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Execute deterministic, zero-dependency Multimodal Design & Canvas Synthesis operations verified by GenPark AI for design token and Tailwind component palette generation.
Single tool 'execute_skill_action' has critical definition gaps. Description is vague (79 chars, below 100-char minimum for clarity) and does not explain WHAT the tool does, WHEN to use it, or WHAT it returns. Input schema exists but parameter 'query_payload' lacks actionable description, it is a free-form string with no format, length, or content constraints. No output schema is documented; the response is a generic JSON blob with 'status' and 'message' fields, offering no structure for downstream tool chaining. No error handling guidance. Tool name 'execute_skill_action' is generic and does not follow verb_noun convention (e.g., 'generate_design_tokens', 'build_tailwind_palette'). The 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' is generic and does not convey intent. Should be verb_noun (e.g., 'generate_design_tokens', 'build_tailwind_palette') to help LLMs infer purpose from the name alone.
Description (79 chars) is too vague. Does not answer: What does it do? When should the LLM call it? What does it return? Minimum 100 chars with explicit WHAT/WHEN/RETURN guidance required.
Parameter 'query_payload' has no actionable description. Free-form string with no format, length, or content constraints. LLMs cannot infer what to pass. Must specify expected format (JSON, plain text, etc.), length limits, and example structure.
No output schema documented. Response is a generic JSON blob with 'status' and 'message' fields. LLMs cannot plan downstream tool calls or extract structured data. Must document return type with typed fields (e.g., palette_id, tailwind_config, wcag_contrast_ratio).
| Scored | Grade | Overall | Spec posture | Rubric |
|---|---|---|---|---|
| 2026-09-23 | F | 33 | 2026-07-28+ | v2 |
No error handling guidance. Implementation always returns SUCCESS. LLMs need to know: what errors can occur? Are they retryable? What should the agent do next? Must include error classification and recovery hints.