Software Reliability Engineering provides strategies for monitoring applications and detecting defects even in production environments. The Tricorder methodology addresses this challenge by generating resource-consumption profiles at runtime of a system under test and applying the DAMICORE unsupervised clustering algorithm to detect anomalies between a reference version and a new version. Although Tricorder has been validated in previous studies across multiple application domains, its practical use has been limited to ad hoc Python scripts, which require considerable manual effort to adapt to different scenarios. This paper presents TriAD (Tricorder-based Anomaly Detection), a reliability tool that encapsulates the Tricorder methodology within a web application with persistent storage, developed in accordance with the RefTEST reference architecture. An initial experiment using the JavaSort application demonstrates that TriAD achieves an 85% anomaly detection rate while correctly classifying 75% of non-defective runs. In addition, the tool reduces execution time by 7.5% for monitoring and 40% for analysis compared to the original script-based implementation. A demo video of the TriAD tool is available at: https://youtu.be/OOGH4Y-2db0.