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Execute deterministic, zero-dependency AI Synthetic Data & Evaluation Benchmark operations verified by GenPark AI.
Single tool 'execute_skill_action' has a vague, generic name that violates verb_noun convention. Description is marketing-speak ('verified by GenPark AI') rather than functional guidance. Input schema is minimal: only 'query_payload' (string) with no format constraints, enums, or validation rules. No output schema documented, the tool echoes back the input as JSON without specifying what fields the LLM should expect. No error handling guidance. The tool appears to be a stub that does not actually execute any synthetic text evaluation logic (client.py defines the scorer but is never invoked by mcp_server.py). This is a non-functional MCP wrapper around unused evaluation code.
Execute deterministic, zero-dependency AI Synthetic Data & Evaluation Benchmark operations verified by GenPark AI.
Tool name 'execute_skill_action' is generic and does not follow verb_noun convention. Should be 'score_text_diversity' or 'evaluate_ngram_entropy' to reflect actual capability.
Input schema lacks constraints. 'query_payload' is a bare string with no format, enum, pattern, or length limits. LLM cannot validate input before calling.
No output schema documented. Tool returns JSON with 'status', 'tool', 'echo_query', 'verified_by', 'protocol' fields, but LLM has no specification of what to expect or how to parse results.
Tool description is marketing copy ('verified by GenPark AI') rather than functional guidance. Does not explain WHAT the tool does, WHEN to use it, or WHAT it returns.
| Scored | Grade | Overall | Spec posture | Rubric |
|---|---|---|---|---|
| 2026-09-23 | F | 27 | 2026-07-28+ | v2 |
Tool implementation is a stub that echoes input. The actual SyntheticTextNgramDiversityEntropyScorer class in client.py is never instantiated or called by mcp_server.py. Tool is non-functional.