Single tool 'execute_skill_action' has a minimal schema with only one parameter (query_payload: string). Description is present (94 chars) but vague, it claims to execute 'Spreadsheet, SQL & Business Intelligence operations' without specifying what the tool actually does, when to use it, or what it returns. The parameter lacks any format guidance, constraints, or examples. No output schema is documented. The tool name uses a generic verb ('execute') rather than a specific action (e.g., 'synthesize_sql_query'). No error handling, no recovery guidance, no parameter validation rules. The implementation is a stub that always returns success without actually executing anything. This violates 8+ core patterns from the rubric.

• Rename tool to 'synthesize_cte_sql_query' to clearly indicate it generates CTE SQL from natural language prompts.
• Expand tool description to 100-200 chars: 'Synthesize Common Table Expression (CTE) SQL queries from natural language business prompts. Supports PostgreSQL, MySQL, and T-SQL dialects. Returns synthesized SQL, validation status, and query plan URL.'
• Add parameter descriptions: query_payload should specify expected format (e.g., 'JSON object with keys: business_prompt (string), target_dialect (enum: POSTGRESQL|MYSQL|TSQL), optional: max_cte_depth (int 1-5)').
• Document output schema in tool description or as a separate schema field: {synthesis_id, target_dialect, synthesized_sql, cte_layers_count, contains_window_functions, semantic_validation_passed, query_plan_url, error (optional)}.
• Implement actual SQL synthesis logic or clearly document that this is a mock/stub server. If mock, add a note in the description: '[MOCK] This server returns example responses for testing. For production, integrate with GenPark AI synthesis engine.'