Shared control improves Human-Robot Interaction by reducing the user's workload and increasing the robot's autonomy. It allows robots to perform tasks under the user's supervision. Current eye-tracking-driven approaches face several challenges. These include accuracy issues in 3D gaze estimation and difficulty interpreting gaze when differentiating between multiple tasks. We present an eye-tracking-driven control framework, aimed at enabling individuals with severe physical disabilities to perform daily tasks independently. Our system uses task pictograms as fiducial markers combined with a feature matching approach that transmits data of the selected object to accomplish necessary task related measurements with an eye-in-hand configuration. This eye-tracking control does not require knowledge of the user's position in relation to the object. The framework correctly interpreted object and task selection in up to 97.9% of measurements. Issues were found in the evaluation, that were improved and shared as lessons learned. The open-source framework can be adapted to new tasks and objects due to the integration of state-of-the-art object detection models.