Evaluating mitigating and aggravating circumstances in criminal liability poses significant challenges for judges, who must often make these assessments under uncertainty. While the M-LAMAC model assists judges in this task, its current framework lacks the ability to aggregate intensity values additively when circumstances of the same type must be combined—instead relying on minimum, maximum, or average values. This limitation restricts M-LAMAC’s generality, particularly in cases requiring the application of Articles 81.1 and 81.2 of the Cuban Penal Code. To address this gap, we propose the 2-Tuple Linguistic Bounded Sum (2TLBS), a novel aggregation operator designed to compute the collective intensity of circumstances within the M-LAMAC model. We formally demonstrate its properties (commutativity, associativity, and non-decreasing monotonicity) and validate its practical utility through two criminal case studies. The results show that 2TLBS significantly improve the sanction intervals recommendation, highlighting its potential to enhance judicial decision-making.