Developed core automated musical score analysis features for the music-computing/amads repository, focusing on monophonic scores. Leveraging Python and expertise in algorithmic composition and music information retrieval, implemented boundary detection and segmentgestalt algorithms to identify local boundaries and analyze pitch and temporal relationships within scores. Introduced basic reliability tests to ensure the robustness of these new features, supporting downstream processes such as transcription, segmentation, and analysis pipelines. This work established a foundational layer for faster and higher-quality score analysis, enabling more efficient data extraction and analytics workflows in music computing applications, and strengthening the baseline for future enhancements.