At present, with the development of Next-generation Sequencing Technology (NGS), new MSI detection methods and software tools are emerging around high-throughput sequencing data. However, existing methods have problems that they need paired normal tissue sequencing data for reference or a large number of other microsatellite stable (MSS) samples’ normal tissue sequencing data to construct baselines. And this will cause the inconvenience to useness. Regarding the issue above, a new MSI detection model based on the information entropy theory for tumor tissue sequencing data is proposed in this study. First, built on original detection software MSlsensor1.1, the MSI detection module based on only tumor tissue sequencing data is proposed and optimized. The augmented software can perform MSI detection for tumor-normal paired sequencing data and single tumor sequencing data. Second, benchmark performance is evaluated on the extended module. For this module, the software performance indicators were evaluated using exon sequencing data of samples. The results show that the performance of the detection module with information entropy theory for single tumor sequencinge data is better, which provides theoretical basis and technical support for the subsequent iteration of more complex mutation signal detection process.