In the weak-strong distance oracle model of Bateni, Dharangutte, Jayaram and Wang (COLT 2024), a metric $(X,d)$ on $n$ points is accessed through two oracles: a cheap weak oracle that returns each queried distance correctly with probability $1-δ$ and permits arbitrary values on corrupted pairs, chosen by an adversary who knows the entire metric and all corrupted pairs, and an expensive strong oracle that reveals a point exactly. The goal is to cluster well while making as few strong queries as possible. Our main result is a $(6+\varepsilon)$-approximation for $k$-center using $O(k\log n\log(\varepsilon^{-1}\log n))$ strong point queries, for any fixed $\varepsilon>0$. This improves both the $(14+\varepsilon)$ approximation factor and the $O(k\log^2 n\log(\varepsilon^{-1}\log n))$ query bound of Bateni et al. It also improves the query complexity of the recent $(6+\varepsilon)$-approximation of Pradhan, Bhattacharya and Jaiswal (AISTATS 2026), in the variant of the model they study. Our algorithm outputs an explicitly known sketch of an otherwise unknown metric: a concrete metric on at most $k$ points that a downstream algorithm can compute with directly, with no further queries and no noise. As an application, we give a simple FPT algorithm for Minimum Sum of Radii (MSR) that returns a constant-factor solution using $2^{O(k\log k)}\cdot O(\log n\log\log n)$ strong queries. We complement the upper bound with an $Ω(k\log(n/k))$ lower bound for $k$-center with assignment that holds for row-local algorithms. These algorithms assign two unqueried points to the same center whenever their weak distances to the strong-queried points are exactly the same. Our algorithm and those of Bateni et al. and Pradhan et al. are all row-local, so our query bound is optimal for this class up to the $O(\log\log n)$ factor.

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