So I'm someone who has published several meta-analyses of different forms, and written about meta-analysis as a topic.
I won't disagree with you that there are many poorly conducted meta-analyses. However, I think there's many well-done meta-analyses as well, and most importantly maybe meta-analyses aren't really different from anything else in life: some are good, some are bad, and many are in between.
One thing I've always argued is that meta-analyses have as a benefit a way of honing discussion around concrete specifics. The linked paper, for example, exists in part because there was a meta-analysis drawing attention to the literature at large. There's a decent chance that these studies would never be discussed if there wasn't a spotlight being pointed at the area.
With reviews, what happens is people pick and choose studies anyway, or don't, and then come to some subjective conclusion that's based on some unclear process. Meta-analysis makes all of this clear, and forces everyone to be absolutely explicit (or as explicit as can be) about how they're coming to their conclusions. If there's something wrong with it, then you can point to the specifics of that instead of going back and forth.
The problem with relying on definitive studies alone is that sometimes there will be more than one of them, or there won't be any definitive study, but many decently-done studies. Or the "definitive" study will have some controversial feature that doesn't clearly rule it out, but clouds the waters in a way that several smaller studies might draw attention to. Alternatively, there might be important heterogeneity across designs that illuminates moderating variables (like dose, or environmental context, or gender, or age, or whatever).
This paper is about meta-analysis of summary statistics, which to me is kind of bringing up a red herring. Statistically speaking if you can calculate the right summary statistics, the results should be the same as having the raw data. Issues about irregularities in results apply to raw as well as summary statistics; it also seems unrealistic to expect raw data in every case, and journals don't apply that standard either (that is, journals don't expect reviewers to reanalyze the data from scratch).
What's really needed is open data sharing, and scrutiny about studies that increases as the stakes of the results increase. I can speak to cases where I've been surprised at the state of the raw data, even in situations where the whole point of the study was to skeptically replicate a finding. Maybe for something like invermectin raw data analyses are appropriate. But it seems absurd to expect to throw out studies in the literature just because you don't have access to the raw data in every case.