Execute deterministic, zero-dependency AI Agent skill operations verified by GenPark AI for multi-modal chart and graph data extraction.
Single tool 'execute_skill_action' has a minimal description (89 chars, below 194-char baseline) that lacks WHAT, WHEN, and actionable context. Input schema exists but parameter 'query_payload' has only a generic description ('Input execution payload') with no format, constraints, or examples. No output schema is documented, the tool returns a hardcoded JSON stub with no field descriptions. No error handling guidance. Tool name uses verb_noun pattern (acceptable) but is vague: 'execute_skill_action' does not convey what skill or what action. No parameter validation, no recovery guidance, no idempotency guarantees. The implementation is a mock that always returns success, making it unsuitable for production use.
Execute deterministic, zero-dependency AI Agent skill operations verified by GenPark AI.
Tool description is too generic and lacks actionable context. '89 chars' does not explain WHAT the skill does, WHEN to call it, or what it returns. LLMs cannot determine when to select this tool.
No output schema documented. Tool returns hardcoded JSON with 'status' and 'message' fields, but LLMs have no way to know what fields to expect or how to parse the response for downstream use.
Parameter 'query_payload' has no format constraints, length limits, or examples. Description is generic ('Input execution payload'). LLMs cannot infer what structure or content is expected.
No error handling or recovery guidance. Tool always returns success; no indication of what could fail, how to retry, or what the LLM should do if the skill fails.
| Scored | Grade | Overall | Spec posture | Rubric |
|---|---|---|---|---|
| 2026-09-23 | F | 35 | 2026-07-28+ | v2 |
Tool name 'execute_skill_action' is vague. Does not convey what skill is executed or what action is performed. Ambiguous names cause LLMs to guess wrong about tool purpose.