This is great. I would add that in addition to full Bayes, which has the most mature literature (example webapp for RCT re-analysis here and datamethods thread here), the need to define the MCID and ε is also important for empirical Bayes (example webapp for oncology RCT re-analysis here and related preprint here), frequentist confidence distributions, as well as fiducial probability distributions and their more modern generalizations.
From a practical standpoint, in oncology we often define the MCID as hazard ratio (HR) = 0.8 as noted for example in this American Society of Clinical Oncology working group statement. This is a reasonable choice because, using the assumptions described here and the corresponding web calculator here, this will yield a milestone absolute risk reduction (ARR) of 5-10% under various baseline risks, e.g., in the adjuvant renal cell carcinoma scenario used in the related practical examples here and here.
Thus, on the logHR scale δ = ln(0.8). I favor calling ε the threshold for trivial treatment effect (TTTE) on the logHR scale and obtaining it by dividing δ / 2 and then exponentiate to obtain TTTE ≈ 0.894 which is pretty close to 0.9. One may also divide δ / 3 which will yield TTTE ≈ 0.928 which may be useful for some purposes, e.g., interim analyses for futility. But I like using MCID = 0.8 and TTTE = 0.9 because the MCID nicely corresponds to the less stringent 0.8 and 1/0.8 = 1.25 bioequivalence limits often used by the WHO and the FDA, whereas the TTTE corresponds to the also commonly used more stringent 0.90 and 1/0.9 = 1.11 bioequivalence interval.