Execute deterministic, zero-dependency Multimodal Design & Canvas Synthesis operations verified by GenPark AI. Normalizes SVG geometry for brand assets.
Single tool 'execute_skill_action' has a minimal description (97 chars) that lacks actionable context for LLM selection. 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 structure definition. No error handling, no parameter validation guidance, and no indication of what 'Multimodal Design & Canvas Synthesis operations' actually entails. The tool name does not follow verb_noun convention (should be something like 'normalize_svg_geometry' or 'synthesize_design'). Tool definition is visible in code but implementation is a stub that always returns success, making it unsuitable for production use.
Execute deterministic, zero-dependency Multimodal Design & Canvas Synthesis operations verified by GenPark AI.
Tool name 'execute_skill_action' violates verb_noun convention and is too generic. Does not convey what action is executed. Should be 'normalize_svg_geometry', 'synthesize_design', or similar specific action.
Parameter 'query_payload' has no format specification, constraints, or examples. Description is generic ('Input execution payload for this skill'). LLM cannot determine what structure or content is expected.
No output schema documented. Tool returns hardcoded JSON with 'status' and 'message' fields, but LLM has no way to know what fields to expect or how to parse results for downstream operations.
Tool description (97 chars) is too vague. 'Multimodal Design & Canvas Synthesis operations verified by GenPark AI' does not explain WHEN to use this tool, WHAT it actually does, or WHAT it returns. Lacks actionable context for LLM selection.
| 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; no indication of failure modes, validation errors, or what the LLM should do if execution fails.