Single tool 'execute_skill_action' has a minimal description (79 chars) that lacks actionable context. Input schema is present but 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 appears to be a stub that always returns success, making it unsuitable for production use. Naming is verb-based but vague; 'execute_skill_action' does not convey what skill or what action.

• Document the output schema explicitly. Define what fields the response contains (e.g., status, message, result_data, execution_time) with types and descriptions so LLMs know what to expect.
• Expand the tool description to 100-200 characters explaining: What skill does this execute? When should an agent call it? What are the prerequisites? Example: 'Execute a GenPark AI skill for long-horizon context compaction. Call this to compress conversation history while preserving key facts. Requires a valid JSON query_payload with skill_id and parameters.'
• Add a detailed description to the 'query_payload' parameter specifying format (JSON object? string? base64?), required fields, and constraints. Example: 'JSON object with keys: skill_id (string), session_turn_count (int, 1-1000), raw_token_count (int, 100-1000000). See GenPark docs for full schema.'
• Implement real error handling. Return structured errors with error_code, error_message, and recovery_hint. Example: '{"status": "ERROR", "error_code": "INVALID_PAYLOAD", "message": "query_payload must be valid JSON", "recovery_hint": "Check payload format and retry."}'
• Add parameter validation and return actionable error messages. Validate query_payload format, required fields, and value ranges. Return specific errors like 'Invalid skill_id: must be alphanumeric' instead of generic success.