Search is easy when you know the exact name of a WordPress plugin. Discovery begins when you do not.
A user knows the problem. A developer knows the product and the words used to describe it. A useful discovery system has to connect those two perspectives.
Identification is not discovery
Suppose a store owner asks:
Which WooCommerce plugin can calculate local delivery fees based on distance, without requiring another paid service?
The user has expressed an outcome, a platform dependency and a commercial constraint. But they may not know whether developers describe the featureas
distance shipping,
geolocation,
route calculation or something else.
Finding a plugin whose name the user already knows is identification. Finding a qualified candidate from the requirement alone is discovery.
What an apparently complete result conceals
A familiar list of plugins can look comprehensive. Yet the list only describes those candidates a search or recommendation process surfaced.
A system may accurately describe every plugin on its shortlist and still leave out a better functional match.
Accuracy about known candidates does not demonstrate coverage of unknown candidates.This is an especially difficult omission for the end user to notice. A user cannot ask why an unknown plugin is missing by name.
Better questions change the search, not necessarily the inventory
A conversational assistant can ask whether the user needs WooCommerce support, a free capability or a particular integration. That can sharpen the requirement considerably.
But better understanding of the query does not automatically create a complete, up-to-date set of plugins to evaluate. The source inventory and the way candidates are retrieved still matter.
A robust discovery process should expose its scope: which repository was searched, how current the underlying records are, and which important requirements could not be verified.
The evidence boundary
Plugin descriptions can support a
plausible functional match. They are not equivalent to testing the source code under the user's exact conditions. Missing information should be marked as missing.
That limitation does not justify replacing functional discovery with a list of the most popular names. It just means that candidate discovery and the decision to install must remain separate.
The first task is to find plausible solutions. The second is to decide which are sufficiently supported for the user's situation.