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We develop a Python tool to estimate the tail distribution of the number ofdark matter halos beyond a mass threshold and in a given volume in alight-cone. The code is based on the extended Press-Schechter model and iscomputationally efficient, typically taking a few seconds on a personal laptopfor a given set of cosmological parameters. The high efficiency of the codeallows a quick estimation of the tension between cosmological models and thered candidate massive galaxies released by the James Webb Space Telescope, aswell as scanning the theory space with the Markov Chain Monte Carlo method. Asan example application, we use the tool to study the cosmological implicationof the candidate galaxies presented in Labb\'e et al. (2023). The standard$\Lambda$ cold dark matter ($\Lambda$CDM) model is well consistent with thedata if the star formation efficiency can reach $\sim 0.3$ at high redshift.For a low star formation efficiency $\epsilon \sim 0.1$, $\Lambda$CDM model isdisfavored at $\sim 2\sigma$-$3\sigma$ confidence level.

Quantifying the tension between cosmological models and JWST red candidate massive galaxies