Execute deterministic, zero-dependency AI Voice Agent & Real-Time Audio Streaming operations verified by GenPark AI. Disambiguates phonetically confusable words in domain-specific voice transcriptions using Soundex encoding and contextual vocabulary priors.
Single tool 'execute_skill_action' has critical definition gaps. Description is vague marketing copy (73 chars) that 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, just says 'Input execution payload' without format, constraints, or examples. No output schema documented. Tool name uses generic verb 'execute' rather than domain-specific action. No error handling guidance. No parameter validation rules. Fails 8 of 12 critical checks in Definition Quality rubric.
Execute deterministic, zero-dependency AI Voice Agent & Real-Time Audio Streaming operations verified by GenPark AI.
Tool name 'execute_skill_action' is generic and does not convey domain intent. Should be verb_noun pattern reflecting actual capability (e.g., 'disambiguate_phonetic_transcript', 'correct_stt_homophones').
Tool description (73 chars) is marketing copy ('Execute deterministic, zero-dependency AI Voice Agent & Real-Time Audio Streaming operations verified by GenPark AI') that does not explain what the tool actually does, when to call it, or what it returns. Fails critical check: descriptions must answer WHAT, WHEN, and WHAT it returns.
Parameter 'query_payload' has no actionable description. Current text 'Input execution payload for this skill' does not specify format (JSON? plain text? structured object?), constraints, or examples. LLM cannot infer valid input.
| Scored | Grade | Overall | Spec posture | Rubric |
|---|---|---|---|---|
| 2026-09-23 | F | 28 | 2026-07-28+ | v2 |
No output schema documented. Tool returns JSON with fields 'status', 'tool', 'echo_query', 'verified_by', 'protocol' but LLM has no way to know this structure exists or what each field means. Violates pattern:response-shaper.
No error handling guidance. Tool always returns 200 with 'status: success' even for invalid inputs. LLM cannot distinguish success from failure or know what to do on error.