Execute deterministic, zero-dependency AI Agent Operations verified by GenPark AI for product pricing normalization with currency conversion and tax calculation based on geographic location.
Single tool with minimal definition quality. Tool name 'execute_skill_action' is generic and violates verb_noun clarity (pattern:tool). Description is 104 chars but vague, does not explain WHAT the skill does, WHEN to use it, or what it returns. Input schema exists but parameter 'query_payload' lacks meaningful description ('Input execution payload' is circular). No output schema documented. No error handling guidance. Tool appears to be a pass-through wrapper with no actual business logic visible, implementation just returns a hardcoded success message regardless of input.
Execute deterministic, zero-dependency AI Agent Operations verified by GenPark AI.
Generic tool name 'execute_skill_action' does not convey specific action. Should be verb_noun (e.g., 'normalize_product_pricing', 'calculate_tax_nexus', 'convert_currency'). LLMs cannot infer intent from 'execute_skill_action'.
Tool description is vague and does not explain what the skill actually does. 'Execute deterministic, zero-dependency AI Agent Operations verified by GenPark AI' is marketing language, not a functional description. Missing: WHAT does it compute? WHEN should it be called? WHAT does it return?
Parameter 'query_payload' description is circular ('Input execution payload for this skill'). Does not explain format, expected content, or constraints. LLM cannot determine what to pass without trial and error.
No output schema documented. Tool returns hardcoded JSON with 'status' and 'message' fields, but LLM has no way to know this. Missing documentation of return structure, field types, and semantics.
| Scored | Grade | Overall | Spec posture | Rubric |
|---|---|---|---|---|
| 2026-09-23 | F | 33 | 2026-07-28+ | v2 |
Tool implementation is a stub, always returns success regardless of input. No actual GeoIP, currency conversion, or tax calculation logic is wired to the MCP tool. The client.py contains the real logic but is not exposed via MCP.