Every classical hypothesis test has a test statistic, and an implicit ordering on what values of the test statistic constitute more or less evidence for the alternative hypothesis. To understand the appropriate "tail area" for your critical region or p-value, you need to understand the ordering-of-evidence inherent in the test statistic. The test statistic and its (often implicit) ordering effectively defines a total order on the set of all possible observed outcomes, which tells you what outcomes constitute more or less evidence for the alternative hypothesis.
In hypothesis tests where the null-distribution is an F-distribution (i.e., the test statistic has an F-distribution, conditional on the null hypothesis being true), it is usually the case that the test statistic is a positive measure of deviation away from the outcome least conducive to the alternative hypothesis. In these cases a test statistic of zero represents the least possible evidence for the alternative, and higher values represent more evidence for the alternative. In this case, the p-value of the test is obtained solely from the upper tail of the F-distribution (i.e., outcomes that are at least as conducive to the alternative hypothesis as some cut-off outcome) and so the test is "one-sided".
An important thing to remember in hypothesis testing is that the test statistic and its null distribution are not sufficient to describe the test. You also need to know the evidentiary ordering of the test statistic that describes whether an outcome is more or less conducive to the alternative. Often this is not stated explicitly, and you need to look at the nature of the test statistic to figure it out.