Merge 573b02a35f into e58a561caa

litellm/constants.py

@@ -522,7 +522,7 @@ ANTHROPIC_WEB_SEARCH_TOOL_MAX_USES = {
 
 # LiteLLM standard web search tool name
 # Used for web search interception across providers
-LITELLM_WEB_SEARCH_TOOL_NAME = "litellm_web_search"
+LITELLM_WEB_SEARCH_TOOL_NAME = "WebSearch"
 
 DEFAULT_IMAGE_ENDPOINT_MODEL = "dall-e-2"
 DEFAULT_VIDEO_ENDPOINT_MODEL = "sora-2"

litellm/integrations/websearch_interception/handler.py

@@ -86,10 +86,14 @@ class WebSearchInterceptionLogger(CustomLogger):
 
         Claude Code sends web search as a separate, standalone /v1/messages
         request with a simple prompt and only web_search tool(s). For providers
-        that don't natively support web search (e.g. github_copilot), there is
-        no need to route this through the backend LLM — we can detect the
-        pattern, execute the search via Tavily/Perplexity, and return a
-        synthetic Anthropic response immediately.
+        that don't natively support web search, we execute the search via the
+        configured provider (SearXNG/Tavily/Perplexity) and return a synthetic
+        response in native Anthropic format (server_tool_use +
+        web_search_tool_result) so Claude Code's WebSearchTool parser works.
+
+        Providers with native Anthropic Messages support (anthropic, bedrock,
+        vertex_ai, azure_ai) are skipped — their API handles web search
+        natively and returns the correct format already.
 
         Args:
             model: Model name from the request
@@ -112,12 +116,9 @@ class WebSearchInterceptionLogger(CustomLogger):
         ):
             return None
 
-        # Only short-circuit for providers without native Anthropic Messages
-        # support.  Providers that have a BaseAnthropicMessagesConfig (bedrock,
-        # vertex_ai, azure_ai, anthropic) already use the agentic loop, which
-        # includes a follow-up LLM call to synthesize the answer from search
-        # results.  Short-circuiting those would skip that synthesis step and
-        # return raw search text — a regression for existing users.
+        # Skip providers with native Anthropic Messages support — their API
+        # handles web_search_20250305 natively, returning server_tool_use +
+        # web_search_tool_result in the correct format already.
         try:
             provider_enum = LlmProviders(provider_str)
             anthropic_config = (
@@ -128,7 +129,7 @@ class WebSearchInterceptionLogger(CustomLogger):
             if anthropic_config is not None:
                 verbose_logger.debug(
                     f"WebSearchInterception: Skipping short-circuit for {provider_str} "
-                    "(provider has native Anthropic Messages support, using agentic loop)"
+                    "(provider has native web search support)"
                 )
                 return None
         except (ValueError, Exception):
@@ -161,21 +162,69 @@ class WebSearchInterceptionLogger(CustomLogger):
             )
             search_result_text = f"Search failed: {e}"
 
-        # Build synthetic Anthropic response
+        # Parse search results into structured hits for web_search_tool_result
+        search_hits = []
+        for block in search_result_text.split("\n\n"):
+            title, url, snippet = "", "", ""
+            for line in block.strip().splitlines():
+                if line.startswith("Title: "):
+                    title = line[7:]
+                elif line.startswith("URL: "):
+                    url = line[5:]
+                elif line.startswith("Snippet: "):
+                    snippet = line[9:]
+            if url:
+                hit: Dict[str, Any] = {
+                    "type": "web_search_result",
+                    "url": url,
+                    "title": title or url,
+                    "encrypted_content": snippet or "",
+                    "page_age": None,
+                }
+                search_hits.append(hit)
+
+        tool_use_id = f"srvtoolu_{str(uuid.uuid4()).replace('-', '')[:24]}"
+
+        # Build response in native Anthropic format so Claude Code's
+        # WebSearchTool parser sees server_tool_use + web_search_tool_result.
+        content: List[Dict[str, Any]] = [
+            {
+                "type": "server_tool_use",
+                "id": tool_use_id,
+                "name": "web_search",
+                "input": {"query": query},
+            },
+            {
+                "type": "web_search_tool_result",
+                "tool_use_id": tool_use_id,
+                "content": search_hits,
+            },
+            {
+                "type": "text",
+                "text": search_result_text,
+            },
+        ]
+
         response: Dict[str, Any] = {
-            "id": f"msg_{str(uuid.uuid4())}",
+            "id": f"msg_{str(uuid.uuid4()).replace('-', '')[:20]}",
             "type": "message",
             "role": "assistant",
             "model": model,
-            "content": [{"type": "text", "text": search_result_text}],
+            "content": content,
             "stop_reason": "end_turn",
             "stop_sequence": None,
-            "usage": {"input_tokens": 0, "output_tokens": 0},
+            "usage": {
+                "input_tokens": 0,
+                "output_tokens": 0,
+                "server_tool_use": {
+                    "web_search_requests": 1,
+                },
+            },
         }
 
