Reading is an essential skill for any child to learn, and finding enjoyment in it can greatly contribute to developing proper reading comprehension. Finding the books they like could prove to be difficult. Utilizing collaborative filtering recommender systems to recommend books to children is a tricky task, the lack of user feedback makes it difficult to accurately recommend books they would enjoy. Using content based recommender systems might be preferable, but what book features could a recommender system like this base recommendations on? This research explores the idea of utilizing the textual complexity of books and their descriptions as such a possible feature. By evaluating how accurate readability formulas can predict the age a book is intended for, how the variability and length of sentences vary per age and analysing the difficulty of words used, this paper finds that the descriptions of books intended for younger audiences might not be aimed at them, but instead at their parents. These findings imply that basing recommendations of the textual complexity of book descriptions might not be the most useful feature to base recommendations of.