MCP server for executing deterministic, zero-dependency AI Voice Agent & Real-Time Audio Streaming operations with SIP/PSTN telephony lifecycle management, DTMF digit recognition, and call transfer handoffs
Single tool 'execute_skill_action' has a vague, marketing-focused description that does not explain what the tool actually does or when to use it. Input schema is minimal (single string parameter with no format/constraint guidance). No output schema documented, the tool returns a JSON echo with no structure definition for downstream agents. No error handling guidance, no parameter validation rules, no security considerations. The tool name uses 'execute' (generic verb) rather than a domain-specific action like 'route_call' or 'process_dtmf'. The description conflates multiple concerns (voice agents, audio streaming, verification) without clarity on what this specific tool does. Baseline: A+ tools average 194 chars in description and 72 chars per param; this tool has ~120 chars description and ~60 chars param description, but lacks actionable detail.
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 like 'route_call', 'process_dtmf', or 'transfer_call' to help LLMs infer purpose.
Description is marketing-focused ('verified by GenPark AI') rather than functional. Does not explain: What does the tool do? When should the LLM call it? What does it return? What are prerequisites?
Input parameter 'query_payload' is a free-form string with no format, length, or content constraints. No enum, pattern, or examples. LLMs will hallucinate invalid payloads.
No output schema documented. Tool returns JSON with fields 'status', 'tool', 'echo_query', 'verified_by', 'protocol', but LLMs have no formal definition of these fields, types, or when they appear.
| Scored | Grade | Overall | Spec posture | Rubric |
|---|---|---|---|---|
| 2026-09-23 | F | 32 | 2026-07-28+ | v2 |
No error handling guidance. Tool always returns 'success' status even for invalid input. LLMs cannot distinguish success from failure or know what to do next on error.