DNA is a promising candidate for data storage due to its high density and long-term stability. However, accessing specific data, known as random access, is challenging. This process uses primers, short DNA segments that act as identifiers. Efficient random access depends on highquality primers, constrained by DNA structure. This paper introduces a method to generate primers that meet strict biochemical criteria, avoiding sequences that form problematic shapes. The proposed tool uses computational models to predict primer binding affinity and specificity, allowing users to adjust parameters for lab protocols, enhancing data retrieval efficiency and optimization.