For a binary classifier, recall is defined as the true positive rate: Of all the samples that are actually positive, how often did our model output "positive".

To get this to 100%, the model would simply say "positive" all the time. This means that the precision (the fraction of samples where the classifier said "positive" that were actually positive) goes to 50% (for a balanced dataset).