Single tool 'execute_skill_action' has a vague, generic description (89 chars) that fails to explain WHAT the tool does, WHEN to use it, or what it returns. The name uses 'execute' + 'action', both generic verbs that violate verb_noun clarity. Input schema is minimal (single string param with no format/constraint guidance). No output schema documented. No error handling, recovery guidance, or examples. The description 'Execute deterministic, zero-dependency AI Agent Governance & Observability operations' is marketing language, not actionable LLM guidance. Tool appears to be a stub/placeholder rather than a production-grade definition.

• Rename tool to a specific verb_noun pair that reflects the actual operation. E.g., 'enforce_dialog_boundary', 'validate_query_safety', or 'route_user_query', something that tells the LLM what happens when called.
• Rewrite description as: '[ACTION] [WHEN] [RETURN]. E.g., "Validates whether a user query falls within allowed topic boundaries. Call this before routing queries to domain-specific handlers. Returns a decision object with is_within_boundary (bool), violation_type (string), and deflection_response (string)."'
• Add format/constraint guidance to 'query_payload' parameter. E.g., 'The user query as a plain string (max 2000 chars). Do not include system instructions or metadata.'
• Document the output schema explicitly in the tool definition. Include field names, types, and descriptions for all response fields (status, message, guardrail_decision_id, etc.).
• Add error handling: return distinct error responses for invalid input, boundary violations, and service failures. Include actionable guidance: 'Query violates safety policy. Deflect to: "I can only assist with banking queries."'