Movement Primitives are a well studied and widely applied concept in modern robotics. Composing primitives out of an existing library, however, has shown to be a challenging problem. We propose probabilistic context-free grammars to sequence a series of primitives to generate complex robot policies from a given library of primitives. The rule-based nature of formal grammars allows an intuitive encoding of hierarchically and recursively structured tasks. This hierarchical concept strongly connects with the way robot policies can be learned, organized, and re-used. The induction of context-free grammars is often formulated as a maximum a-posteriori optimization. Given the common formulation of the prior,the resulting grammars are often not easily comprehensible by non-experts. We introduce a novel, easy to tune prior aimed at producing intuitive grammars for movement primitive sequencing.