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This paper introduces Distribird, an agentic web application that automates the design of informative prior distributions for Bayesian model calibration by searching and reading scientific literature. It evaluates the tool on 24 parameters across ten domains using local open-weight LLMs, emphasizing traceability, privacy, and out-of-scope rejection over raw accuracy.