diff --git a/litellm/__init__.py b/litellm/__init__.py
index 2b7d26de129..eec28a58364 100644
--- a/litellm/__init__.py
+++ b/litellm/__init__.py
@@ -137,6 +137,7 @@ _custom_logger_compatible_callbacks_literal = Literal[
     "focus",
     "posthog",
     "levo",
+    "tool_search_pre_call_hook",
 ]
 cold_storage_custom_logger: Optional[_custom_logger_compatible_callbacks_literal] = None
 logged_real_time_event_types: Optional[Union[List[str], Literal["*"]]] = None
diff --git a/litellm/integrations/tool_search_pre_call_hook.py b/litellm/integrations/tool_search_pre_call_hook.py
new file mode 100644
index 00000000000..d07ce95492e
--- /dev/null
+++ b/litellm/integrations/tool_search_pre_call_hook.py
@@ -0,0 +1,459 @@
+"""
+Tool Search Pre-Call Hook
+
+Client-side tool search (BM25 + regex) for providers that don't support
+Anthropic's server-side tool search API.
+
+Automatically handles the tool_search agentic loop - when the model calls
+tool_search, this hook executes the search and continues the conversation
+with expanded tools until the model stops calling tool_search.
+"""
+
+import math
+import re
+from collections import Counter
+from typing import Any, Dict, List, Optional, Tuple
+
+import litellm
+from litellm._logging import verbose_logger
+from litellm.integrations.custom_logger import CustomLogger
+from litellm.types.utils import CallTypes, LLMResponseTypes
+
+
+class ToolSearchPreCallHook(CustomLogger):
+    """
+    Hook that handles client-side tool search automatically.
+
+    When a model calls tool_search, this hook:
+    1. Executes local BM25/regex search on deferred tools
+    2. Expands discovered tools into the tools list
+    3. Automatically continues the conversation
+    4. Returns final response when model stops calling tool_search
+    """
+
+    SUPPORTED_PROVIDERS = {"anthropic"}
+    MAX_TOOL_SEARCH_ITERATIONS = 5  # Prevent infinite loops
+
+    async def async_pre_call_deployment_hook(
+        self, kwargs: Dict[str, Any], call_type: Optional[CallTypes]
+    ) -> Optional[dict]:
+        """
+        Transform request before sending to provider.
+
+        - Remove deferred tools (don't send many tools to provider)
+        - Replace tool_search_tool with regular function tool
+        - Store deferred tools for later search execution
+        """
+        if call_type != CallTypes.anthropic_messages:
+            return None
+
+        tools = kwargs.get("tools")
+        if not tools:
+            return None
+
+        # Detect tool search tool
+        tool_search_config = self._detect_tool_search(tools)
+        if not tool_search_config:
+            return None
+
+        # Check provider support
+        model = kwargs.get("model", "")
+        try:
+            _, custom_llm_provider, _, _ = litellm.get_llm_provider(
+                model=model,
+                custom_llm_provider=kwargs.get("custom_llm_provider"),
+            )
+        except Exception:
+            custom_llm_provider = None
+
+        if custom_llm_provider in self.SUPPORTED_PROVIDERS:
+            return None  # Pass through to server-side
+
+        verbose_logger.debug(
+            f"ToolSearchPreCallHook: Client-side tool search for provider={custom_llm_provider}"
+        )
+
+        modified_tools, deferred_tools = self._prepare_tools(tools, tool_search_config)
+        kwargs["tools"] = modified_tools
+        kwargs["_tool_search_config"] = tool_search_config
+        kwargs["_deferred_tools"] = deferred_tools
+        kwargs["_tool_search_iteration"] = kwargs.get("_tool_search_iteration", 0)
+        kwargs["_original_tools"] = tools  # Keep original for reference
+
+        return kwargs
+
+    async def async_post_call_success_deployment_hook(
+        self,
+        request_data: dict,
+        response: LLMResponseTypes,
+        call_type: Optional[CallTypes],
+    ) -> Optional[LLMResponseTypes]:
+        """
+        Handle tool_search calls automatically.
