The implementation of robotic applications is currently facing various control challenges that simple controllers fail to address, e.g., systems are increasingly complex, need to comply with constraints and need to account for several, sometimes conflicting performance objectives. Model predictive control (MPC) is an advanced control technique that explicitly accounts for all these challenges by considering system models and solving constrained optimization problems in real-time at every control step. However, wide adoption of MPC in complex robotic applications is impeded by its high computational and engineering complexity. This work addresses both issues, reducing the computational complexity of MPC implementations for robotic applications and reducing the engineering time required for its deployment. This research is supported by an MPC toolchain development in order to integrate all software in an open and modular fashion as to create a workflow from problem specification to deployment. The developments are validated computationally and experimentally on industrial robotic set-ups in the lab.