         verbose_logger.debug(
             "WebSearchInterception: Short-circuit search completed, "
-            f"returning synthetic response ({len(search_result_text)} chars)"
+            f"returning native format ({len(search_hits)} hits)"
         )
         return response
 
@@ -880,8 +929,9 @@ class WebSearchInterceptionLogger(CustomLogger):
                 )
                 llm_router = None
 
-            # Determine search provider from router's search_tools
+            # Determine search provider and api_base from router's search_tools
             search_provider: Optional[str] = None
+            api_base: Optional[str] = None
             if llm_router is not None and hasattr(llm_router, "search_tools"):
                 if self.search_tool_name:
                     # Find specific search tool by name
@@ -892,9 +942,9 @@ class WebSearchInterceptionLogger(CustomLogger):
                     ]
                     if matching_tools:
                         search_tool = matching_tools[0]
-                        search_provider = search_tool.get("litellm_params", {}).get(
-                            "search_provider"
-                        )
+                        litellm_params = search_tool.get("litellm_params", {})
+                        search_provider = litellm_params.get("search_provider")
+                        api_base = litellm_params.get("api_base")
                         verbose_logger.debug(
                             f"WebSearchInterception: Found search tool '{self.search_tool_name}' "
                             f"with provider '{search_provider}'"
@@ -908,9 +958,9 @@ class WebSearchInterceptionLogger(CustomLogger):
                 # If no specific tool or not found, use first available
                 if not search_provider and llm_router.search_tools:
                     first_tool = llm_router.search_tools[0]
-                    search_provider = first_tool.get("litellm_params", {}).get(
-                        "search_provider"
-                    )
+                    litellm_params = first_tool.get("litellm_params", {})
+                    search_provider = litellm_params.get("search_provider")
+                    api_base = api_base or litellm_params.get("api_base")
                     verbose_logger.debug(
                         f"WebSearchInterception: Using first available search tool with provider '{search_provider}'"
                     )
@@ -926,7 +976,7 @@ class WebSearchInterceptionLogger(CustomLogger):
             verbose_logger.debug(
                 f"WebSearchInterception: Executing search for '{query}' using provider '{search_provider}'"
             )
-            result = await litellm.asearch(query=query, search_provider=search_provider)
+            result = await litellm.asearch(query=query, search_provider=search_provider, api_base=api_base)
 
             # Format using transformation function
             search_result_text = WebSearchTransformation.format_search_response(result)

litellm/integrations/websearch_interception/tools.py

@@ -13,36 +13,28 @@ from litellm.constants import LITELLM_WEB_SEARCH_TOOL_NAME
 
 def get_litellm_web_search_tool() -> Dict[str, Any]:
     """
-    Get the standard LiteLLM web search tool definition.
+    Get the web search tool definition in Anthropic format.
 
-    This is the canonical tool definition that all native web search tools
-    (like Anthropic's web_search_20250305, Claude Code's web_search, etc.)
-    are converted to for interception.
+    Uses the same name and schema that Claude Code expects so it appears
+    as the native WebSearch tool in the client.
 
