This work addresses the problem of robust output feedback model predictive control for discrete-time, constrained linear systems corrupted by (bounded) state and measurement disturbances. Using the available information on measurements and uncertainty bounds, the objective is to stabilize such a system while robustly respecting the imposed constraints on state and control. To this end, interval observer and predictor with guaranteed performance are incorporated into the classic MPC scheme. This new approach offers advantages such as enlarged feasible regions for the optimal control problem, low computational burden, and ease of design.