Single tool 'execute_skill_action' has a minimal description (89 chars) that lacks actionable context. The input schema is present but the parameter 'query_payload' has only a generic description ('Input execution payload for this skill'). No output schema is documented, the tool returns a hardcoded JSON string with no structure definition. No error handling guidance, no parameter constraints, and no indication of what 'deterministic, zero-dependency AI Agent Operations' actually means. The tool name is vague (execute_skill_action could mean anything) and does not follow verb_noun conventions clearly. No evidence of idempotency, side effects, or recovery paths.

• Document the output schema explicitly. Define all fields returned (status, message, data, etc.) with types and descriptions so the LLM knows what to expect and can chain calls.
• Expand tool description to 100-200 chars explaining: What does this tool do? When should the LLM call it? What does it return? Example: 'Execute a GenPark voice orchestration skill. Returns turn-taking action (BARGE_IN_INTERRUPT, YIELD_TO_USER, CONTINUE_STREAMING) and buffer directives for real-time voice agents.'
• Add constraints and format guidance to query_payload parameter. Specify: Is it JSON? Plain text? What fields does it expect? Example: 'JSON object with keys: vad_energy_db (float), silence_duration_ms (int), agent_speaking (bool).'
• Rename tool to a more specific verb_noun pattern. Instead of 'execute_skill_action', use 'orchestrate_voice_turn' or 'get_turn_taking_action' to make intent clear from the name alone.
• Add error handling guidance. Document failure modes (invalid payload, timeout, service unavailable) and tell the LLM what to do: retry, ask user for clarification, or escalate.