EX MCP Server

{ "status": "calling_expert_analysis", "step_number": 1, "total_steps": 1, "next_step_required": false, "continuation_id": "a93030cb-449c-49ff-b4a0-8d09e166bb8a", "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 version tool is a lightweight, read-only utility that cleanly surfaces build and runtime metadata. \nIts architecture is sound—no external model calls, minimal I/O, and graceful degradation when offline. \nHowever, it embeds three systemic liabilities: (1) hard-wired GitHub URL that will break on repo rename or fork, (2) runtime import side-effects that couple the tool to the running server singleton, and (3) provider-configuration enumeration that duplicates logic already owned by the registry. These issues are low-risk today but become blockers when the project scales to multiple distributions, containers, or private deployments.\n\n## Strategic Findings (Ordered by Impact)\n\n### 1. Brittle Update-Check Endpoint\n**Insight:** Line 93 hard-codes “BeehiveInnovations/zen-mcp-server”; any repo move, fork, or enterprise mirror invalidates the check and logs a warning that users cannot suppress. \n**Evidence:** \n```python\ngithub_url = \"https://raw.githubusercontent.com/BeehiveInnovations/zen-mcp-server/main/config.py\"\n``` \n**Impact:** Silent feature loss in forks; support burden increases when users see perpetual “check failed” messages. \n**Recommendation:** Move URL to `config.py` or an env var with a fallback “disable” flag; inject via DI in `__init__`. \n**Effort vs. Benefit:** Low / High\n\n### 2. Tight Runtime Coupling to Server Singleton\n**Insight:** Lines 203-206 import the live `server` module at runtime to extract client context, assuming the process is an MCP server and not a CLI or test runner. \n**Evidence:** \n```python\nimport server as server_module\nclient_info = get_client_info_from_context(server_module.server)\n``` \n**Impact:** Prevents reuse of the tool in offline scripts, unit tests, or future headless distributions; import may raise `AttributeError` or circular imports as the codebase grows. \n**Recommendation:** Accept an optional `context` parameter in `execute` and let the caller inject client metadata; keep the tool pure. \n**Effort vs. Benefit:** Medium / High\n\n### 3. Duplicate Provider-Introspection Logic\n**Insight:** Lines 227-263 re-implement provider enumeration and status formatting that the `ModelProviderRegistry` already exposes. \n**Evidence:** Local lists `provider_types`/`provider_names` must be kept in sync with registry additions. \n**Impact:** Double source of truth; missing or reordered entries produce misleading “not configured” messages. \n**Recommendation:** Add `registry.get_provider_summary()` returning a list of `(name, configured)` tuples; let the tool simply iterate and render. \n**Effort vs. Benefit:** Low / Medium\n\n### 4. Version Parsing Reinvents Packaging Standards\n**Insight:** Lines 34-55 implement custom tuple parsing instead of using `packaging.version.Version`, which handles pre-releases, local segments, and edge cases. \n**Impact:** Future versions like “1.2.3b1” or “1.2.3+gpu” will mis-compare; silent logic bug. \n**Recommendation:** Replace `parse_version` with `packaging.version.parse`; drop bespoke code. \n**Effort vs. Benefit:** Low / Medium\n\n### 5. Silent Failure on Missing urllib\n**Insight:** Lines 16-22 set `HAS_URLLIB = False` if import fails, but urllib is part of CPython stdlib since 2.x; the guard is unnecessary and signals an environment that is effectively unsupported. \n**Impact:** Code clutter; future readers waste time wondering when urllib could be absent. \n**Recommendation:** Remove the conditional import and the flag; let the import raise if the runtime is truly broken. \n**Effort vs. Benefit:** Low / Low\n\n## Quick Wins\n- Replace `parse_version` with `packaging.version.parse` (one-line change). \n- Extract GitHub URL to `config.GITHUB_RAW_URL` with env override. \n- Delete `HAS_URLLIB` branch and associated log noise. \n- Add unit test that calls `VersionTool.execute({})` in isolation to prove no import side-effects.\n\n## Long-Term Roadmap Suggestions\n- Introduce a `SystemInfoPort` interface so tools can receive host metadata (client, paths, feature flags) without importing the server. \n- Centralise all “distribution” metadata (repo URL, docs URL, issue tracker) in a single `DistributionManifest` loaded at start-up; tools read from there. \n- Adopt a plugin-style tool loader so version, health, and similar utilities are registered declaratively and remain side-effect free.\n\n## Next Steps\n1. Open PR that externalises the GitHub URL and removes the urllib guard—low risk, immediate payoff. \n2. Add isolated unit test coverage for `VersionTool.execute`; enforce no server import in CI. \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 version tool implementation for flaws, inefficiencies, instability, and UX complexity risks.", "steps_taken": 1, "files_examined": [], "relevant_files": [ "C:\\Project\\EX-AI-MCP-Server\\tools\\version.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" } }