A natural language-based requirements specification tends to be full of requirements that are ambiguous, unnecessarily complicated, missing, wrong, duplicated or conflicting. Poor quality requirements often compromise the subsequent software construction activities (e.g. planning, design, coding or testing). A strategy for requirements quality evaluation should enable a faster requirements analysis, highlight defect indicators and incorporate also fix recommendations to help practitioners to effectively improve their requirements. In this paper we brief describe a Natural Language Processing and Petri-Net strategy for automated analysis of scenario-driven requirements named C&L prototype tool. The C&L evaluates structural (Static analysis) aspects of scenarios and behavioral aspects (Dynamic analysis) of equivalent Petri-Nets. The feasibility of the C&L is evaluated in four projects described as use cases, which indicates promising results (the overall precision was 93.5% and the recalls were perfect).