Current evidence-based assessment methods, such as structured interviews and lengthy assessment batteries, often require hours to administer, score, and interpret and thus are infrequently used in real-world practice. As evidence-based assessment tools are developed for implementation in real-world youth mental health settings, the transportability properties of assessment procedures (including administration and interpretation burden) need to be considered and improved. In the present study, we thus conducted an initial feasibility study using a clinical sample of community-based youths (N = 306) to develop an assessment protocol based on 2 child and 2 parent self-report questionnaires (thus low on administration burden). Using decision-tree analysis, we identified a series of cutoff scores across these scales that may be used to inform treatment need related to anxiety, depression, attention-deficit/hyperactivity disorder (ADHD), and disruptive behavior problems. This algorithm-based approach to interpreting assessment information provided clear and simple guidelines (thus low on interpretation burden) that matched the best estimate treatment determinations derived by trained assessors, supervisors, and expert consultants who integrated information provided by child and parent structured interviews and self-report scales. The present study demonstrated the feasibility of developing an assessment protocol to inform various treatment allocation decisions in a way that imposes little assessment administration and interpretation burden yet maintains adequate classification accuracy. These characteristics make the proposed protocol promising with regard to its transportability and suitability for adoption and implementation in real-world mental health settings.