diff --git a/src/databricks_ai_bridge/genie.py b/src/databricks_ai_bridge/genie.py
index 50e0a89..66ab1dc 100644
--- a/src/databricks_ai_bridge/genie.py
+++ b/src/databricks_ai_bridge/genie.py
@@ -5,10 +5,16 @@
 
 import pandas as pd
 from databricks.sdk import WorkspaceClient
+import tiktoken
 
 MAX_TOKENS_OF_DATA = 20000  # max tokens of data in markdown format
 MAX_ITERATIONS = 50  # max times to poll the API when polling for either result or the query results, each iteration is ~1 second, so max latency == 2 * MAX_ITERATIONS
 
+# Define a function to count tokens
+def _count_tokens(text):
+    encoding = tiktoken.encoding_for_model("gpt-4o")
+    return len(encoding.encode(text))
+
 def _parse_query_result(resp) -> Union[str, pd.DataFrame]:
     columns = resp["manifest"]["schema"]["columns"]
     header = [str(col["name"]) for col in columns]
@@ -43,8 +49,25 @@ def _parse_query_result(resp) -> Union[str, pd.DataFrame]:
 
         rows.append(row)
 
-    query_result = pd.DataFrame(rows, columns=header).to_string()
-    return query_result
+    query_result = pd.DataFrame(rows, columns=header).to_markdown()
+
+    # trim down from the total rows until we get under the token limit
+    trimmed_rows = len(rows)
+    tokens_used = _count_tokens(query_result)
+    while trimmed_rows > 0 and tokens_used > MAX_TOKENS_OF_DATA:
+        # convert to markdown
+        query_result = (
+            pd.DataFrame(rows, columns=header).head(trimmed_rows).to_markdown()
+        )
+        # keep trimming down until we get under the token limit
+        trimmed_rows -= 5
+        # worst case, return None, which the Agent will handle and not display the query results
+        tokens_used = _count_tokens(query_result)
+        if trimmed_rows == 0:
+            query_result = None
+            tokens_used = 0
+
+    return query_result.strip() if query_result else query_result
 
 
 class Genie: