diff --git a/libs/vertexai/tests/integration_tests/test_maas.py b/libs/vertexai/tests/integration_tests/test_maas.py
index dfbceef9..78b5c016 100644
--- a/libs/vertexai/tests/integration_tests/test_maas.py
+++ b/libs/vertexai/tests/integration_tests/test_maas.py
@@ -1,8 +1,15 @@
+import json
+from typing import List
+
 import pytest
 from langchain_core.messages import (
     AIMessage,
     AIMessageChunk,
+    BaseMessage,
+    HumanMessage,
+    ToolMessage,
 )
+from langchain_core.tools import tool
 
 from langchain_google_vertexai.model_garden_maas.mistral import (
     VertexModelGardenMistral,
@@ -48,3 +55,53 @@ async def test_astream(model_name: str) -> None:
     output = llm.astream("What is the meaning of life?")
     async for chunk in output:
         assert isinstance(chunk, AIMessageChunk)
+
+
+@pytest.mark.extended
+@pytest.mark.parametrize("model_name", model_names)
+async def test_tools(model_name: str) -> None:
+    @tool
+    def search(
+        question: str,
+    ) -> str:
+        """
+        Useful for when you need to answer questions or visit websites.
+        You should ask targeted questions.
+        """
+        return "brown"
+
+    tools = [search]
+
+    llm = VertexModelGardenMistral(model=model_name, location="us-central1")
+    llm_with_search = llm.bind_tools(
+        tools=tools,
+    )
+    llm_with_search_force = llm_with_search.bind(
+        tool_choice={"type": "function", "function": {"name": "search"}}
+    )
+    request = HumanMessage(
+        content="Please tell the primary color of sparrow?",
+    )
+    response = llm_with_search_force.invoke([request])
+
+    assert isinstance(response, AIMessage)
+    tool_calls = response.tool_calls
+    assert len(tool_calls) == 1
+
+    tool_response = search("sparrow")
+    tool_messages: List[BaseMessage] = []
+
+    for tool_call in tool_calls:
+        assert tool_call["name"] == "search"
+        tool_message = ToolMessage(
+            name=tool_call["name"],
+            content=json.dumps(tool_response),
+            tool_call_id=(tool_call["id"] or ""),
+        )
+        tool_messages.append(tool_message)
+
+    result = llm_with_search.invoke([request, response] + tool_messages)
+
+    assert isinstance(result, AIMessage)
+    assert "brown" in result.content
+    assert len(result.tool_calls) == 0