Monte Carlo Markov Chain

I was trying to figure out what is a Monte Carlo Markov Chain.
From what I understand it is a way of computing an approximation of a probability distribution, which cannot compute exactly.
So we keep sampling from a probability distribution, in order to be more accurate, reducing variance of the samples by increasing the number of examples, and this samples are given by Gibbs Sampling.
This step-to-step process is a Markov Chain, but I don't really get the details of this one.