Online charts are ubiquitous, yet remain systematically inaccessible to screen reader users. This paper presents the process for developing the MATCHA Chart Summarizer (MCS), an interactive, browser-based system, leveraging multimodal LLM’s to generate accessible spoken chart summaries and support natural-language querying. MCS aims to enhance functional comprehension and narrow the reasoning gap for screen reader users when interacting with data visualizations. Through a three-stage mixed-methods, user-based evaluation (N = 126 total), we conduct a needs assessment, design our prototype, and demonstrate that LLM-generated summaries can improve task accuracy and increase user confidence, informativeness, and usability. We also quantify how visualization flaws, such as missing labels or poor contrast, induce semantic drift in generated chart summaries. We contribute an accessible assistive NLP system, an impact-oriented evaluation framework, and evidence-based design recommendations for automated chart summarization.