+
+        When model calls tool_search:
+        1. Execute local BM25/regex search
+        2. Expand discovered tools
+        3. Continue conversation automatically
+        4. Return final response
+        """
+        if call_type != CallTypes.anthropic_messages:
+            return None
+
+        tool_search_config = request_data.get("_tool_search_config")
+        deferred_tools = request_data.get("_deferred_tools")
+        iteration = request_data.get("_tool_search_iteration", 0)
+
+        if not tool_search_config or not deferred_tools:
+            return None
+
+        # Check iteration limit
+        if iteration >= self.MAX_TOOL_SEARCH_ITERATIONS:
+            verbose_logger.warning(
+                f"ToolSearchPreCallHook: Max iterations ({self.MAX_TOOL_SEARCH_ITERATIONS}) reached"
+            )
+            return None
+
+        tool_search_name = tool_search_config.get("name", "tool_search")
+        search_type = tool_search_config.get("search_type", "bm25")
+
+        # Get content from response
+        content = self._get_response_content(response)
+        if not content:
+            return None
+
+        # Find tool_search calls and execute searches
+        tool_search_calls = []
+        expanded_tool_names = set()
+
+        for block in content:
+            block_type, block_name, block_id, block_input = self._parse_block(block)
+
+            if block_type == "tool_use" and block_name == tool_search_name:
+                query = block_input.get("query", "") if isinstance(block_input, dict) else ""
+
+                # Execute local search
+                search_engine = ClientSideToolSearch(deferred_tools)
+                results = search_engine.search(query, search_type)
+
+                verbose_logger.debug(
+                    f"ToolSearchPreCallHook: Search '{query}' found {len(results)} tool(s)"
+                )
+
+                tool_search_calls.append({
+                    "tool_use_id": block_id,
+                    "query": query,
+                    "results": results,
+                })
+
+                # Track which tools to expand
+                for ref in results:
+                    expanded_tool_names.add(ref.get("tool_name"))
+
+        # If no tool_search calls, return as-is
+        if not tool_search_calls:
+            return None
+
+        # Build expanded tools list
+        current_tools = request_data.get("tools", [])
+        deferred_dict = {t.get("name"): t for t in deferred_tools}
+
+        for tool_name in expanded_tool_names:
+            if tool_name in deferred_dict:
+                # Add tool without defer_loading flag
+                tool = deferred_dict[tool_name].copy()
+                tool.pop("defer_loading", None)
+                # Only add if not already present
+                if not any(t.get("name") == tool_name for t in current_tools):
+                    current_tools.append(tool)
+
+        # Build messages for follow-up call
+        messages = request_data.get("messages", [])
+
+        # Add assistant response with tool_use
+        assistant_content = self._build_assistant_content(content)
+        messages = messages + [{"role": "assistant", "content": assistant_content}]
+
+        # Add tool_results for each search
+        tool_results = []
+        for call in tool_search_calls:
+            found_names = [r.get("tool_name") for r in call["results"]]
+            result_text = f"Found {len(found_names)} tool(s): {', '.join(found_names)}. These tools are now available."
+            tool_results.append({
+                "type": "tool_result",
+                "tool_use_id": call["tool_use_id"],
+                "content": result_text,
+            })
+
+        messages = messages + [{"role": "user", "content": tool_results}]
+
+        # Make follow-up call with expanded tools
+        verbose_logger.debug(
+            f"ToolSearchPreCallHook: Continuing with {len(current_tools)} tools (iteration {iteration + 1})"
+        )
+
+        # Import here to avoid circular imports
+        from litellm.llms.anthropic.experimental_pass_through.messages.handler import (
+            anthropic_messages,
+        )
+
+        # Prepare follow-up request
+        follow_up_kwargs = {
+            "model": request_data.get("model"),
+            "messages": messages,
+            "max_tokens": request_data.get("max_tokens", 1024),
+            "tools": current_tools,
+            "_tool_search_config": tool_search_config,
+            "_deferred_tools": deferred_tools,
+            "_tool_search_iteration": iteration + 1,
+        }
+
+        # Copy other relevant params
+        for key in ["temperature", "top_p", "stop_sequences", "stream"]:
+            if key in request_data:
+                follow_up_kwargs[key] = request_data[key]
+
+        # Make recursive call
+        return await anthropic_messages(**follow_up_kwargs)
+
+    async def async_post_call_streaming_deployment_hook(
+        self,
+        request_data: dict,
+        response_chunk: Any,
+        call_type: Optional[CallTypes],
+    ) -> Optional[Any]:
+        """Handle streaming responses."""
