MCP server for executing deterministic, zero-dependency AI Agent Operations verified by GenPark AI, specifically for delegated agentic checkout guardrail evaluation.
Single tool 'execute_skill_action' has a minimal description (89 chars, below 194-char baseline) that lacks WHAT, WHEN, and actionable context. Input schema is present but parameter 'query_payload' has only a generic description ('Input execution payload for this skill') with no format, constraints, or examples. No output schema is documented, the tool returns a hardcoded JSON string with no field descriptions. No error handling guidance, no idempotency declaration, no security context. The tool name 'execute_skill_action' is vague (does not start with a clear verb like 'run_', 'invoke_', or 'execute_guardrail_'); 'action' is ambiguous. No parameter validation, no recovery guidance. This is a stub implementation masquerading as a production tool.
Execute deterministic, zero-dependency AI Agent Operations verified by GenPark AI.
Tool description is too generic and lacks actionable context. '89 chars' is below the 194-char baseline. Does not explain WHAT the tool does, WHEN to use it, or what the LLM should expect. Baseline: 194 chars (p10=34, p90=392).
Parameter 'query_payload' has no format, length, or content constraints. Description is generic ('Input execution payload for this skill'). LLM cannot infer valid input structure, leading to hallucinated payloads.
No output schema documented. Tool returns hardcoded JSON with fields 'status' and 'message', but LLM has no way to know what fields to expect or how to parse the response. Violates pattern:tool requirement for documented return types.
Tool name 'execute_skill_action' is vague. Does not start with a clear action verb (e.g., 'run_', 'invoke_', 'evaluate_'). 'Action' is ambiguous, does it execute a guardrail check, a payment, a policy? Baseline: 90% of A+ tools start with action verb.
| Scored | Grade | Overall | Spec posture | Rubric |
|---|---|---|---|---|
| 2026-09-23 | F | 33 | 2026-07-28+ | v2 |
No error handling or recovery guidance. Tool always returns 'SUCCESS' regardless of input. LLM cannot distinguish valid execution from failure, and has no guidance on what to do if the tool fails.