diff --git a/skpro/metrics/_classes.py b/skpro/metrics/_classes.py
index 85f449552..816e2a238 100644
--- a/skpro/metrics/_classes.py
+++ b/skpro/metrics/_classes.py
@@ -127,9 +127,8 @@ def _evaluate_by_index(self, y_true, y_pred, **kwargs):
             # if alpha was provided, check whether  they are predicted
             #   if not all alpha are observed, raise a ValueError
             if not np.isin(alpha, y_pred_alphas).all():
-                # todo: make error msg more informative
-                #   which alphas are missing
-                msg = "not all quantile values in alpha are available in y_pred"
+                missing_alphas = list(set(alpha) - set(y_pred_alphas))
+                msg = f"not all quantile values in alpha are available in y_pred. Missing alphas: {missing_alphas}."
                 raise ValueError(msg)
             else:
                 alphas = alpha
diff --git a/skpro/metrics/tests/test_probabilistic_metrics.py b/skpro/metrics/tests/test_probabilistic_metrics.py
index eb852ffa4..1882bd398 100644
--- a/skpro/metrics/tests/test_probabilistic_metrics.py
+++ b/skpro/metrics/tests/test_probabilistic_metrics.py
@@ -210,7 +210,7 @@ def test_evaluate_alpha_positive(Metric, y_pred, y_true):
 )
 def test_evaluate_alpha_negative(Metric, y_pred, y_true):
     """Tests whether correct error raised when required quantile not present."""
-    with pytest.raises(ValueError):
+    with pytest.raises(ValueError, match=".*Missing alphas:.*"):
         # 0.3 not in test quantile data so raise error.
         Loss = Metric.create_test_instance().set_params(alpha=0.3)
         res = Loss(y_true=y_true, y_pred=y_pred)  # noqa