diff --git a/taar/recommenders/ensemble_recommender.py b/taar/recommenders/ensemble_recommender.py
index f1e9d11..1d0bc57 100644
--- a/taar/recommenders/ensemble_recommender.py
+++ b/taar/recommenders/ensemble_recommender.py
@@ -33,8 +33,9 @@ class EnsembleRecommender(BaseRecommender):
 
     def can_recommend(self, client_data, extra_data={}):
         """The ensemble recommender is always going to be
-        available"""
-        return True
+        available if at least one recommender is available"""
+        return sum([self._recommender_map[rkey].can_recommend(client_data)
+                    for rkey in self.RECOMMENDER_KEYS])
 
     def recommend(self, client_data, limit, extra_data={}):
         """
diff --git a/tests/test_ensemblerecommender.py b/tests/test_ensemblerecommender.py
new file mode 100644
index 0000000..fd2bda5
--- /dev/null
+++ b/tests/test_ensemblerecommender.py
@@ -0,0 +1,73 @@
+import boto3
+import json
+import pytest
+from moto import mock_s3
+from taar.recommenders.ensemble_recommender import S3_BUCKET
+from taar.recommenders.ensemble_recommender import ENSEMBLE_WEIGHTS
+from taar.recommenders.ensemble_recommender import EnsembleRecommender
+
+
+class MockRecommender:
+    """The MockRecommender takes in a map of GUID->weight."""
+
+    def __init__(self, guid_map):
+        self._guid_map = guid_map
+
+    def can_recommend(self, *args, **kwargs):
+        return True
+
+    def recommend(self, *args, **kwargs):
+        return sorted(self._guid_map.items(), key=lambda item: -item[1])
+
+
+def generate_a_fake_taar_client():
+    return {'client_id': 'test-client-001',
+            'activeAddons': [],
+            'geo_city': 'brasilia-br',
+            'subsession_length': 4911,
+            'locale': 'br-PT',
+            'os': 'mac',
+            'bookmark_count': 7,
+            'tab_open_count': 4,
+            'total_uri': 222,
+            'unique_tlds': 21}
+
+
+@pytest.fixture
+def mock_s3_ensemble_weights():
+    result_data = {'ensemble_weights': {'legacy': 10000,
+                                        'collaborative': 1000,
+                                        'similarity': 100,
+                                        'locale': 10}}
+    mock_s3().start()
+    conn = boto3.resource('s3', region_name='us-west-2')
+    conn.create_bucket(Bucket=S3_BUCKET)
+    conn.Object(S3_BUCKET, key=ENSEMBLE_WEIGHTS).put(Body=json.dumps(result_data))
+    yield conn
+    mock_s3().stop()
+
+
+def test_recommendations(mock_s3_ensemble_weights):
+    EXPECTED_RESULTS = [('cde', 12000.0),
+                        ('bcd', 11000.0),
+                        ('abc', 10023.0),
+                        ('ghi', 3430.0),
+                        ('def', 3320.0),
+                        ('ijk', 3200.0),
+                        ('hij', 3100.0),
+                        ('lmn', 420.0),
+                        ('klm', 409.99999999999994),
+                        ('jkl', 400.0)]
+    mock_legacy = MockRecommender({'abc': 1.0, 'bcd': 1.1, 'cde': 1.2})
+    mock_locale = MockRecommender({'def': 2.0, 'efg': 2.1, 'fgh': 2.2, 'abc': 2.3})
+    mock_collaborative = MockRecommender({'ghi': 3.0, 'hij': 3.1, 'ijk': 3.2, 'def': 3.3})
+    mock_similarity = MockRecommender({'jkl': 4.0,  'klm': 4.1, 'lmn': 4.2, 'ghi': 4.3})
+    mock_recommenders = {'legacy': mock_legacy,
+                         'collaborative': mock_collaborative,
+                         'similarity': mock_similarity,
+                         'locale': mock_locale}
+    r = EnsembleRecommender(mock_recommenders)
+    client = generate_a_fake_taar_client()
+    recommendation_list = r.recommend(client, 10)
+    assert isinstance(recommendation_list, list)
+    assert recommendation_list == EXPECTED_RESULTS