future contributors must read 3× more text to grasp intent. \n**Recommendation:** Collapse descriptions to ≤120 chars each; inline them directly in Pydantic `Field(description=…)` and delete the dict. \n**Effort vs. Benefit:** Low / High\n\n### 2. Speculative Abstraction in Workflow Base\n**Insight:** `WorkflowTool` base class (imported line 31) appears to provide generic orchestration yet every significant behavior is overridden in `TestGenTool`, suggesting the abstraction was premature. \n**Evidence:** `prepare_prompt` returns empty string (line 574), `get_completion_status`, `get_completion_data_key`, etc. are all overridden. \n**Impact:** Cognitive indirection, harder to trace execution, risk of base-class changes breaking unrelated tools. \n**Recommendation:** Evaluate whether `WorkflowTool` truly offers reusable value; if not, flatten hierarchy and let `TestGenTool` own its logic. \n**Effort vs. Benefit:** Medium / High\n\n### 3. Granular Confidence Model Creates UX Drag\n**Insight:** Seven-level confidence enum (`exploring` → `certain`, lines 80-85) forces users into micro-gradations that don’t materially affect behavior except for the final “certain” skip. \n**Evidence:** Only `request.confidence == \"certain\"` has special handling (line 397); other values funnel into same action lists. \n**Impact:** Users must guess distinction between `very_high` vs `almost_certain`, slowing iteration; code paths proliferate without benefit. \n**Recommendation:** Collapse to three levels: `investigating`, `reviewing`, `certain`. \n**Effort vs. Benefit:** Low / Medium\n\n### 4. Mandatory Step-wise Progression Bottleneck\n**Insight:** Tool enforces sequential step lock-step (`step_number`, `total_steps`, `next_step_required`) even when the AI already has enough context to generate tests in one pass. \n**Evidence:** `get_test_generation_step_guidance` emits 20-line CAPITALIZED warnings (lines 489-524) threatening recursion violations. \n**Impact:** Latency inflation, token burn, user annoyance; contradicts “comprehensive” claim by throttling speed. \n**Recommendation:** Allow “fast-path” parameter that skips to final step when coverage goals are met; keep step-wise mode for complex unknown codebases. \n**Effort vs. Benefit:** Medium / High\n\n### 5. String-Based Status Proliferation\n**Insight:** Status strings (`test_generation_complete_ready_for_implementation`, line 407) are hard-coded and mapped through multiple dicts (lines 538-546), creating fragile string coupling. \n**Evidence:** Any rename requires touching 4+ locations; typos break downstream consumers. \n**Impact:** Refactor risk, silent failures, harder static analysis. \n**Recommendation:** Replace with `enum.StrEnum` constants exported from a single module. \n**Effort vs. Benefit:** Low / Medium\n\n## Quick Wins\n- Delete `TESTGEN_WORKFLOW_FIELD_DESCRIPTIONS` dict; move concise descriptions inline with Pydantic fields. \n- Replace seven-level confidence enum with three levels; migrate existing callers. \n- Introduce `TestGenStatus` enum for all magic strings; remove ad-hoc mappings. \n- Add `--fast` CLI flag that sets `total_steps=1` and short-circuits to completion when user already knows target files. \n\n## Long-Term Roadmap Suggestions\n1. **Flatten inheritance:** Evaluate concrete reuse of `WorkflowTool`; if <20 % code is shared, remove base class and let each tool be self-contained. \n2. **Streaming / incremental generation:** Support “live” test file emission as soon as each code unit is analyzed, reducing wait time. \n3. **", "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 testgen tool implementation for flaws, inefficiencies, instability, and UX complexity risks.", "steps_taken": 1, "files_examined": [], "relevant_files": [ "C:\\Project\\EX-AI-MCP-Server\\tools\\testgen.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" } }