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Execute deterministic, zero-dependency Voice Telephony & Speech Agent operations verified by GenPark AI. Dispatches post-call CRM actions including lead scoring, sentiment analysis, and HubSpot deal creation.
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 of valid payloads. No output schema is documented, the tool returns a hardcoded success message with no field definitions. No error handling guidance, no parameter validation rules, and no indication of what 'deterministic, zero-dependency Voice Telephony & Speech Agent operations' actually entails. The tool name uses a generic verb ('execute') rather than a domain-specific action verb. No security considerations documented despite WRITE risk classification.
Execute deterministic, zero-dependency Voice Telephony & Speech Agent operations verified by GenPark AI.
Parameter 'query_payload' lacks format specification and validation rules. Description does not explain what constitutes a valid payload, expected structure, or examples. LLMs cannot infer correct input format.
No output schema documented. Tool returns hardcoded JSON with 'status' and 'message' fields, but LLMs have no way to know what fields to expect or how to parse the response for downstream operations.
Tool description (97 chars) is vague and domain-specific jargon ('Voice Telephony & Speech Agent operations verified by GenPark AI') does not explain WHEN to call this tool or WHAT it actually does. Lacks actionable context for LLM selection.
Tool name 'execute_skill_action' uses generic verb 'execute' instead of domain-specific action verb (e.g., 'dispatch_crm_action', 'create_lead_record'). LLMs cannot infer intent from the name alone.
| Scored | Grade | Overall | Spec posture | Rubric |
|---|---|---|---|---|
| 2026-09-23 | F | 35 | 2026-07-28+ | v2 |
No error handling guidance. Tool always returns success; no indication of failure modes, retryability, or recovery steps. LLMs cannot distinguish between actual success and mock responses.