diff --git a/libs/partners/anthropic/langchain_anthropic/chat_models.py b/libs/partners/anthropic/langchain_anthropic/chat_models.py
index 0b7e6f516eb..310380a4199 100644
--- a/libs/partners/anthropic/langchain_anthropic/chat_models.py
+++ b/libs/partners/anthropic/langchain_anthropic/chat_models.py
@@ -1123,6 +1123,11 @@ class ChatAnthropic(BaseChatModel):
     ) -> int:
         """Count tokens in a sequence of input messages.
 
+        Args:
+            messages: The message inputs to tokenize.
+            tools: If provided, sequence of dict, BaseModel, function, or BaseTools
+                to be converted to tool schemas.
+
         .. versionchanged:: 0.2.5
 
                 Uses Anthropic's token counting API to count tokens in messages. See:
diff --git a/libs/partners/anthropic/tests/integration_tests/test_chat_models.py b/libs/partners/anthropic/tests/integration_tests/test_chat_models.py
index 59f50efa039..0aff4ac3f0e 100644
--- a/libs/partners/anthropic/tests/integration_tests/test_chat_models.py
+++ b/libs/partners/anthropic/tests/integration_tests/test_chat_models.py
@@ -336,6 +336,7 @@ def test_anthropic_multimodal() -> None:
     assert isinstance(response.content, str)
     num_tokens = chat.get_num_tokens_from_messages(messages)
     assert num_tokens > 0
+    import pdb; pdb.set_trace()
 
 
 def test_streaming() -> None:
@@ -508,24 +509,34 @@ def test_with_structured_output() -> None:
 
 
 def test_get_num_tokens_from_messages() -> None:
-    llm = ChatAnthropic(model="claude-3-5-haiku-20241022")  # type: ignore[call-arg]
+    llm = ChatAnthropic(model="claude-3-5-sonnet-20241022")  # type: ignore[call-arg]
 
     # Test simple case
     messages = [
-        SystemMessage(content="You are an assistant."),
-        HumanMessage(content="What is the weather in SF?"),
+        SystemMessage(content="You are a scientist"),
+        HumanMessage(content="Hello, Claude"),
     ]
     num_tokens = llm.get_num_tokens_from_messages(messages)
     assert num_tokens > 0
 
     # Test tool use
-    @tool
+    @tool(parse_docstring=True)
     def get_weather(location: str) -> str:
-        """Get weather report for a city"""
+        """Get the current weather in a given location
+
+        Args:
+            location: The city and state, e.g. San Francisco, CA
+        """
         return "Sunny"
 
     messages = [
-        HumanMessage(content="What is the weather in SF?"),
+        HumanMessage(content="What's the weather like in San Francisco?"),
+    ]
+    num_tokens = llm.get_num_tokens_from_messages(messages, tools=[get_weather])
+    assert num_tokens > 0
+
+    messages = [
+        HumanMessage(content="What's the weather like in San Francisco?"),
         AIMessage(
             content=[
                 {"text": "Let's see.", "type": "text"},