Second language writers rarely receive the detailed feedback they need to improve, as comprehensive feedback takes teachers days per class. To solve this, we present WrAFT, a Writing Assessment and Feedback Tool, that delivers reliable scores along with effective comprehensive feedback for argumentative essays. WrAFT adopts a modular design by dividing the automated writing evaluation (AWE) into essay scoring, surface-level feedback and deep-level feedback. In building the system, various Large Language Models (LLMs) have been evaluated, including LLaMA-3.3-70B-Instruct, GPT-4o, and Claude 3.7, through both supervised fine-tuning and direct prompting. Evaluation shows that WrAFT achieves state-of-the-art performance in scoring with a Quadratic Weighted Kappa (QWK) of 0.84 on a proprietary dataset of 480 TOEFL Independent Writing essays. More importantly, human evaluation of system-generated feedback also reveals high approval ratings (96.14% for surface-level, 93.03% for deep-level macro feedback, and 94.69% for deep-level micro feedback). Publicly available interactive user interface has also been developed for the system.