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ChemAmp introduces a tool amplification paradigm that dynamically coordinates specialized chemistry tools (UniMol2, Chemformer) as composable agents to enhance performance on molecular tasks. The framework outperforms chemistry-specialized models and reduces inference token costs by 94% compared to vanilla multi-agent systems.