     Returns:
-        Dict containing the Anthropic-style tool definition with:
-        - name: Tool name
-        - description: What the tool does
-        - input_schema: JSON schema for tool parameters
-
-    Example:
-        >>> tool = get_litellm_web_search_tool()
-        >>> tool['name']
-        'litellm_web_search'
+        Dict containing the Anthropic-style tool definition.
     """
     return {
         "name": LITELLM_WEB_SEARCH_TOOL_NAME,
         "description": (
-            "Search the web for information. Use this when you need current "
-            "information or answers to questions that require up-to-date data."
+            "Search the web for current information. Returns search results "
+            "with titles, URLs, and snippets. Use this tool when you need "
+            "up-to-date information beyond your knowledge cutoff."
         ),
         "input_schema": {
             "type": "object",
             "properties": {
                 "query": {
                     "type": "string",
-                    "description": "The search query to execute",
-                }
+                    "description": "The search query to use",
+                },
             },
             "required": ["query"],
         },
@@ -51,7 +43,7 @@ def get_litellm_web_search_tool() -> Dict[str, Any]:
 
 def get_litellm_web_search_tool_openai() -> Dict[str, Any]:
     """
-    Get the standard LiteLLM web search tool definition in OpenAI format.
+    Get the web search tool definition in OpenAI format.
 
     Used by async_pre_call_deployment_hook which runs in the chat completions
     path where tools must be in OpenAI format (type: "function" with
@@ -65,16 +57,17 @@ def get_litellm_web_search_tool_openai() -> Dict[str, Any]:
         "function": {
             "name": LITELLM_WEB_SEARCH_TOOL_NAME,
             "description": (
-                "Search the web for information. Use this when you need current "
-                "information or answers to questions that require up-to-date data."
+                "Search the web for current information. Returns search results "
+                "with titles, URLs, and snippets. Use this tool when you need "
+                "up-to-date information beyond your knowledge cutoff."
             ),
             "parameters": {
                 "type": "object",
                 "properties": {
                     "query": {
                         "type": "string",
-                        "description": "The search query to execute",
-                    }
+                        "description": "The search query to use",
+                    },
                 },
                 "required": ["query"],
             },
@@ -82,45 +75,35 @@ def get_litellm_web_search_tool_openai() -> Dict[str, Any]:
     }
 
 
+_WEB_SEARCH_NAMES = {LITELLM_WEB_SEARCH_TOOL_NAME, "WebSearch", "web_search", "litellm_web_search"}
+
+
 def is_web_search_tool_chat_completion(tool: Dict[str, Any]) -> bool:
     """
-    Check if a tool is a web search tool for Chat Completions API (strict check).
+    Check if a tool is a web search tool for Chat Completions API.
 
-    This is a stricter version that ONLY checks for the exact LiteLLM web search tool name.
-    Use this for Chat Completions API to avoid false positives with user-defined tools.
-
-    Detects ONLY:
-    - LiteLLM standard: name == "litellm_web_search" (Anthropic format)
-    - OpenAI format: type == "function" with function.name == "litellm_web_search"
+    Detects:
+    - Anthropic format: name in {litellm_web_search, WebSearch, web_search}
+    - OpenAI format: type == "function" with function.name in same set
 
     Args:
         tool: Tool dictionary to check
 
     Returns:
-        True if tool is exactly the LiteLLM web search tool
-
-    Example:
-        >>> is_web_search_tool_chat_completion({"name": "litellm_web_search"})
-        True
-        >>> is_web_search_tool_chat_completion({"type": "function", "function": {"name": "litellm_web_search"}})
-        True
-        >>> is_web_search_tool_chat_completion({"name": "web_search"})
-        False
-        >>> is_web_search_tool_chat_completion({"name": "WebSearch"})
-        False
+        True if tool is a web search tool
     """
     tool_name = tool.get("name", "")
     tool_type = tool.get("type", "")
 
-    # Check for OpenAI format: {"type": "function", "function": {"name": "litellm_web_search"}}
+    # Check for OpenAI format
     if tool_type == "function" and "function" in tool:
         function_def = tool.get("function", {})
         function_name = function_def.get("name", "")
-        if function_name == LITELLM_WEB_SEARCH_TOOL_NAME:
+        if function_name in _WEB_SEARCH_NAMES:
             return True
 
-    # Check for LiteLLM standard tool (Anthropic format)
-    if tool_name == LITELLM_WEB_SEARCH_TOOL_NAME:
+    # Check for Anthropic format
+    if tool_name in _WEB_SEARCH_NAMES:
         return True
 
     return False
@@ -175,8 +158,8 @@ def is_web_search_tool(tool: Dict[str, Any]) -> bool:
     if tool_name == "web_search" and tool_type:
         return True
 
