This chapter capitalizes on the understanding of the MPC implementation hopefully grasped in Chap. 5 in order to explore in depth the different details of the implementation settings of an Nonlinear MPC and their impact on the success/failure of the resulting real-time closed-loop implementation. The success/failure of the resulting closed-loop depends on the certification of the satisfaction of the conditions regarding three issues, namely: 1) the real-timeReal-time implementability in terms of computation time on a specific target. 2) the satisfaction of the constraints and 3) the contraction of the optimal cost which plays generally the role of the Lyapunov function in the analysis of the stabilityStability of the closed-loop under the MPC feedback. More precisely, although a detailed definition of the implementation parameters listed below is given in Chap. 6, a quick statement of these parameters is given here to help the reader getting a rapid hint: This chapter describes in detail a heuristic that enables to appropriately search the domain of definition of the above implementation parameters in order to determine a dashboard of admissible settings with regards to the three above mentioned criteria. Moreover, the freely available Python package MPC_tuner that implements the above algorithm is also presented and assessed through an illustrative example of the Planar Vertical Take-Off and Landing (PVTOL)PVTOL aircraft which involves six states and two control inputs.