In this study, we evaluated the capabilities of three LLMs -GPT-4, Claude, and Gemini -in reading MOF-related literature through two key tasks: synthesis condition extraction, and Q&A dataset generation. Claude performed exceptionally well in terms of completeness, effectively covering a wide range of synthesis details, while Gemini showed both outstanding correctness and completeness, making it the most reliable for accurate and complete data extraction. GPT-4, although less effective in quantitative metrics, exhibited strong logical reasoning and context inference abilities, as evidenced by its capacity to extrapolate synthesis conditions effectively. Our error analysis indicated common challenges across all LLMs, particularly in the interpretation of numerical data, highlighting the need for improvements to enhance data extraction consistency. In the Q&A dataset generation tasks, Claude and Gemini outperformed GPT-4 in accuracy and groundedness for both single-hop and multi-hop questions, making them strong candidates for use as benchmarks in future studies. Gemini's ability to impose a structured format on its responses further suggests its suitability for building organized datasets with minimal post-processing requirements.