+        # Streaming with tool_search is complex - pass through for now
+        return None
+
+    def _detect_tool_search(self, tools: List[Dict]) -> Optional[Dict]:
+        """Detect tool_search_tool in tools list."""
+        for tool in tools:
+            tool_type = tool.get("type", "")
+            if tool_type == "tool_search_tool_regex_20251119":
+                return {"search_type": "regex", "name": tool.get("name", "tool_search")}
+            elif tool_type == "tool_search_tool_bm25_20251119":
+                return {"search_type": "bm25", "name": tool.get("name", "tool_search")}
+        return None
+
+    def _prepare_tools(
+        self, tools: List[Dict], config: Dict
+    ) -> Tuple[List[Dict], List[Dict]]:
+        """Separate deferred tools and create synthetic tool_search function."""
+        deferred: List[Dict] = []
+        non_deferred: List[Dict] = []
+
+        for tool in tools:
+            tool_type = tool.get("type", "")
+            if tool_type in [
+                "tool_search_tool_regex_20251119",
+                "tool_search_tool_bm25_20251119",
+            ]:
+                continue
+            elif tool.get("defer_loading", False):
+                deferred.append(tool)
+            else:
+                non_deferred.append(tool)
+
+        tool_search_func = self._create_tool_search_function(config)
+        non_deferred.append(tool_search_func)
+
+        return non_deferred, deferred
+
+    def _create_tool_search_function(self, config: Dict) -> Dict:
+        """Create synthetic tool_search function in Anthropic format."""
+        name = config["name"]
+        search_type = config["search_type"]
+
+        if search_type == "regex":
+            description = "Search for available tools using regex patterns. Returns tool references that can be used in subsequent requests."
+        else:
+            description = "Search for available tools using natural language. Returns tool references that can be used in subsequent requests."
+
+        return {
+            "name": name,
+            "description": description,
+            "input_schema": {
+                "type": "object",
+                "properties": {
+                    "query": {
+                        "type": "string",
+                        "description": "Search query to find relevant tools.",
+                    }
+                },
+                "required": ["query"],
+            },
+        }
+
+    def _get_response_content(self, response: LLMResponseTypes) -> Optional[List]:
+        """Extract content from response."""
+        if isinstance(response, dict):
+            return response.get("content")
+        elif hasattr(response, "content"):
+            return getattr(response, "content", None)
+        return None
+
+    def _parse_block(self, block: Any) -> Tuple[Optional[str], Optional[str], Optional[str], Any]:
+        """Parse a content block to extract type, name, id, and input."""
+        if isinstance(block, dict):
+            return (
+                block.get("type"),
+                block.get("name"),
+                block.get("id"),
+                block.get("input", {}),
+            )
+        return (
+            getattr(block, "type", None),
+            getattr(block, "name", None),
+            getattr(block, "id", None),
+            getattr(block, "input", {}),
+        )
+
+    def _build_assistant_content(self, content: List) -> List[Dict]:
+        """Build assistant content for follow-up message."""
+        result = []
+        for block in content:
+            block_type, block_name, block_id, block_input = self._parse_block(block)
+
+            if block_type == "text":
+                text = block.get("text") if isinstance(block, dict) else getattr(block, "text", "")
+                result.append({"type": "text", "text": text})
+            elif block_type == "tool_use":
+                result.append({
+                    "type": "tool_use",
+                    "id": block_id,
+                    "name": block_name,
+                    "input": block_input,
+                })
+
+        return result
+
+
+class ClientSideToolSearch:
+    """
+    BM25 and regex search algorithms for tool discovery.
