Smart contracts, predominantly authored in the Solidity programming language, are fundamental components of the blockchain ecosystem; however, they are frequently susceptible to security vulnerabilities. While existing detection tools have made significant advances in identifying such vulnerabilities, a critical gap remains in providing comprehensive explanations and reasoning about the identified issues. This is further complicated by the deficiency of well-annotated, high-quality training datasets specifically tailored for Solidity, which limits the development of advanced machine learning models capable of producing coherent and insightful explanations. In light of these challenges, this paper introduces a novel system designed to furnish detailed, context-aware explanations for vulnerabilities in Solidity-based smart contracts. A cornerstone of our methodology is the construction of a synthetic dataset, which encompasses a wide spectrum of code vulnerabilities matched with high-quality synthetic explanations. This dataset addresses the shortage of existing training data and enhances the capability of our system to offer meaningful insights into the vulnerabilities detected, thereby empowering developers and security auditors to understand and remediate potential security threats more effectively. Our contributions aim to enhance the interpretability of smart contract vulnerabilities and fortify the security of blockchain technologies.