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MCP server for deterministic, zero-dependency AI Code Review & Semantic Static Analysis operations for detecting flaky test timing non-determinism
Single tool 'execute_skill_action' has a minimal description (89 chars, below 194-char baseline) that lacks WHAT/WHEN/WHY context. Input schema is present but parameter 'query_payload' has only a generic description ('Input execution payload'). No output schema is documented, the tool returns a hardcoded JSON string with no field descriptions. No error handling guidance, no parameter constraints, no examples of valid payloads. The tool name does not clearly convey its purpose; 'execute_skill_action' is vague and generic. No evidence of idempotency, permission gates, or security considerations. This is a stub implementation, not production-ready.
Execute deterministic, zero-dependency AI Code Review & Semantic Static Analysis operations verified by GenPark AI.
Tool name 'execute_skill_action' is generic and does not start with a clear action verb. 'execute' + 'skill_action' is redundant and vague. Does not convey what the tool actually does (code review? test analysis? static analysis?). LLMs cannot infer intent from the name alone.
Parameter 'query_payload' lacks actionable description. 'Input execution payload for this skill' does not explain format, constraints, or valid values. No enum, pattern, or example. LLMs will guess at the structure and likely pass invalid payloads.
No output schema documented. Tool returns a hardcoded JSON object with 'status' and 'message' fields, but LLMs have no way to know what fields to expect or how to parse the response. Violates pattern:response-shaper.
| Scored | Grade | Overall | Spec posture | Rubric |
|---|---|---|---|---|
| 2026-09-23 | F | 29 | 2026-07-28+ | v2 |
Tool description (89 chars) is below the 194-char baseline and lacks WHAT/WHEN/WHY context. Does not explain when to call this tool vs. other code review tools, what prerequisites exist, or what the output represents. Too vague for LLM selection.
No error handling guidance. Tool always returns success; no indication of failure modes, retryability, or recovery steps. If the tool fails in production, LLMs have no guidance on what to do next.