We present Distribird, an agentic web application that automates the process of building informative priors from scientific literature for Bayesian model calibration. Given a parameter name, physical description, and domain context, Distribird deploys a multi-agent pipeline that searches the literature, extracts and weights reported values by domain relevance, and fits a probability distribution via AIC model selection. When no literature is available, the system falls back to sensible uninformative alternatives, and clearly reports both the evidence behind and the confidence level of every prior it produces. We evaluate the tool on 24 parameters across 10 scientific domains comparing three open-weight models with a single-prompt LLM baseline. On prior quality the full pipeline matches this baseline. Every prior is traced to the specific papers and values from which it was constructed; a built-in validity layer declines to produce priors for out-of-scope requests, whereas the single-prompt baseline returns confident but unfounded priors for them in 11 of 30 model--parameter cases; and every language-model call runs locally, so no parameter description or unpublished modelling detail is transmitted to a third-party LLM provider. For scientific use, we argue these properties matter more than a marginal improvement in point-estimate accuracy. Blogger's Review: Distribird is a very useful tool that can help researchers automate the process of building informative priors for Bayesian model calibration. By leveraging scientific literature and a multi-agent pipeline, Distribird can provide high-quality priors and clearly report the evidence and confidence level of each prior. This approach can help reduce the time and expertise required to manually construct priors and improve the accuracy and reliability of Bayesian model calibration.