diff --git a/examples/background_training_example.py b/examples/background_training_example.py
index 48089260..7e57656a 100644
--- a/examples/background_training_example.py
+++ b/examples/background_training_example.py
@@ -51,7 +51,7 @@ def main():
     print("✅ Training started!")
     print(f"   Job ID: {job_id}")
     print(f"   Estimator: {job_result['estimator']}")
-    print(f"   Dataset: {job_result['data_source']}")
+    print(f"   Dataset: {job_result.get('data_source') or job_result.get('dataset', 'unknown')}")
     print(f"   Horizon: {job_result['horizon']}")
 
     # Step 3: Monitor progress
diff --git a/src/sktime_mcp/tools/fit_predict.py b/src/sktime_mcp/tools/fit_predict.py
index ef8f1b46..3f3dc8fa 100644
--- a/src/sktime_mcp/tools/fit_predict.py
+++ b/src/sktime_mcp/tools/fit_predict.py
@@ -217,6 +217,14 @@ def fit_predict_async_tool(
         loop = asyncio.new_event_loop()
         asyncio.set_event_loop(loop)
 
+    # If the event loop is not running (e.g. when called from a plain script
+    # rather than from within the MCP server), start it in a background thread
+    # so that run_coroutine_threadsafe has a live loop to schedule work on.
+    if not loop.is_running():
+        import threading
+        thread = threading.Thread(target=loop.run_forever, daemon=True)
+        thread.start()
+
     coro = executor.fit_predict_async(
         estimator_handle,
         dataset=dataset,