diff --git a/libs/aws/langchain_aws/chat_models/bedrock_converse.py b/libs/aws/langchain_aws/chat_models/bedrock_converse.py
index 94506908..16907691 100644
--- a/libs/aws/langchain_aws/chat_models/bedrock_converse.py
+++ b/libs/aws/langchain_aws/chat_models/bedrock_converse.py
@@ -268,7 +268,7 @@ class Joke(BaseModel):
     max_tokens: Optional[int] = None
     """Max tokens to generate."""
 
-    stop_sequences: Optional[List[str]] = Field(None, alias="stop")
+    stop_sequences: Optional[List[str]] = Field(default=None, alias="stop")
     """Stop generation if any of these substrings occurs."""
 
     temperature: Optional[float] = None
@@ -308,12 +308,17 @@ class Joke(BaseModel):
     have an ARN associated with them.
     """
 
-    endpoint_url: Optional[str] = Field(None, alias="base_url")
+    endpoint_url: Optional[str] = Field(default=None, alias="base_url")
     """Needed if you don't want to default to us-east-1 endpoint"""
 
     config: Any = None
     """An optional botocore.config.Config instance to pass to the client."""
 
+    formatted_tools: List[
+        Dict[Literal["toolSpec"], Dict[str, Union[Dict[str, Any], str]]]
+    ] = Field(default_factory=list, exclude=True)
+    """"Formatted tools to be stored and used in the toolConfig parameter."""
+
     class Config:
         """Configuration for this pydantic object."""
 
@@ -413,7 +418,8 @@ def bind_tools(
     ) -> Runnable[LanguageModelInput, BaseMessage]:
         if tool_choice:
             kwargs["tool_choice"] = _format_tool_choice(tool_choice)
-        return self.bind(tools=_format_tools(tools), **kwargs)
+        self.formatted_tools = _format_tools(tools)
+        return self.bind(tools=self.formatted_tools, **kwargs)
 
     def with_structured_output(
         self,
@@ -467,8 +473,7 @@ def _converse_params(
             }
         if not toolConfig and tools:
             toolChoice = _format_tool_choice(toolChoice) if toolChoice else None
-            toolConfig = {"tools": _format_tools(tools), "toolChoice": toolChoice}
-
+            toolConfig = {"tools": self.formatted_tools, "toolChoice": toolChoice}
         return _drop_none(
             {
                 "modelId": modelId or self.model_id,
@@ -648,7 +653,7 @@ def _anthropic_to_bedrock(
                 {
                     "toolUse": {
                         "toolUseId": block["id"],
-                        "input": block["input"],
+                        "input": _try_to_convert_to_dict(block["input"]),
                         "name": block["name"],
                     }
                 }
@@ -852,3 +857,13 @@ def _format_openai_image_url(image_url: str) -> Dict:
         "format": match.group("media_type"),
         "source": {"bytes": _b64str_to_bytes(match.group("data"))},
     }
+
+
+def _try_to_convert_to_dict(tool_use_input: Any) -> Any:
+    """Attempt to convert the toolUse.input to a dictionary."""
+    if isinstance(tool_use_input, str):
+        try:
+            return json.loads(tool_use_input)
+        except json.JSONDecodeError:
+            return tool_use_input
+    return tool_use_input