-    # Check for legacy WebSearch format
-    if tool_name == "WebSearch":
+    # Check for legacy names
+    if tool_name in ("WebSearch", "litellm_web_search"):
         return True
 
     return False

litellm/integrations/websearch_interception/transformation.py

@@ -101,11 +101,11 @@ class WebSearchTransformation:
                 block_input = getattr(block, "input", {})
 
             # Check for LiteLLM standard or legacy web search tools
-            # Handles: litellm_web_search, WebSearch, web_search
             if block_type == "tool_use" and block_name in (
                 LITELLM_WEB_SEARCH_TOOL_NAME,
                 "WebSearch",
                 "web_search",
+                "litellm_web_search",
             ):
                 # Convert to dict for easier handling
                 tool_call = {
@@ -195,6 +195,7 @@ class WebSearchTransformation:
                 LITELLM_WEB_SEARCH_TOOL_NAME,
                 "WebSearch",
                 "web_search",
+                "litellm_web_search",
             ):
                 # Parse arguments (might be JSON string)
                 if isinstance(function_arguments, str):

litellm/llms/anthropic/experimental_pass_through/messages/fake_stream_iterator.py

@@ -131,6 +131,18 @@ class FakeAnthropicMessagesStreamIterator:
                 f"event: content_block_delta\ndata: {json.dumps(content_block_delta)}\n\n".encode()
             )
 
+        elif block_type in ("server_tool_use", "web_search_tool_result"):
+            # Emit the full block as content_block_start — same as
+            # Anthropic's native streaming format for server-side tools.
+            content_block_start = {
+                "type": "content_block_start",
+                "index": index,
+                "content_block": block_dict,
+            }
+            chunks.append(
+                f"event: content_block_start\ndata: {json.dumps(content_block_start)}\n\n".encode()
+            )
+
         content_block_stop = {"type": "content_block_stop", "index": index}
         chunks.append(
             f"event: content_block_stop\ndata: {json.dumps(content_block_stop)}\n\n".encode()

tests/test_litellm/integrations/websearch_interception/test_websearch_short_circuit.py

@@ -3,6 +3,9 @@ Unit tests for WebSearch Short-Circuit
 
 Tests the short-circuit path that detects web-search-only /v1/messages requests
 and executes the search directly without routing through the backend LLM.
+
+The response uses native Anthropic format (server_tool_use + web_search_tool_result)
+so Claude Code's WebSearchTool parser works correctly.
 """
 
 from unittest.mock import AsyncMock, patch
@@ -48,11 +51,103 @@ class TestTryShortCircuitSearch:
         assert result["type"] == "message"
         assert result["role"] == "assistant"
         assert result["stop_reason"] == "end_turn"
-        assert len(result["content"]) == 1
-        assert result["content"][0]["type"] == "text"
-        assert "Result" in result["content"][0]["text"]
+        # Native format: server_tool_use + web_search_tool_result + text
+        assert result["content"][0]["type"] == "server_tool_use"
+        assert result["content"][0]["name"] == "web_search"
+        assert result["content"][1]["type"] == "web_search_tool_result"
+        assert result["content"][2]["type"] == "text"
+        assert "Result" in result["content"][2]["text"]
         mock_search.assert_called_once_with("Search for Claude Code releases")
 