+
+    Searches tool names, descriptions, and parameter information.
+    """
+
+    def __init__(self, tools: List[Dict]):
+        self.tools = tools
+        self._build_index()
+
+    def search(self, query: str, search_type: str, max_results: int = 5) -> List[Dict]:
+        """Execute search based on type."""
+        if search_type == "regex":
+            return self.search_regex(query, max_results)
+        return self.search_bm25(query, max_results)
+
+    def _build_index(self) -> None:
+        """Build BM25 search index from tools."""
+        self._tool_docs: List[Tuple[Dict, str]] = []
+        self._idf_cache: Dict[str, float] = {}
+
+        for tool in self.tools:
+            doc = self._tool_to_text(tool)
+            self._tool_docs.append((tool, doc.lower()))
+
+        if self._tool_docs:
+            all_terms: set = set()
+            for _, doc in self._tool_docs:
+                all_terms.update(self._tokenize(doc))
+
+            n_docs = len(self._tool_docs)
+            for term in all_terms:
+                doc_freq = sum(
+                    1 for _, doc in self._tool_docs if term in self._tokenize(doc)
+                )
+                self._idf_cache[term] = math.log(
+                    (n_docs - doc_freq + 0.5) / (doc_freq + 0.5) + 1
+                )
+
+    def _tool_to_text(self, tool: Dict) -> str:
+        """Convert tool definition to searchable text."""
+        parts: List[str] = []
+
+        name = tool.get("name") or tool.get("function", {}).get("name", "")
+        if name:
+            parts.extend([name] * 3)
+
+        desc = tool.get("description") or tool.get("function", {}).get("description", "")
+        if desc:
+            parts.append(desc)
+
+        schema = tool.get("input_schema") or tool.get("function", {}).get("parameters", {})
+        if isinstance(schema, dict):
+            for prop_name, prop_info in schema.get("properties", {}).items():
+                parts.append(prop_name)
+                if isinstance(prop_info, dict) and prop_info.get("description"):
+                    parts.append(prop_info["description"])
+
+        return " ".join(parts)
+
+    def _tokenize(self, text: str) -> List[str]:
+        """Simple tokenization."""
+        return re.findall(r"\w+", text.lower())
+
+    def search_bm25(self, query: str, max_results: int = 5) -> List[Dict]:
+        """Search tools using BM25 algorithm."""
+        if not query or not self._tool_docs:
+            return []
+
+        query_terms = self._tokenize(query)
+        if not query_terms:
+            return []
+
+        k1, b = 1.2, 0.75
+        avg_doc_len = sum(len(self._tokenize(doc)) for _, doc in self._tool_docs) / len(self._tool_docs)
+
+        scores: List[Tuple[Dict, float]] = []
+
+        for tool, doc_text in self._tool_docs:
+            doc_terms = self._tokenize(doc_text)
+            doc_len = len(doc_terms)
+            term_freqs = Counter(doc_terms)
+
+            score = 0.0
+            for term in query_terms:
+                if term not in self._idf_cache:
+                    continue
+                tf = term_freqs.get(term, 0)
+                idf = self._idf_cache[term]
+                numerator = tf * (k1 + 1)
+                denominator = tf + k1 * (1 - b + b * (doc_len / avg_doc_len))
+                score += idf * (numerator / denominator) if denominator > 0 else 0
+
+            if score > 0:
+                scores.append((tool, score))
+
+        scores.sort(key=lambda x: x[1], reverse=True)
+
+        return [
+            {"type": "tool_reference", "tool_name": t.get("name") or t.get("function", {}).get("name")}
+            for t, _ in scores[:max_results]
+        ]
+
+    def search_regex(self, pattern: str, max_results: int = 5) -> List[Dict]:
+        """Search tools using regex pattern matching."""
