Mean Field Game equilibria are based on the assumption of instantaneous interactions within a population of interchangeable agents, where each agent's impact diminishes as the population size increases. However, in practical scenarios, agents may not continuously observe the overall population state. Instead, in some situations, agents observe the empirical mean state only at discrete time intervals. This observation structure likely influences the nature of Nash equilibria that agents can attain. This paper characterizes the best responses of agents under such discrete observation conditions. Sufficient conditions for the existence of a so-called Markov Nash equilibrium within a finite population of agents are presented. Additionally, the difference in cost due to discrete versus continuous mean observations is evaluated.