+    @pytest.mark.asyncio
+    async def test_native_format_search_hits(self):
+        """Search results are structured as web_search_result hits"""
+        logger = WebSearchInterceptionLogger(enabled_providers=["github_copilot"])
+
+        with patch.object(
+            logger, "_execute_search", new_callable=AsyncMock
+        ) as mock_search:
+            mock_search.return_value = (
+                "Title: First Result\nURL: https://example.com/1\nSnippet: first\n\n"
+                "Title: Second Result\nURL: https://example.com/2\nSnippet: second"
+            )
+
+            result = await logger.try_short_circuit_search(
+                model="github_copilot/claude-sonnet-4",
+                messages=[{"role": "user", "content": "Search query"}],
+                tools=[{"type": "web_search_20250305", "name": "web_search"}],
+                custom_llm_provider="github_copilot",
+            )
+
+        assert result is not None
+        hits = result["content"][1]["content"]
+        assert len(hits) == 2
+        assert hits[0]["type"] == "web_search_result"
+        assert hits[0]["url"] == "https://example.com/1"
+        assert hits[0]["title"] == "First Result"
+        assert hits[1]["url"] == "https://example.com/2"
+
+    @pytest.mark.asyncio
+    async def test_server_tool_use_has_query(self):
+        """server_tool_use block contains the original search query"""
+        logger = WebSearchInterceptionLogger(enabled_providers=["github_copilot"])
+
+        with patch.object(
+            logger, "_execute_search", new_callable=AsyncMock
+        ) as mock_search:
+            mock_search.return_value = "Title: R\nURL: https://x.com\nSnippet: s"
+
+            result = await logger.try_short_circuit_search(
+                model="github_copilot/claude-sonnet-4",
+                messages=[{"role": "user", "content": "trending AI topics"}],
+                tools=[{"type": "web_search_20250305", "name": "web_search"}],
+                custom_llm_provider="github_copilot",
+            )
+
+        stu = result["content"][0]
+        assert stu["type"] == "server_tool_use"
+        assert stu["name"] == "web_search"
+        assert stu["input"]["query"] == "trending AI topics"
+        assert stu["id"].startswith("srvtoolu_")
+
+    @pytest.mark.asyncio
+    async def test_tool_use_id_links_blocks(self):
+        """server_tool_use.id matches web_search_tool_result.tool_use_id"""
+        logger = WebSearchInterceptionLogger(enabled_providers=["github_copilot"])
+
+        with patch.object(
+            logger, "_execute_search", new_callable=AsyncMock
+        ) as mock_search:
+            mock_search.return_value = "Title: R\nURL: https://x.com\nSnippet: s"
+
+            result = await logger.try_short_circuit_search(
+                model="github_copilot/claude-sonnet-4",
+                messages=[{"role": "user", "content": "query"}],
+                tools=[{"type": "web_search_20250305", "name": "web_search"}],
+                custom_llm_provider="github_copilot",
+            )
+
+        assert result["content"][0]["id"] == result["content"][1]["tool_use_id"]
+
+    @pytest.mark.asyncio
+    async def test_usage_includes_web_search_requests(self):
+        """Usage includes server_tool_use.web_search_requests count"""
+        logger = WebSearchInterceptionLogger(enabled_providers=["github_copilot"])
+
+        with patch.object(
+            logger, "_execute_search", new_callable=AsyncMock
+        ) as mock_search:
+            mock_search.return_value = "Title: R\nURL: https://x.com\nSnippet: s"
+
+            result = await logger.try_short_circuit_search(
+                model="github_copilot/claude-sonnet-4",
+                messages=[{"role": "user", "content": "query"}],
+                tools=[{"type": "web_search_20250305", "name": "web_search"}],
+                custom_llm_provider="github_copilot",
+            )
+
+        assert result["usage"]["server_tool_use"]["web_search_requests"] == 1
+
     @pytest.mark.asyncio
     async def test_does_not_short_circuit_mixed_tools(self):
         """Mix of web_search and other tools → NOT short-circuited"""
@@ -115,27 +210,42 @@ class TestTryShortCircuitSearch:
         assert result is None
 
     @pytest.mark.asyncio
-    async def test_does_not_short_circuit_bedrock(self):
-        """Bedrock has native agentic loop support → NOT short-circuited.
+    async def test_does_not_short_circuit_native_providers(self):
+        """Providers with native Anthropic Messages support (anthropic, bedrock,
+        vertex_ai) are skipped — their API handles web search natively."""
+        for provider in ["anthropic", "bedrock"]:
+            logger = WebSearchInterceptionLogger(
+                enabled_providers=[provider, "github_copilot"]
+            )
 