+        if not pattern or len(pattern) > 200:
+            return []
+
+        try:
+            regex = re.compile(pattern, re.IGNORECASE)
+        except re.error:
+            return []
+
+        matches: List[Tuple[Dict, int]] = []
+
+        for tool in self.tools:
+            text = self._tool_to_text(tool)
+            match_count = len(regex.findall(text))
+            if match_count > 0:
+                matches.append((tool, match_count))
+
+        matches.sort(key=lambda x: x[1], reverse=True)
+
+        return [
+            {"type": "tool_reference", "tool_name": t.get("name") or t.get("function", {}).get("name")}
+            for t, _ in matches[:max_results]
+        ]
diff --git a/litellm/llms/anthropic/experimental_pass_through/adapters/handler.py b/litellm/llms/anthropic/experimental_pass_through/adapters/handler.py
index 795f9a4cd09..c8284efb0b6 100644
--- a/litellm/llms/anthropic/experimental_pass_through/adapters/handler.py
+++ b/litellm/llms/anthropic/experimental_pass_through/adapters/handler.py
@@ -92,7 +92,13 @@ class LiteLLMMessagesToCompletionTransformationHandler:
                 "include_usage": True,
             }
 
-        excluded_keys = {"anthropic_messages"}
+        excluded_keys = {
+            "anthropic_messages",
+            "_tool_search_config",  # Internal: tool search hook config
+            "_deferred_tools",  # Internal: deferred tools for tool search
+            "_tool_search_iteration",  # Internal: iteration counter
+            "_original_tools",  # Internal: original tools list
+        }
         extra_kwargs = extra_kwargs or {}
         for key, value in extra_kwargs.items():
             if (
diff --git a/tests/litellm/test_tool_search_pre_call_hook.py b/tests/litellm/test_tool_search_pre_call_hook.py
new file mode 100644
index 00000000000..24b2b78cf15
--- /dev/null
+++ b/tests/litellm/test_tool_search_pre_call_hook.py
@@ -0,0 +1,311 @@
+"""
+Unit tests for ToolSearchPreCallHook
+
+Tests client-side tool search functionality (BM25 + regex) for
+providers that don't support server-side tool search.
+"""
+
+import pytest
+
+from litellm.integrations.tool_search_pre_call_hook import (
+    ClientSideToolSearch,
+    ToolSearchPreCallHook,
+)
+from litellm.types.utils import CallTypes
+
+
+class TestClientSideToolSearch:
+    """Tests for ClientSideToolSearch class."""
+
+    @pytest.fixture
+    def sample_tools(self):
+        """Sample tool definitions for testing."""
+        return [
+            {
+                "name": "get_weather",
+                "description": "Get current weather information for a location",
+                "input_schema": {
+                    "type": "object",
+                    "properties": {
+                        "location": {
+                            "type": "string",
+                            "description": "City name or coordinates",
+                        },
+                        "units": {
+                            "type": "string",
+                            "description": "Temperature units (celsius/fahrenheit)",
+                        },
+                    },
+                },
+            },
+            {
+                "name": "search_flights",
+                "description": "Search for available flights between destinations",
+                "input_schema": {
+                    "type": "object",
+                    "properties": {
+                        "origin": {"type": "string", "description": "Departure airport"},
+                        "destination": {
+                            "type": "string",
+                            "description": "Arrival airport",
+                        },
+                    },
+                },
+            },
+            {
+                "name": "book_hotel",
+                "description": "Book a hotel room for specified dates",
+                "input_schema": {
+                    "type": "object",
+                    "properties": {
+                        "city": {"type": "string", "description": "City name"},
+                        "check_in": {"type": "string", "description": "Check-in date"},
+                    },
+                },
+            },
+            {
+                "type": "function",
+                "function": {
+                    "name": "calculate_distance",
+                    "description": "Calculate distance between two points",
+                    "parameters": {
+                        "type": "object",
+                        "properties": {
+                            "point1": {"type": "string"},
+                            "point2": {"type": "string"},
+                        },
+                    },
+                },
+            },
+        ]
+
+    def test_bm25_search_basic(self, sample_tools):
+        """Test BM25 search returns relevant tools."""
+        search = ClientSideToolSearch(sample_tools)
+        results = search.search_bm25("weather forecast", max_results=5)
+
+        assert len(results) > 0
+        assert results[0]["type"] == "tool_reference"
+        assert results[0]["tool_name"] == "get_weather"
+
+    def test_bm25_search_flight(self, sample_tools):
+        """Test BM25 search for flights."""
+        search = ClientSideToolSearch(sample_tools)
+        results = search.search_bm25("find flights to paris", max_results=5)
+
+        assert len(results) > 0
+        tool_names = [r["tool_name"] for r in results]
+        assert "search_flights" in tool_names
+
+    def test_bm25_search_empty_query(self, sample_tools):
+        """Test BM25 search with empty query returns empty."""
+        search = ClientSideToolSearch(sample_tools)
+        results = search.search_bm25("", max_results=5)
+        assert results == []
+
+    def test_regex_search_basic(self, sample_tools):
+        """Test regex search returns matching tools."""
+        search = ClientSideToolSearch(sample_tools)
+        results = search.search_regex("weather", max_results=5)
+
+        assert len(results) > 0
+        assert results[0]["tool_name"] == "get_weather"
+
+    def test_regex_search_pattern(self, sample_tools):
+        """Test regex search with pattern."""
+        search = ClientSideToolSearch(sample_tools)
+        results = search.search_regex("book.*|search.*", max_results=5)
+
+        assert len(results) >= 2
+        tool_names = [r["tool_name"] for r in results]
+        assert "book_hotel" in tool_names
+        assert "search_flights" in tool_names
+
+    def test_regex_search_invalid_pattern(self, sample_tools):
+        """Test regex search with invalid pattern returns empty."""
+        search = ClientSideToolSearch(sample_tools)
+        results = search.search_regex("[invalid", max_results=5)
+        assert results == []
+
+    def test_regex_search_too_long_pattern(self, sample_tools):
+        """Test regex search with too long pattern returns empty."""
+        search = ClientSideToolSearch(sample_tools)
+        results = search.search_regex("a" * 201, max_results=5)
+        assert results == []
+
+    def test_search_function_format_tools(self, sample_tools):
+        """Test search handles both native and function-wrapped tool formats."""
+        search = ClientSideToolSearch(sample_tools)
+        results = search.search_bm25("calculate distance", max_results=5)
+
+        assert len(results) > 0
+        assert results[0]["tool_name"] == "calculate_distance"
+
+    def test_max_results_limit(self, sample_tools):
+        """Test max_results limits number of results."""
+        search = ClientSideToolSearch(sample_tools)
+        results = search.search_bm25("location city", max_results=2)
+
+        assert len(results) <= 2
+
+
+class TestToolSearchPreCallHook:
+    """Tests for ToolSearchPreCallHook class."""
+
+    @pytest.fixture
+    def hook(self):
+        """Create hook instance."""
+        return ToolSearchPreCallHook()
+
+    def test_detect_tool_search_bm25(self, hook):
+        """Test detection of BM25 tool search."""
+        tools = [
+            {"type": "tool_search_tool_bm25_20251119", "name": "my_search"},
+            {"name": "other_tool"},
+        ]
+        config = hook._detect_tool_search(tools)
+
+        assert config is not None
+        assert config["search_type"] == "bm25"
+        assert config["name"] == "my_search"
+
+    def test_detect_tool_search_regex(self, hook):
+        """Test detection of regex tool search."""
+        tools = [
+            {"type": "tool_search_tool_regex_20251119", "name": "regex_search"},
+            {"name": "other_tool"},
+        ]
+        config = hook._detect_tool_search(tools)
+
+        assert config is not None
+        assert config["search_type"] == "regex"
+        assert config["name"] == "regex_search"
+
+    def test_detect_tool_search_not_present(self, hook):
+        """Test no detection when tool_search not present."""
+        tools = [{"name": "regular_tool"}, {"name": "another_tool"}]
+        config = hook._detect_tool_search(tools)
+        assert config is None
+
+    def test_prepare_tools_separates_deferred(self, hook):
+        """Test _prepare_tools separates deferred tools."""
