The luminosity function is a fundamental observable for characterising how galaxies form and evolve throughout the cosmic history. One key ingredient to derive this measurement from the number counts in a survey is the characterisation of the completeness and redshift selection functions for the observations. In this paper, we present GLACiAR, an open python tool available on GitHub to estimate the completeness and selection functions in galaxy surveys. The code is tailored for multiband imaging surveys aimed at searching for high-redshift galaxies through the Lyman-break technique, but it can be applied broadly. The code generates artificial galaxies that follow Sérsic profiles with different indexes and with customisable size, redshift, and spectral energy distribution properties, adds them to input images, and measures the recovery rate. To illustrate this new software tool, we apply it to quantify the completeness and redshift selection functions for J-dropouts sources (redshift z ~ 10 galaxies) in the Hubble Space Telescope Brightest of Reionizing Galaxies Survey. Our comparison with a previous completeness analysis on the same dataset shows overall agreement, but also highlights how different modelling assumptions for the artificial sources can impact completeness estimates.