EX MCP Server

{ "status": "calling_expert_analysis", "step_number": 1, "total_steps": 1, "next_step_required": false, "continuation_id": "73fdd604-93f3-40a3-b855-1230b7500835", "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 `provider_capabilities` tool is a lightweight, read-only diagnostic helper that surfaces environment readiness without exposing secrets. It is architecturally sound for its narrow purpose, but the current implementation couples itself to a hard-coded provider list, relies on a fragile 90-second “bucket” cache, and silently swallows all registry failures—introducing stealth UX degradation and minor operational risk as the MCP server grows beyond two providers.\n\n## Strategic Findings (Ordered by Impact)\n\n### 1. Hard-Coded Provider Coupling\n**Insight:** Lines 73-84 enumerate exactly two providers (KIMI/GLM) and their bespoke keys; any new provider requires editing this file. \n**Evidence:** \n```python\n\"KIMI_API_KEY_present\": present(\"KIMI_API_KEY\"),\n\"GLM_API_KEY_present\": present(\"GLM_API_KEY\"),\n...\n``` \n**Impact:** New providers or renamed env-vars force code changes, defeating the “plugin” spirit of the broader tool-registry design. \n**Recommendation:** Shift to a convention-based scan: `*_API_KEY`, `*_API_URL`, plus an optional allow-list in settings. \n**Effort vs. Benefit:** Medium / High\n\n### 2. Silent Failure Anti-Pattern\n**Insight:** The `try/except: pass` on lines 88 & 100 hides registry or import errors, so users see an empty tools list with no indication of mis-configuration. \n**Evidence:** \n```python\nexcept Exception:\n pass\n``` \n**Impact:** Troubleshooting onboarding issues becomes guess-work; failures accumulate unseen. \n**Recommendation:** Log the exception at `WARNING` level and return `tools_error` field in the JSON payload. \n**Effort vs. Benefit:** Low / High\n\n### 3. Time-Bucket Cache Instability\n**Insight:** The 90-second “time//90” cache key (lines 58-65) is shared across all instances, has no TTL eviction, and is reset on any import error, making cache hits non-deterministic. \n**Evidence:** \n```python\ncache_key = f\"pcap:{int(time()//90)}\"\n``` \n**Impact:** Repeated calls in the same minute may still miss cache; concurrency could corrupt the global. \n**Recommendation:** Remove the cache entirely (env inspection is nanoseconds) or replace with an in-memory TTL cache with explicit lock. \n**Effort vs. Benefit:** Low / Medium\n\n### 4. UX Parameter Overload\n**Insight:** Three boolean flags (`include_tools`, `show_advanced`, `invalidate_cache`) expose internal mechanics that most callers never vary. \n**Impact:** API surface is larger than necessary; users must learn semantics of “advanced” vs “core”. \n**Recommendation:** Collapse to a single `detail` enum (`core | all`) and drop the cache invalidation flag once cache is removed. \n**Effort vs. Benefit:** Low / Medium\n\n### 5. Missing Security Sanity Check\n**Insight:** While secrets are not emitted, the tool happily reports key *presence* and full URL strings. \n**Impact:** Reveals infrastructure details to any client with access to the MCP endpoint; aids reconnaissance. \n**Recommendation:** Allow-list env-keys via configuration; redact URL paths if they contain internal hosts. \n**Effort vs. Benefit:** Medium / Medium\n\n## Quick Wins\n- Replace `try/except: pass` with `logger.warning(\"ToolRegistry failed\", exc_info=True)` \n- Delete the home-grown cache block (lines 55-66) – cost is negligible \n- Add unit test asserting JSON shape and non-leakage of real keys \n- Document the expected env-var naming convention in README so new providers need zero code changes\n\n## Long-Term Roadmap Suggestions\n1. Introduce a `ProviderManifest` pydantic model that declares required env-vars per provider; registry auto-discovers manifests and feeds the capabilities tool. \n2. Surface last-successful-call timestamps and health status for each provider endpoint (opt-in) to turn the tool into a mini health-check dashboard. \n3. Publish the JSON schema for the capabilities payload as an OpenAPI example so IDE plugins can auto-generate client bindings.\n\n## Next Steps\n1. Refactor env-summary logic to iterate over `*_API_KEY` pattern instead of hard-coded list (Medium effort, High payoff). \n2. Remove or replace the time-bucket cache and add structured logging for registry failures (Low effort, High payoff). \n3. Write a single integration test that spins up the MCP server with dummy env-vars and asserts the returned JSON contains expected keys but no secrets (Low effort, guards against regression).", "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 provider_capabilities tool implementation for flaws, inefficiencies, instability, and UX complexity risks.", "steps_taken": 1, "files_examined": [], "relevant_files": [ "C:\\Project\\EX-AI-MCP-Server\\tools\\provider_capabilities.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" } }