Large Language Models (LLMs) are transforming chemical research by enabling specialized reasoning models and tool-augmented agents. Yet existing benchmarks typically evaluate reasoning or tool invocation in isolation, overlooking the dynamic interaction between internal deliberation and external execution. We present ChemEval2, a multimodal benchmark for tool-augmented chemical reasoning, containing 2,410 samples spanning fundamental principles, spectroscopic analysis, and complex reaction understanding. Built from professional textbooks and experimental data through automated generation and rigorous human annotation, ChemEval2 introduces a tool-augmented Chain-of-Thought evaluation framework that embeds tool-use checkpoints at stages where unaided language reasoning becomes unreliable. This design evaluates whether models can identify information gaps, invoke appropriate tools, and integrate tool outputs into reasoning trajectories. Across diverse text and multimodal models, we observe a process–outcome inconsistency: models can satisfy intermediate reasoning steps or execute useful tool calls yet still fail at final synthesis. These results provide evidence that coordinating computational tool use with downstream chemical reasoning remains a substantial challenge. We open-source ChemEval2 and an MCP-based suite of specialized chemical tools for reproducible.