fix(anthropic): keep advisor_20260301 tool native through guardrails

litellm/llms/anthropic/chat/transformation.py

@@ -1027,6 +1027,24 @@ class AnthropicConfig(AnthropicModelInfo, BaseConfig):
                 _computer_tool["display_number"] = _display_number
 
             returned_tool = _computer_tool
+        # Before the hosted-tools prefix match so the advisor keeps its model validation.
+        elif tool["type"] == ANTHROPIC_ADVISOR_TOOL_TYPE:
+            from litellm.types.llms.anthropic import AnthropicAdvisorTool
+
+            _tool_dict: Final = cast(dict, tool)
+            advisor_model: Final = _tool_dict.get("model")
+            if not isinstance(advisor_model, str):
+                raise ValueError("Advisor tool must have a valid model")
+            _advisor_tool: Final = AnthropicAdvisorTool(
+                type=ANTHROPIC_ADVISOR_TOOL_TYPE,
+                name="advisor",
+                model=advisor_model,
+            )
+            if _tool_dict.get("max_uses") is not None:
+                _advisor_tool["max_uses"] = _tool_dict["max_uses"]
+            if _tool_dict.get("caching") is not None:
+                _advisor_tool["caching"] = _tool_dict["caching"]
+            returned_tool = _advisor_tool
         elif any(tool["type"].startswith(t) for t in ANTHROPIC_HOSTED_TOOLS):
             function_name_obj: Final = tool.get("name", tool.get("function", {}).get("name"))
             if function_name_obj is None or not isinstance(function_name_obj, str):
@@ -1071,23 +1089,6 @@ class AnthropicConfig(AnthropicModelInfo, BaseConfig):
                 type="tool_search_tool_bm25_20251119",
                 name=tool_name,
             )
-        elif tool["type"] == ANTHROPIC_ADVISOR_TOOL_TYPE:
-            from litellm.types.llms.anthropic import AnthropicAdvisorTool
-
-            _tool_dict: Final = cast(dict, tool)
-            advisor_model: Final = _tool_dict.get("model")
-            if not isinstance(advisor_model, str):
-                raise ValueError("Advisor tool must have a valid model")
-            _advisor_tool: Final = AnthropicAdvisorTool(
-                type=ANTHROPIC_ADVISOR_TOOL_TYPE,
-                name="advisor",
-                model=advisor_model,
-            )
-            if _tool_dict.get("max_uses") is not None:
-                _advisor_tool["max_uses"] = _tool_dict["max_uses"]
-            if _tool_dict.get("caching") is not None:
-                _advisor_tool["caching"] = _tool_dict["caching"]
-            returned_tool = _advisor_tool
         if returned_tool is None and mcp_server is None:
             raise ValueError(f"Unsupported tool type: {tool['type']}")
 

litellm/types/llms/anthropic.py

@@ -797,6 +797,7 @@ class ANTHROPIC_HOSTED_TOOLS(str, Enum):
     WEB_FETCH = "web_fetch"
     MEMORY = "memory"
     TOOL_SEARCH_TOOL = "tool_search_tool"
+    ADVISOR = "advisor"
 
 
 class ANTHROPIC_BETA_HEADER_VALUES(str, Enum):

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

@@ -1602,6 +1602,52 @@ class TestAnthropicMessagesHandlerInputProcessing:
         assert tools[1]["description"] == "Get the weather at a specific location"
         assert "input_schema" in tools[1]
 
+    @pytest.mark.asyncio
+    async def test_process_input_messages_keeps_advisor_tool_native(self):
+        """The advisor_20260301 server tool must survive the guardrail round trip untouched.
+
+        Claude Code sends the advisor beside its function tools. The guardrail path
+        translates tools to OpenAI format and back; before the fix the advisor was not
+        recognised as a hosted tool and came back as an empty custom tool, so Anthropic
+        treated it as a client tool and Claude Code failed with
+        "No such tool available: advisor".
+        """
+        handler = AnthropicMessagesHandler()
+        guardrail = MockPassThroughGuardrail(guardrail_name="test")
+        advisor_tool = {
+            "type": "advisor_20260301",
+            "name": "advisor",
+            "model": "claude-opus-4-6",
+            "defer_loading": True,
+        }
+        data = {
+            "model": "claude-sonnet-4-6",
+            "messages": [{"role": "user", "content": "Consult the advisor, then answer: what is 2+2?"}],
+            "tools": [
+                advisor_tool,
+                {
+                    "name": "Bash",
+                    "description": "Run a shell command",
+                    "input_schema": {
+                        "type": "object",
+                        "properties": {"command": {"type": "string"}},
+                        "required": ["command"],
+                    },
+                },
+            ],
+        }
+
+        result = await handler.process_input_messages(
+            data=data, guardrail_to_apply=guardrail, litellm_logging_obj=MagicMock()
+        )
+
+        tools = result["tools"]
+        assert len(tools) == 2
+        assert tools[0] == advisor_tool
+        assert tools[1]["type"] == "custom"
+        assert tools[1]["name"] == "Bash"
+        assert tools[1]["description"] == "Run a shell command"
+
 
 class ToolAppendingGuardrail(CustomGuardrail):
     """Guardrail that appends a new OpenAI-format function tool, mimicking a

tests/unit/llms/anthropic/messages/adapters/test_anthropic_messages_adapters_transformation.py

@@ -2835,6 +2835,20 @@ def test_translate_anthropic_tools_to_openai_passes_openai_function_tools_throug
     assert result == [openai_tool]
 
 
+def test_translate_anthropic_tools_to_openai_keeps_advisor_tool_native():
+    """The advisor_20260301 server tool has no OpenAI equivalent and must pass through verbatim."""
+    advisor_tool = {
+        "type": "advisor_20260301",
+        "name": "advisor",
+        "model": "claude-opus-4-6",
+        "defer_loading": True,
+    }
+    adapter = LiteLLMAnthropicMessagesAdapter()
+    result, tool_name_mapping = adapter.translate_anthropic_tools_to_openai(tools=[advisor_tool], model=None)
+    assert result == [advisor_tool]
+    assert tool_name_mapping == {}
+
+
 def test_translate_completion_input_params_keeps_provider_native_tools():
     """/v1/messages request translation must keep router-merged provider-native tools in kwargs['tools'] (LIT-6286)."""
     adapter = AnthropicAdapter()