diff --git a/evalharness/data/loader.py b/evalharness/data/loader.py
index 14fd930..420d00c 100644
--- a/evalharness/data/loader.py
+++ b/evalharness/data/loader.py
@@ -106,7 +106,7 @@ def _read_file(path: str) -> List[Dict[str, Any]]:
             import pyarrow.parquet as pq
         except ImportError:
             raise ImportError(
-                f'{path} is parquet; install the reader first: pip install pyarrow'
+                f'{path} is parquet; install the reader first: pip install "evalharness[all]"'
             )
         return pq.read_table(path).to_pylist()
     with open(path, encoding='utf-8') as f:
@@ -386,7 +386,7 @@ def _load_from_hub(spec: DatasetSpec) -> List[Dict[str, Any]]:
     except ImportError:
         raise ImportError(
             f'dataset {spec.name!r} lives on a hub ({spec.source!r}); '
-            "install the optional dependency first: pip install 'evalharness[hub]'"
+            'install it first: pip install "evalharness[all]" (or [hub] for datasets only)'
         )
     kwargs = {k: v for k, v in spec.params.items() if k not in _RESERVED_PARAMS}
     subset = None if spec.subset == 'default' else spec.subset
diff --git a/evalharness/eval/scorer.py b/evalharness/eval/scorer.py
index 4af5dfe..cb66f6f 100644
--- a/evalharness/eval/scorer.py
+++ b/evalharness/eval/scorer.py
@@ -129,7 +129,7 @@ def math_equal(pred: str, target, sample: Sample, ctx: ScoreContext):
                 details['acc'] = {'path': 'sympy'}
                 return {'acc': 1.0}, details
         except ImportError:
-            details['acc']['sympy'] = 'not installed (pip install sympy pylatexenc)'
+            details['acc']['sympy'] = 'not installed; pip install "evalharness[all]" enables official symbolic grading'
     return {'acc': 0.0}, details
 
 
@@ -194,7 +194,7 @@ def _drop_metrics(predicted: List[str], gold: List[str]):
         import numpy as np
         from scipy.optimize import linear_sum_assignment
     except ImportError as e:
-        raise LayerNotReady("official DROP scoring needs numpy+scipy: pip install 'evalharness[exec]'") from e
+        raise LayerNotReady("official DROP scoring needs numpy+scipy: pip install 'evalharness[all]'") from e
 
     n, m = len(gold_bags), len(pred_bags)
     if m == 0:
@@ -357,11 +357,15 @@ def env_reward(pred: str, target, sample: Sample, ctx: ScoreContext):
         if backend != 'auto':
             raise LayerNotReady(f'backend {backend!r} unavailable '
                                 '(missing dependency?)')
+        # auto fell back: say so in details so users know native was used
+        native_details_hint = ('native comparison used; install "evalharness[bfcl]" '
+                               'for the official ast_checker')
     gt_calls = (env_state.get('ground_truth') or {}).get('tool_calls')
     if gt_calls is None:
         # irrelevance categories: correct behavior is calling NOTHING
         hit = int(len(calls) == 0)
-        return {'acc': float(hit)}, {'acc': {'mode': 'no_calls', 'n_calls': len(calls)}}
+        return {'acc': float(hit)}, {'acc': {'mode': 'no_calls', 'n_calls': len(calls),
+                                             'hint': native_details_hint}}
 
     def norm(call: Dict[str, Any]) -> str:
         return json.dumps({'name': call.get('name'),
@@ -374,6 +378,7 @@ def env_reward(pred: str, target, sample: Sample, ctx: ScoreContext):
     return {'acc': float(hit)}, {'acc': {
         'mode': 'call_sequence', 'expected': want[:5], 'got': got[:5],
         'n_expected': len(want), 'n_got': len(got),
+        'hint': native_details_hint,
     }}