One of the main capabilities of Atom Probe Tomography (APT) is the ability to not only identify but characterise early stages of precipitation at scales that are not achievable by other techniques. The most popular method, based on the Density-Based Spatial Clustering of Applications with Noise (DBSCAN), is used extensively in many branches of research. It is, however, not common to report all the steps which led to selection of certain parameters used in the analysis. This makes it difficult for researchers to reproduce the results and can lead to errors, and is a problem that the APT community needs to address. In this work, a simple and open source tool which tries to answer this issue is presented. PosgenPy emphasises the need for strong randomisation steps and systematic reporting of the parameter selection and returns justifiable ranges of parameters that should be considered in the cluster analysis. To verify its effectiveness, it was used on three case studies: one is a simulated material with known values; two others are experimental datasets from a low-alloy steel.