EX MCP Server

{ "status": "calling_expert_analysis", "step_number": 1, "total_steps": 1, "next_step_required": false, "continuation_id": "13d3e400-7cb1-44d6-8953-2a296d5b2a1a", "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 `secaudit` tool is a well-intentioned security-audit orchestrator that inherits from a generic `WorkflowTool` base. It succeeds in providing a repeatable, step-wise OWASP-flavoured checklist and a rich prompt bundle for LLM-based security triage. However, the implementation is **over-engineered** (≈ 800 lines of mostly boiler-plate), **couples UI orchestration to security logic**, and **lacks run-time safety rails** (no time-outs, no circuit-breakers, no payload size guards). These traits make the component a future maintenance sink and a latent reliability risk once large mono-repos or recursive dependency trees are audited.\n\n---\n\n## Strategic Findings (Ordered by Impact)\n\n### 1. Excessive Configuration Surface & Schema Duplication \n**Insight:** 130-line field-description table + 70-line Pydantic model + 70-line JSON-schema builder re-declare the same 15 security parameters. \n**Evidence:** Lines 37-131 vs. 134-196 vs. 451-541. \n**Impact:** Every new parameter triples maintenance cost; schema drift inevitable. \n**Recommendation:** Collapse into a single `SecurityAuditParams` Pydantic model and auto-generate the JSON schema with `model_json_schema()`. \n**Effort vs. Benefit:** Low / High \n\n### 2. Hard-coded 6-Step Waterfall Prescribes Workflow Velocity \n**Insight:** `get_required_actions()` (lines 277-324) locks the CLI agent into an immutable 6-step sequence regardless of repo size or risk profile. \n**Impact:** Agents waste cycles on irrelevant steps (e.g., dependency scan on a static HTML site). \n**Recommendation:** Replace the `if/elif` ladder with a strategy registry (`Dict[AuditFocus, List[Check]]`) and let the first step prune the checklist based on detected stack & focus. \n**Effort vs. Benefit:** Medium / High \n\n### 3. No Guard-rails Against Run-away Token Usage \n**Insight:** Files are blindly embedded (line 367-369) and findings concatenated forever; no max-file-count, max-bytes, or max-steps cut-off. \n**Impact:** A single call on a 2 GB node_modules folder will OOM the host or exhaust token budget. \n**Recommendation:** Add `MAX_FILES=150`, `MAX_BYTES=5 MiB`, `MAX_STEPS=10` class constants and short-circuit with a warning status. \n**Effort vs. Benefit:** Low / High \n\n### 4. Mixed Presentation & Business Logic \n**Insight:** String templates such as `get_completion_next_steps_message()` (lines 601-619) contain CLI instructions, markdown, and status flags. \n**Impact:** UI text churn forces code re-test; hard to localize or re-skin. \n**Recommendation:** Externalise user-facing strings to a `secaudit.responses.yaml` and keep the tool returning pure data objects. \n**Effort vs. Benefit:** Low / Medium \n\n### 5. Silent Failure when Compliance Tag Unknown \n**Insight:** Validator (lines 191-194) only logs a warning for an invalid compliance tag and continues. \n**Impact:** Audit report may claim “GDPR review done” while having skipped controls. \n**Recommendation:** Raise `ValueError` instead of `logger.warning`; fail fast. \n**Effort vs. Benefit:** Low / High \n\n### 6. Inheritance Depth Hampers Unit Testing \n**Insight:** Seven hook methods (`should_call_expert_analysis`, `prepare_expert_analysis_context`, …) are spread across 200 lines solely to tweak base-class behaviour. \n**Impact:** To unit-test one variant you instantiate the whole 800-line class; mocks become brittle. \n**Recommendation:** Extract a `SecurityExpertGateway` service; leave the tool as a thin coordinator. \n**Effort vs. Benefit:** Medium / Medium \n\n---\n\n## Quick Wins \n- Delete `SECAUDIT_WORKFLOW_FIELD_DESCRIPTIONS` and reuse Field descriptions from the Pydantic model. \n- Replace 60-line `get_description()` string with a single concise sentence; append dynamic examples at run-time. \n- Add `@lru_cache` to `get_input_schema()`—schema never changes per session. \n- Convert `severity_groups` (line 410) to `defaultdict(list)` to remove four repetitive keys. \n- Swap `logger.warning` for `logger.info` on missing `security_scope`; it is not a warning-worthy event. \n\n---\n\n## Long-Term Road-map (If Tool Proves Traction) \n1. **Plug-in Architecture:** Let security teams drop in custom checks as `.py` or `.rego` files without touching the core. \n2. **Parallelisable Steps:** Allow agent to run dependency-check, secret-scan, and SCA in concurrent coroutines; aggregate results before step 5. \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 secaudit tool implementation for flaws, inefficiencies, instability, and UX complexity risks.", "steps_taken": 1, "files_examined": [], "relevant_files": [ "C:\\Project\\EX-AI-MCP-Server\\tools\\secaudit.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" } }