This looks great.
I have a few suggestions:
1. Perhaps you could add additional guidelines on how to report "null results". For example, studies often make exaggerated claims that "there was no evidence for an effect", without considering whether their estimated effects were compatible with expected effect sizes (e.g. from observational studies), taking into account confidence intervals.
2. In general, studies should be encouraged to consider a priori or expected effect sizes (e.g. from observational studies), which can help guide interpretation of MR results, e.g. statistical power as well as interpretation of null results.
3. Explicit consideration of statistical power and instrument strength.
4. The list seems largely designed with one sample MR analyses in mind and there are a lot of items that aren't really relevant or practical for two sample MR studies. For example, many of the items in section "4. Study design and data sources".
5. Some of the suggested items will be extremely difficult or impractical for two sample MR studies. For example, "For each exposure, outcome and other relevant variables, describe methods of assessment and, in the case of diseases, the diagnostic criteria used." Sometimes a two-sample MR study will be based on very large meta-analyses of exposure and/or outcome studies, where going back to the original studies to identify all this information is likely to be practically impossible. I agree that just because something is difficult doesn't mean we shouldn't do it. But there might be alternative metrics that are easier to get hold of that also address the same underlying issues, e.g. metrics of heterogeneity between studies within the outcome or exposure GWAS meta-analysis.
6. The recommendation in 10.d.i. will usually be quite difficult to obtain. Instead, authors should be encouraged to provide evidence that the exposure and outcome samples come from the same population, which could include 10.d.i on rare occasions. "Provide information on the similarity of the genetic variant-exposure associations between the exposure and outcome samples."