To support energy planning and grid investigations, this paper presents an open source tool to generate and integrate realistic synthetic load profiles (SLPs) into power system software. The tool assigns daily SLPs to all loads within a network and is compatible with Vision Network Analysis and OpenDSS. It features a Graphical User Interface (GUI) and employs the probabilistic Multivariate Elliptical Copula (MEC) method, using active power consumption data as input. Additional functionalities include preprocessing, clustering, and validation. Preprocessing handles outlier and duplicate removal via the interquartile range (IQR) and imputes missing values using linear interpolation or k-Nearest Neighbors (kNN). Clustering groups data by month and day type, and later by K-Means to identify different consumer types. A selected cluster serves as input to the MEC model, which can be conditioned on annual consumption, mean load, and peak load. Validation metrics include Mean Absolute Percentage Error (MAPE), Root Mean Square Error (RMSE), and Jensen-Shannon Divergence (JS-D). In a case study using real medium voltage (MV) load data from a Dutch city, the tool achieved an average MAPE of \(0.56\%\) for industrial and \(0.51\%\) for aggregated residential consumers. It also successfully assigned SLPs, performed load flow calculations, and extracted congestion-related results in both software environments.