Single tool 'execute_skill_action' has a minimal description (97 chars) that lacks actionable context. The input schema is present but extremely sparse: only one parameter 'query_payload' (string type, minimal description). No output schema is documented, the tool returns a hardcoded JSON string with no structure definition. The tool name does not follow verb_noun convention (should be 'execute_skill' or similar). No error handling guidance, no parameter constraints, no indication of what 'query_payload' should contain or how to format it. The implementation is a stub that always returns success, making it unsuitable for production use.

• Add a detailed output schema documenting all fields returned by the tool (e.g., status, message, result_data, execution_time). Include field types and descriptions so LLMs know what to extract.
• Expand the tool description to 100-200 chars explaining: (1) What the tool does in concrete terms, (2) When to use it vs. other tools, (3) What inputs it expects, (4) What it returns. Example: 'Execute a predefined skill operation. Pass a JSON payload describing the operation type and parameters. Returns status, result data, and execution metadata.'
• Add constraints and format guidance to 'query_payload': specify whether it expects JSON, plain text, or a specific schema; provide an example structure or enum of valid operation types.
• Implement actual error handling: validate query_payload format, return specific error messages (e.g., 'Invalid JSON in query_payload', 'Unknown operation type'), and guide recovery (e.g., 'Call list_available_skills to see valid operations').
• Rename tool to follow verb_noun convention, e.g., 'execute_skill' or 'run_skill_operation', and clarify the domain (PII masking, redaction, etc.) in the description.