EX MCP Server

{ "status": "calling_expert_analysis", "step_number": 1, "total_steps": 1, "next_step_required": false, "continuation_id": "041a53c9-03da-4e17-ad59-188400b84e4b", "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 HealthTool is a lightweight, single-purpose diagnostic endpoint that surfaces runtime state (configured providers, model list, recent log tails) without external dependencies or side-effects. \nIts design is admirably minimal, but it is fragile under real-world ops load: blocking I/O, no size/time guards on log reads, silent failure swallowing, and no caching or back-pressure for large logs. These issues are tolerable while the server is a dev-time MCP bridge, yet they become availability and UX liabilities once the service is embedded in customer-facing workflows or high-throughput automation.\n\n## Strategic Findings (Ordered by Impact)\n\n### 1. Blocking I/O in Async Context\n**Insight:** `run()` (line 61) and `_tail_file()` (line 50) perform synchronous disk reads inside the async `execute()` path (line 87). \n**Evidence:** `path.open(\"r\").readlines()` on line 55 blocks the event-loop for the duration of the read. \n**Impact:** Under concurrent health probes (or large log files) the single-threaded asyncio loop stalls, inflating P99 latency and triggering false-positive “unhealthy” verdicts from upstream load-balancers. \n**Recommendation:** Convert `_tail_file()` to `aiofiles` or pre-read logs in a thread pool; cap file size and line count. \n**Effort vs. Benefit:** Low effort, High payoff (immediate latency & stability win).\n\n### 2. Silent Failure Swallowing\n**Insight:** All file and registry exceptions are caught and ignored (lines 58, 100-102). \n**Evidence:** `except Exception: return []` and `except Exception as e: … str(e)` return opaque JSON with no stack trace or structured code. \n**Impact:** Operators lose visibility into mis-configured env vars, permission errors, or corrupted JSONL files; downstream dashboards show “healthy” while the server is partially blind. \n**Recommendation:** Log structured errors with `logger.exception()` and expose a numeric “health_status” field (0/1) so callers can distinguish “healthy” from “degraded”. \n**Effort vs. Benefit:** Low effort, Medium payoff (observability & debuggability).\n\n### 3. Unbounded Log Tail\n**Insight:** `tail_lines` defaults to 50 but is not capped; callers may request 10 000 lines (line 62). \n**Evidence:** No upper bound check before `lines[-n:]` slice on line 57. \n**Impact:** A single malicious or mis-programmed client can allocate hundreds of MB, exhaust memory, and crash the MCP server. \n**Recommendation:** Enforce `max_tail=500` in schema and clamp input; stream only the required bytes instead of `readlines()`. \n**Effort vs. Benefit:** Low effort, High payoff (DoS protection).\n\n### 4. Hard-coded Relative Log Paths\n**Insight:** Log location falls back to `.logs/*.jsonl` relative to CWD (lines 68-69). \n**Evidence:** `Path(\".logs/…\")` resolves to whatever directory the server was started in—often not the project root under systemd, Docker, or IDE runners. \n**Impact:** Health checks return empty tails in production containers, leading to false assumptions about system activity. \n**Recommendation:** Resolve paths against a configurable `LOG_DIR` env var with an absolute default (`/var/log/ex-ai-mcp/`). \n**Effort vs. Benefit:** Low effort, Medium payoff (portability & prod hygiene).\n\n### 5. No Caching or Staleness Checks\n**Insight:** Every call re-enumerates providers and re-reads logs. \n**Evidence:** `ModelProviderRegistry.get_available_*` and `_tail_file()` invoked on each request (lines 64-72). \n**Impact:** Registry may hit cloud APIs or disk repeatedly, amplifying latency and rate-limit usage; large JSONL tails re-read each time. \n**Recommendation:** Cache provider list for 30 s; use inotify/watchdog or simple TTL cache for log tails. \n**Effort vs. Benefit:** Medium effort, Medium payoff (scales with probe frequency).\n\n### 6. UX Complexity: Raw JSONL to End-Users\n**Insight:** Tool returns raw log lines as a list of strings (line 77-78). \n**Evidence:** Consumers (humans or LLMs) must parse JSONL themselves and correlate timestamps. \n**Impact:** Chat interfaces become noisy; operators need extra tooling to make sense of health output. \n**Recommendation:** Offer optional `format=\"summary\"` that returns counts (error/warn/info) and last timestamp instead of full lines. \n**Effort vs. Benefit:** Low effort, Medium payoff (usability).\n\n## Quick Wins\n- Add `max_tail=500` clamp in `get_input_schema()` and enforce it in `run()`. \n- Replace `open()` with `aio", "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 health tool implementation for flaws, inefficiencies, instability, and UX complexity risks.", "steps_taken": 1, "files_examined": [], "relevant_files": [ "C:\\Project\\EX-AI-MCP-Server\\tools\\health.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" } }