You are right that the FSRS algorithm is trying to make your “True Retention” match your “Desired Retention”.
It will get better at doing this over time if you optimise your FSRS parameters again as you do more reviews.
N.B. The algorithm is not perfect and the two numbers will probably never match exactly.
Choosing “Desired Retention” is making a trade-off between the amount that you will learn, and the amount of work you have to do.
Higher desired retention = Learn more, but higher workload
Lower desired retention = Learn less, but lower workload
The point of using spaced repetition is to try to optimise the amount you learn per the time you spend learning.
This means that ideally you do not want to increase your Desired Retention too much because there are diminishing returns (significantly increase your workload, to not do much better).
You might want to lower you Desired Retention slightly because you think it is a better trade-off (significantly reduce your workload, to only do a little worse).
However there is a danger that if you lower your Desired Retention too much you end up forgetting so many cards that you are actually learning less per time spent.
This means there is a sweet spot of “good” Desired Retention values that you ideally do not want to go outside of.
The FSRS tutorial has a nice example graph showing this (N.B. this graph would be different for you):
Compute Minimum Recommended Retention (CMRR) is just trying to figure out that bottom bound, which it is not a good idea to set Desired Retention below.
You can continue to use a higher Desired Retention if you want to learn more, but CMRR is telling you that if you want to reduce your workload it is safe to reduce your Desired Retention as long as you do not go below the number it gave you.
See: