MCP server for executing deterministic, zero-dependency Multimodal Design & Canvas Synthesis operations for responsive wireframe component layout 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 hardcoded response '"status": "SUCCESS"' is not formally specified. Tool name uses 'execute' (generic verb) rather than domain-specific action like 'generate_wireframe' or 'synthesize_layout'. No error handling guidance, no examples of valid payloads, no indication of idempotency or side effects. The client.py and example_usage.py files show the tool should generate wireframe layouts, but this intent is completely absent from the MCP tool definition itself.
Execute deterministic, zero-dependency Multimodal Design & Canvas Synthesis operations verified by GenPark AI.
Tool name 'execute_skill_action' is generic and does not reflect domain intent. Should be 'generate_responsive_wireframe', 'synthesize_layout', or similar verb_noun pattern matching the actual capability shown in client.py.
Parameter 'query_payload' has no actionable description. It is a free-form string with no format, length, or content constraints. LLMs cannot infer what to pass. Should specify: expected JSON structure, required fields, example payload, or link to schema.
Tool description (79 chars) is too vague. Does not answer: What does it do? When should I call it? What does it return? Current text 'Execute deterministic, zero-dependency Multimodal Design & Canvas Synthesis operations verified by GenPark AI' is marketing language, not functional documentation.
| Scored | Grade | Overall | Spec posture | Rubric |
|---|---|---|---|---|
| 2026-09-23 | F | 32 | 2026-07-28+ | v2 |
No output schema documented. Tool returns hardcoded JSON with 'status' and 'message' fields, but LLMs have no formal specification of return structure, field types, or what data is available. Should document: response fields, types, and example output.
No error handling guidance. Tool always returns 'SUCCESS' status; no indication of failure modes, retryability, or recovery steps. LLMs cannot distinguish transient errors from permanent failures.