Interconnection planning involving bi-directional converters (BdCs) is crucial for enhancing the reliability and robustness of hybrid alternating current (AC)/direct current (DC) microgrid clusters with high penetrations of renewable energy resources (RESs). However, challenges such as the non-convex nature of BdC efficiency and renewable energy uncertainty complicate the planning process. To address these issues, this paper proposes a tri-level BdC-based planning framework that incorporates dynamic BdC efficiency and a data-correlated uncertainty set (DcUS) derived from historical data patterns. The proposed framework employs a least-squares approximation to linearize BdC efficiency and constructs the DcUS to balance computational efficiency and solution robustness. Additionally, a fully parallel column and constraint generation algorithm is developed to solve the model efficiently. Numerical simulations on a practical hybrid AC/DC microgrid system demonstrate that the proposed method reduces interconnection costs by up to 21.8% compared to conventional uncertainty sets while ensuring robust operation under all considered scenarios. These results highlight the computational efficiency, robustness, and practicality of the proposed approach, making it a promising solution for modern power systems.