<p>Visual simultaneous localization and mapping (SLAM) systems are increasingly applied in robotics and autonomous navigation but often pose significant challenges for researchers due to their inherent complexity, making learning and further development difficult. This work introduces an integrated toolbox and an evaluation framework to address these issues. By decomposing SLAM into five principal tasks, our approach enables researchers to systematically analyze intermediate outputs, identify potential issues, and optimize parameter settings effectively. The interactive user interfaces further enhance the ability of researchers to debug and assess algorithm performance efficiently. Additionally, correlation analysis using the minimum redundancy maximum relevance method uncovers meaningful relationships between algorithmic parameters and evaluation metrics, offering researchers systematic guidance for parameter tuning. These contributions facilitate more profound insights into SLAM systems and promote improved algorithm development.</p>