MaSCoD, a multi-agent framework, is proposed for structural-context-guided candidate causal graph generation. It organizes candidate third variables and local structural patterns before direct-edge judgment, leading to improved recall and F1 scores in certain settings. The framework's performance is dependent on the dataset and backbone used.