Current approaches for Natural Language to SPARQL (NL2SPARQL) generation primarily rely on one-turn, training-intensive models. While effective in specific settings, these models often lack generalizability and fail to provide transparency or mechanisms for error recovery in realistic scenarios. Additionally, prior interactive works are largely outdated and incompatible with modern large language model (LLM) workflows. In this paper, we introduce InteracSPARQL, a training-free interactive refinement pipeline that acts as a plug-and-play enhancement for existing SPARQL generation systems. Our approach integrates a set of efficient entity and property lookup tools within a self-correction loop, guided by a novel hybrid Natural Language Explanation (NLE) module. This module combines rule-based Abstract Syntax Tree (AST) parsing with LLM semantic enrichment to produce explanations that are both structurally accurate and linguistically fluent. We evaluate InteracSPARQL on standard benchmarks (QALD-9 and QALD-10), showing that our tool-augmented self-refinement significantly boosts the accuracy of base models without fine-tuning. Furthermore, human evaluation confirms that our structured explanations substantially improve user understanding and ability to correct queries compared to unstructured baselines.