Existing RAG benchmarks often overlook query difficulty, leading to inflated performance on simpler questions and unreliable evaluations. A robust benchmark dataset must satisfy three key criteria: quality, ensuring complete and reliable ground truth (GT) responses; diversity, expanding semantic coverage to prevent overfitting; and difficulty, capturing the complexity of reasoning based on hops and the distribution of supporting evidence. However, current benchmarks lack a systematic approach to defining and controlling query difficulty at a fine-grained level. To address this, we propose MHTS (Multi-Hop Tree Structure), a novel dataset synthesis framework that systematically controls multi-hop reasoning complexity by leveraging a multi-hop tree structure to generate logically connected, multi-chunk queries. Our fine-grained difficulty estimation formula exhibits a strong correlation with the overall performance metrics of a RAG system, validating its effectiveness in assessing both retrieval and answer generation capabilities. By ensuring high-quality, diverse, and difficulty-controlled queries, our approach enhances RAG evaluation and benchmarking capabilities. This work contributes to the development of more reliable, efficient, and adaptable AI-driven research assistants, facilitating advancements in document-based reasoning and retrieval tasks.