In the present paper, we overcome these two crucial challenges for the use of MRF features or MRF itself for the development of (ML) fully nonlocal functionals and making DFT accurate for strongly correlated systems. First, we consider how well the existing MRF forms satisfy certain exact constraints (e.g., when applied to scaled and uniform densities), and how these constraints can be incorporated into it. Then we use these insights to improve MRF by refining and analyzing the so-called fluctuation function, an object that defines the XC functional within MRF. Then we focus on the practical aspects, develop the improved MRF integral package and implement MRF into a development version of the TURBOMOLE program . The key idea is to obtain a flexible implementation that will easily accommodate improvements through refined forms for the fluctuation function. The main computational bottleneck for MRF is the calculation of the density integral of Eq. 1. We overcome this issue by using the analytical integrals pertaining to Eq. 1 over the standard Gaussian basis functions. In addition to the MRF itself, we also implement the MRF reverse machinery 30 , which we use to make the exchange part of the MRF deliver exact exchange energy densities and then we use this as the starting point for building the MRF correlation functional.