-        Providers with a BaseAnthropicMessagesConfig (bedrock, vertex_ai, etc.)
-        use the agentic loop which includes a follow-up LLM synthesis step.
-        The short-circuit must not fire for them.
-        """
-        logger = WebSearchInterceptionLogger(
-            enabled_providers=["bedrock", "github_copilot"]
-        )
+            result = await logger.try_short_circuit_search(
+                model=f"{provider}/claude-sonnet-4",
+                messages=[{"role": "user", "content": "search query"}],
+                tools=[{"type": "web_search_20250305", "name": "web_search"}],
+                custom_llm_provider=provider,
+            )
 
-        result = await logger.try_short_circuit_search(
-            model="bedrock/us.anthropic.claude-sonnet-4-5-20250929-v1:0",
-            messages=[{"role": "user", "content": "Search for something"}],
-            tools=[
-                {"type": "web_search_20250305", "name": "web_search", "max_uses": 8}
-            ],
-            custom_llm_provider="bedrock",
-        )
+            assert result is None, f"Short-circuit should NOT fire for native provider {provider}"
 
-        assert result is None
+    @pytest.mark.asyncio
+    async def test_short_circuits_non_native_providers(self):
+        """Non-native providers (github_copilot, etc.) get short-circuited."""
+        logger = WebSearchInterceptionLogger(enabled_providers=["github_copilot"])
+
+        with patch.object(
+            logger, "_execute_search", new_callable=AsyncMock
+        ) as mock_search:
+            mock_search.return_value = "Title: R\nURL: https://x.com\nSnippet: s"
+
+            result = await logger.try_short_circuit_search(
+                model="github_copilot/claude-sonnet-4",
+                messages=[{"role": "user", "content": "search query"}],
+                tools=[{"type": "web_search_20250305", "name": "web_search"}],
+                custom_llm_provider="github_copilot",
+            )
+
+        assert result is not None
+        assert result["content"][0]["type"] == "server_tool_use"
 
     @pytest.mark.asyncio
     async def test_does_not_short_circuit_no_messages(self):
@@ -173,7 +283,10 @@ class TestTryShortCircuitSearch:
             )
 
         assert result is not None
-        assert "Search failed" in result["content"][0]["text"]
+        # Error text is in the last content block (text)
+        text_block = result["content"][-1]
+        assert text_block["type"] == "text"
+        assert "Search failed" in text_block["text"]
 
     @pytest.mark.asyncio
     async def test_response_has_valid_structure(self):
@@ -183,7 +296,7 @@ class TestTryShortCircuitSearch:
         with patch.object(
             logger, "_execute_search", new_callable=AsyncMock
         ) as mock_search:
-            mock_search.return_value = "search results here"
+            mock_search.return_value = "Title: R\nURL: https://x.com\nSnippet: test"
 
             result = await logger.try_short_circuit_search(
                 model="github_copilot/claude-sonnet-4",
@@ -203,11 +316,8 @@ class TestTryShortCircuitSearch:
         assert result["stop_sequence"] is None
         assert "usage" in result
         assert "content" in result
-
-
-# ---------------------------------------------------------------------------
-# Query extraction tests
-# ---------------------------------------------------------------------------
+        # Content has 3 blocks: server_tool_use, web_search_tool_result, text
+        assert len(result["content"]) == 3
 
 
 # ---------------------------------------------------------------------------
@@ -237,7 +347,7 @@ class TestShortCircuitEntryPoint:
 
     @pytest.mark.asyncio
     async def test_returns_dict_when_not_streaming(self):
-        """Non-streaming short-circuit → returns dict"""
+        """Non-streaming short-circuit → returns dict with native format"""
         from litellm.llms.anthropic.experimental_pass_through.messages.handler import (
             _try_websearch_short_circuit,
         )
@@ -246,7 +356,7 @@ class TestShortCircuitEntryPoint:
         with patch.object(
             logger, "_execute_search", new_callable=AsyncMock
         ) as mock_search:
-            mock_search.return_value = "results"
+            mock_search.return_value = "Title: R\nURL: https://x.com\nSnippet: results"
             with patch("litellm.callbacks", [logger]):
                 result = await _try_websearch_short_circuit(
                     model="github_copilot/claude-sonnet-4",
@@ -257,7 +367,8 @@ class TestShortCircuitEntryPoint:
                 )
 
         assert isinstance(result, dict)
-        assert result["content"][0]["text"] == "results"
+        assert result["content"][0]["type"] == "server_tool_use"
+        assert result["content"][1]["type"] == "web_search_tool_result"
 
