This paper develops a model for studying the problem of information intermediation faced by a platform that connects buyers and sellers. Buyers search for sellers in continuous time and are time-sensitive, while sellers have limited capacity for serving buyers and derive heterogeneous payoffs from being matched with different buyers. The platform controls the information the sellers observe about the buyers before forming a match. I show that full information disclosure is inefficient because of excessive rejections by sellers. When the platform observes the sellers’ preferences, there is a simple policy with partial disclosure that restores full efficiency. When seller preferences are unknown to the platform, I characterize the disclosure policy that maximizes the total surplus. In a setting with linear payoffs and a uniform distribution of seller attributes, I find that the optimal policy perfectly reveals low-cost buyers and pools high-cost buyers (upper-coarsening). With this policy, tighter constraints on sellers’ capacities or a higher buyer-to-seller ratio requires that less information be disclosed. For a general distribution of seller attributes, I develop an approach to solving the disclosure problem with heterogeneous and forward-looking sellers. I discuss several applications to the design of digital matching platforms.