Single tool 'execute_skill_action' has a generic, vague description that fails to explain WHAT the tool does, WHEN to use it, or what it returns. Description is 127 chars but reads like marketing copy ('deterministic, zero-dependency AI Voice Agent & Real-Time Audio Streaming operations verified by GenPark AI') rather than actionable guidance. Input schema is minimal: one string parameter 'query_payload' with no format constraints, length limits, or examples. No output schema documented, the tool returns JSON-stringified text with arbitrary fields ('status', 'tool', 'echo_query', 'verified_by', 'protocol'), but LLMs cannot plan downstream calls without knowing the response structure. Tool name 'execute_skill_action' is generic and does not start with a clear action verb (execute is weak; should be something like 'mask_audio_latency' or 'generate_filler_audio'). No error handling guidance, no parameter validation rules, no indication of side effects or idempotency. The underlying client.py logic (ConversationalFillerLatencyMasker) is never exposed or documented in the MCP interface.

• Rename tool to 'generate_filler_audio' or 'mask_latency_with_filler' to clearly signal intent. Start with a strong action verb.
• Rewrite description: 'Generates natural conversational filler phrases (e.g., "Let me check that", "Hmm, interesting") to mask latency during AI voice agent inference. Call when estimated inference latency exceeds 350ms. Returns selected filler phrase, category, and perceived latency savings.' (target 150-200 chars).
• Add parameter constraints to 'query_payload': describe expected format (e.g., 'User utterance or context string, 1-500 chars'), provide examples ('Can you analyze our cloud spend?'), and document what happens if empty or too long.
• Document output schema explicitly: 'Returns object with fields: inject_filler (boolean), filler_phrase (string or null), category (string: acknowledgement|computation|deep_reasoning|confirmation), estimated_latency_ms (number), latency_saved_perception_ms (number).'
• Add error handling: return structured errors for invalid inputs (e.g., 'query_payload must be 1-500 characters') with actionable guidance, not just 'status: success' for all cases.