     @pytest.mark.asyncio
     async def test_returns_stream_iterator_when_streaming(self):
@@ -273,7 +384,9 @@ class TestShortCircuitEntryPoint:
         with patch.object(
             logger, "_execute_search", new_callable=AsyncMock
         ) as mock_search:
-            mock_search.return_value = "streaming results"
+            mock_search.return_value = (
+                "Title: Result\nURL: https://example.com\nSnippet: streaming results"
+            )
             with patch("litellm.callbacks", [logger]):
                 result = await _try_websearch_short_circuit(
                     model="github_copilot/claude-sonnet-4",
@@ -291,12 +404,12 @@ class TestShortCircuitEntryPoint:
             chunks.append(chunk)
 
         assert len(chunks) > 0
-        # First chunk should be message_start
         assert b"event: message_start" in chunks[0]
-        # Last chunk should be message_stop
         assert b"event: message_stop" in chunks[-1]
-        # Should contain the search results text
+        # Should contain server_tool_use and web_search_tool_result blocks
         all_data = b"".join(chunks)
+        assert b"server_tool_use" in all_data
+        assert b"web_search_tool_result" in all_data
         assert b"streaming results" in all_data
 
     @pytest.mark.asyncio
@@ -321,12 +434,7 @@ class TestShortCircuitEntryPoint:
 
     @pytest.mark.asyncio
     async def test_uses_original_stream_not_hook_converted(self):
-        """Verify that the entry point passes original_stream to the short-circuit.
-
-        The pre-request hook converts stream=True → stream=False for the agentic
-        loop. The short-circuit must use the ORIGINAL stream value so streaming
-        callers get SSE events instead of a plain dict.
-        """
+        """Verify that the entry point passes original_stream to the short-circuit."""
         from litellm.llms.anthropic.experimental_pass_through.messages.fake_stream_iterator import (
             FakeAnthropicMessagesStreamIterator,
         )
@@ -338,47 +446,14 @@ class TestShortCircuitEntryPoint:
         with patch.object(
             logger, "_execute_search", new_callable=AsyncMock
         ) as mock_search:
-            mock_search.return_value = "streaming results"
+            mock_search.return_value = "Title: R\nURL: https://x.com\nSnippet: s"
             with patch("litellm.callbacks", [logger]):
-                # Simulate what anthropic_messages() does: original_stream=True
-                # is passed to the short-circuit, even though the hook would have
-                # already converted stream to False in request_kwargs.
                 result = await _try_websearch_short_circuit(
                     model="github_copilot/claude-sonnet-4",
                     messages=[{"role": "user", "content": "search query"}],
                     tools=[{"type": "web_search_20250305", "name": "web_search"}],
                     custom_llm_provider="github_copilot",
-                    stream=True,  # original_stream, NOT the hook-converted value
+                    stream=True,
                 )
 
-        # Must return a stream iterator, not a plain dict
         assert isinstance(result, FakeAnthropicMessagesStreamIterator)
-
-    @pytest.mark.asyncio
-    async def test_short_circuits_with_provider_from_model_string(self):
-        """Provider embedded in model string (custom_llm_provider=None) should
-        still fire the short-circuit when the caller propagates the derived
-        provider.
-        """
-        from litellm.llms.anthropic.experimental_pass_through.messages.handler import (
-            _try_websearch_short_circuit,
-        )
-
-        logger = WebSearchInterceptionLogger(enabled_providers=["github_copilot"])
-        with patch.object(
-            logger, "_execute_search", new_callable=AsyncMock
-        ) as mock_search:
-            mock_search.return_value = "results"
-            with patch("litellm.callbacks", [logger]):
-                # Simulate the caller having derived custom_llm_provider from
-                # the model string before calling _try_websearch_short_circuit
-                result = await _try_websearch_short_circuit(
-                    model="github_copilot/claude-sonnet-4",
-                    messages=[{"role": "user", "content": "search query"}],
-                    tools=[{"type": "web_search_20250305", "name": "web_search"}],
-                    custom_llm_provider="github_copilot",
-                    stream=False,
-                )
-
-        assert result is not None
-        assert result["content"][0]["text"] == "results"