fix(anthropic): preserve native tool format when guardrails convert tools for Anthropic Messages API

- Keep Anthropic-native tools (tool_search_tool_regex, web_search, bash, etc.) in original format when translating to OpenAI format for guardrails
- Convert guardrail-returned tools back from OpenAI to Anthropic format (type=custom for user tools)
- Add TOOL_SEARCH_TOOL to ANTHROPIC_HOSTED_TOOLS enum; use prefix matching for native tool detection
- Set type=custom explicitly when mapping OpenAI function tools to AnthropicMessagesTool
- Add test for Anthropic native tools with guardrails

Made-with: Cursor

litellm/llms/anthropic/chat/guardrail_translation/handler.py

@@ -127,7 +127,15 @@ class AnthropicMessagesHandler(BaseTranslation):
             guardrailed_texts = guardrailed_inputs.get("texts", [])
             guardrailed_tools = guardrailed_inputs.get("tools")
             if guardrailed_tools is not None:
-                data["tools"] = guardrailed_tools
+                # Convert tools back from OpenAI format to Anthropic format
+                anthropic_config = AnthropicConfig()
+                anthropic_tools: List[AllAnthropicToolsValues] = []
+                for tool in guardrailed_tools:
+                    converted_tool, mcp_server = anthropic_config._map_tool_helper(tool)
+                    if converted_tool is not None:
+                        anthropic_tools.append(converted_tool)
+                    # Note: MCP servers are handled separately in the main transformation
+                data["tools"] = anthropic_tools
 
             # Step 3: Map guardrail responses back to original message structure
             await self._apply_guardrail_responses_to_input(

litellm/llms/anthropic/chat/transformation.py

@@ -55,7 +55,10 @@ from litellm.types.utils import (
     CompletionTokensDetailsWrapper,
 )
 from litellm.types.utils import Message as LitellmMessage
-from litellm.types.utils import PromptTokensDetailsWrapper, ServerToolUse
+from litellm.types.utils import (
+    PromptTokensDetailsWrapper,
+    ServerToolUse,
+)
 from litellm.utils import (
     ModelResponse,
     Usage,
@@ -420,6 +423,7 @@ class AnthropicConfig(AnthropicModelInfo, BaseConfig):
             _tool = AnthropicMessagesTool(
                 name=tool["function"]["name"],
                 input_schema=input_anthropic_schema,
+                type="custom",
             )
 
             _description = tool["function"].get("description")

litellm/llms/anthropic/experimental_pass_through/adapters/transformation.py

@@ -68,6 +68,7 @@ from litellm.litellm_core_utils.prompt_templates.common_utils import (
     parse_tool_call_arguments,
 )
 from litellm.types.llms.anthropic import (
+    ANTHROPIC_HOSTED_TOOLS,
     AllAnthropicToolsValues,
     AnthopicMessagesAssistantMessageParam,
     AnthropicFinishReason,
@@ -771,7 +772,15 @@ class LiteLLMAnthropicMessagesAdapter:
         new_tools: List[ChatCompletionToolParam] = []
         tool_name_mapping: Dict[str, str] = {}
         mapped_tool_params = ["name", "input_schema", "description", "cache_control"]
+
         for tool in tools:
+            # Check if this is an Anthropic-native tool that should be kept as-is
+            tool_type = tool.get("type", "")
+            if any(tool_type.startswith(t.value) for t in ANTHROPIC_HOSTED_TOOLS):
+                # Keep Anthropic-native tools in their original format
+                new_tools.append(tool)  # type: ignore[arg-type]
+                continue
+            
             original_name = tool["name"]
             truncated_name = truncate_tool_name(original_name)
 

litellm/types/llms/anthropic.py

@@ -639,6 +639,7 @@ class ANTHROPIC_HOSTED_TOOLS(str, Enum):
     CODE_EXECUTION = "code_execution"
     WEB_FETCH = "web_fetch"
     MEMORY = "memory"
+    TOOL_SEARCH_TOOL = "tool_search_tool"
 
 
 class ANTHROPIC_BETA_HEADER_VALUES(str, Enum):

tests/test_litellm/llms/anthropic/chat/guardrail_translation/test_anthropic_guardrail_handler.py

@@ -231,6 +231,69 @@ class TestAnthropicMessagesHandlerInputProcessing:
             assert result == responses_so_far
 
 
+    @pytest.mark.asyncio
+    async def test_process_input_messages_with_anthropic_native_tools(self):
+        """Test that Anthropic native tools (tool_search_tool_regex) are preserved correctly
+        
+        This test verifies the fix for the bug where Anthropic native tools like
+        tool_search_tool_regex_20251119 were being converted to OpenAI format and then
+        not properly converted back, causing API errors.
+        
+        The guardrail converts tools to OpenAI format for processing, then they need to be
+        converted back to Anthropic format. Native Anthropic tools should be preserved as-is,
+        while regular tools should be converted to type="custom".
+        """
+        handler = AnthropicMessagesHandler()
+        guardrail = MockPassThroughGuardrail(guardrail_name="test")
+
+        data = {
+            "model": "claude-opus-4-6",
+            "messages": [{"role": "user", "content": "What is the weather in San Francisco?"}],
+            "tools": [
+                {
+                    "type": "tool_search_tool_regex_20251119",
+                    "name": "tool_search_tool_regex"
+                },
+                {
+                    "name": "get_weather",
+                    "description": "Get the weather at a specific location",
+                    "input_schema": {
+                        "type": "object",
+                        "properties": {
+                            "location": {"type": "string"},
+                            "unit": {
+                                "type": "string",
+                                "enum": ["celsius", "fahrenheit"]
+                            }
+                        },
+                        "required": ["location"]
+                    },
+                    "defer_loading": True
+                }
+            ]
+        }
+
+        result = await handler.process_input_messages(
+            data=data,
+            guardrail_to_apply=guardrail,
+            litellm_logging_obj=MagicMock()
+        )
+
+        # Verify tools are in correct Anthropic format
+        tools = result["tools"]
+        assert len(tools) == 2
+        
+        # First tool should be preserved as Anthropic native tool
+        assert tools[0]["type"] == "tool_search_tool_regex_20251119"
+        assert tools[0]["name"] == "tool_search_tool_regex"
+        
+        # Second tool should be converted to Anthropic custom tool format
+        assert tools[1]["type"] == "custom"
+        assert tools[1]["name"] == "get_weather"
+        assert tools[1]["description"] == "Get the weather at a specific location"
+        assert "input_schema" in tools[1]
+
+
 if __name__ == "__main__":
     # Run the tests
     pytest.main([__file__, "-v"])