diff --git a/mindsdb/integrations/handlers/multi_format_api_handler/format_parsers.py b/mindsdb/integrations/handlers/multi_format_api_handler/format_parsers.py
index 9b46ab61da9..ffa297758ad 100644
--- a/mindsdb/integrations/handlers/multi_format_api_handler/format_parsers.py
+++ b/mindsdb/integrations/handlers/multi_format_api_handler/format_parsers.py
@@ -181,7 +181,25 @@ def parse_json(content: str, record_path: Optional[str] = None, auto_explode: bo
     if records is None:
         return pd.DataFrame({'value': [data]})
     if len(records) == 0:
-        return pd.DataFrame()
+        # Empty record array (e.g. {"results": [], "next_url": "..."}). A bare
+        # pd.DataFrame() is shape (0, 0) — zero columns — which DuckDB rejects
+        # downstream with "Need a DataFrame with at least one column" when the
+        # planner runs `SELECT * FROM df`. That surfaces to the agent as an opaque
+        # DuckDB error and triggers pointless query-rewrite retries, when the
+        # query was in fact valid and simply matched no rows.
+        #
+        # Preserve the envelope's top-level scalar siblings as columns so the
+        # empty result still carries metadata (status, count, next_url, ...);
+        # fall back to a single sentinel column so DuckDB always has a schema
+        # to describe.
+        columns = []
+        if isinstance(data, dict):
+            top_key = chosen_path.split('.')[0] if chosen_path else None
+            columns = [
+                k for k, v in data.items()
+                if k != top_key and (v is None or isinstance(v, (str, int, float, bool)))
+            ]
+        return pd.DataFrame(columns=columns or ['_empty'])
 
     df = pd.json_normalize(records, sep='.')
 
diff --git a/mindsdb/integrations/handlers/multi_format_api_handler/tests/test_format_parsers.py b/mindsdb/integrations/handlers/multi_format_api_handler/tests/test_format_parsers.py
index 4c2e2183ecd..ce97644fc9e 100644
--- a/mindsdb/integrations/handlers/multi_format_api_handler/tests/test_format_parsers.py
+++ b/mindsdb/integrations/handlers/multi_format_api_handler/tests/test_format_parsers.py
@@ -151,6 +151,32 @@ def test_empty_array_returns_empty_dataframe():
     df = parse_json(_dumps(payload))
 
     assert len(df) == 0
+    # Must never be a (0, 0) frame: DuckDB's `SELECT * FROM df` requires at
+    # least one column, else it raises "Need a DataFrame with at least one
+    # column" and the agent misreads a valid empty result as a bad query.
+    assert df.shape[1] >= 1
+
+
+def test_empty_array_preserves_envelope_scalar_columns():
+    # Regression: {"results": [], "next_url": "..."} previously produced a
+    # (0, 0) DataFrame, crashing DuckDB downstream. It must now yield an empty
+    # frame whose columns are the envelope's scalar siblings.
+    payload = {"results": [], "next_url": "https://api.example.com/next", "count": 0}
+    df = parse_json(_dumps(payload))
+
+    assert len(df) == 0
+    assert "next_url" in df.columns
+    assert "count" in df.columns
+    assert "results" not in df.columns
+
+
+def test_empty_top_level_array_falls_back_to_sentinel_column():
+    # A bare empty list has no envelope metadata to borrow columns from, so it
+    # falls back to a sentinel column to keep DuckDB happy.
+    df = parse_json(_dumps([]))
+
+    assert len(df) == 0
+    assert df.shape[1] >= 1
 
 
 def test_primitive_wrapped_in_value_column():