+        tools = [
+            {"type": "tool_search_tool_bm25_20251119", "name": "tool_search"},
+            {"name": "always_available", "description": "Always available tool"},
+            {
+                "name": "deferred_tool",
+                "defer_loading": True,
+                "description": "Deferred tool",
+            },
+        ]
+        config = {"search_type": "bm25", "name": "tool_search"}
+
+        modified_tools, deferred_tools = hook._prepare_tools(tools, config)
+
+        # Deferred tool should be separated
+        assert len(deferred_tools) == 1
+        assert deferred_tools[0]["name"] == "deferred_tool"
+
+        # Modified tools should have non-deferred + synthetic tool_search
+        # All tools are now in Anthropic format (top-level name)
+        tool_names = [t.get("name") for t in modified_tools]
+        assert "always_available" in tool_names
+        assert "tool_search" in tool_names  # Synthetic function in Anthropic format
+        assert "deferred_tool" not in tool_names
+
+    def test_create_tool_search_function_bm25(self, hook):
+        """Test creation of synthetic BM25 tool_search function in Anthropic format."""
+        config = {"search_type": "bm25", "name": "my_search"}
+        func = hook._create_tool_search_function(config)
+
+        # Should be in Anthropic format (top-level name, description, input_schema)
+        assert func["name"] == "my_search"
+        assert "natural language" in func["description"].lower()
+        assert "query" in func["input_schema"]["properties"]
+
+    def test_create_tool_search_function_regex(self, hook):
+        """Test creation of synthetic regex tool_search function in Anthropic format."""
+        config = {"search_type": "regex", "name": "regex_search"}
+        func = hook._create_tool_search_function(config)
+
+        # Should be in Anthropic format (top-level name, description, input_schema)
+        assert func["name"] == "regex_search"
+        assert "regex" in func["description"].lower()
+
+
+class TestToolSearchPreCallHookAsync:
+    """Async tests for ToolSearchPreCallHook."""
+
+    @pytest.fixture
+    def hook(self):
+        """Create hook instance."""
+        return ToolSearchPreCallHook()
+
+    @pytest.mark.asyncio
+    async def test_pre_call_deployment_hook_non_anthropic(self, hook):
+        """Test hook passes through for non-anthropic call types."""
+        kwargs = {"tools": [{"type": "tool_search_tool_bm25_20251119"}]}
+        result = await hook.async_pre_call_deployment_hook(
+            kwargs, CallTypes.completion
+        )
+        assert result is None
+
+    @pytest.mark.asyncio
+    async def test_pre_call_deployment_hook_no_tools(self, hook):
+        """Test hook passes through when no tools present."""
+        kwargs = {"model": "openai/gpt-4o", "messages": []}
+        result = await hook.async_pre_call_deployment_hook(
+            kwargs, CallTypes.anthropic_messages
+        )
+        assert result is None
+
+    @pytest.mark.asyncio
+    async def test_pre_call_deployment_hook_no_tool_search(self, hook):
+        """Test hook passes through when no tool_search_tool present."""
+        kwargs = {
+            "model": "openai/gpt-4o",
+            "tools": [{"name": "regular_tool"}],
+        }
+        result = await hook.async_pre_call_deployment_hook(
+            kwargs, CallTypes.anthropic_messages
+        )
+        assert result is None
+
+    @pytest.mark.asyncio
+    async def test_pre_call_deployment_hook_anthropic_passthrough(self, hook):
+        """Test hook passes through for anthropic provider (server-side support)."""
+        kwargs = {
+            "model": "claude-3-5-sonnet-20241022",
+            "custom_llm_provider": "anthropic",
+            "tools": [{"type": "tool_search_tool_bm25_20251119", "name": "tool_search"}],
+        }
+        result = await hook.async_pre_call_deployment_hook(
+            kwargs, CallTypes.anthropic_messages
+        )
+        assert result is None  # Pass through to server-side
+
+    @pytest.mark.asyncio
+    async def test_pre_call_deployment_hook_transforms_for_openai(self, hook):
+        """Test hook transforms request for non-anthropic providers."""
+        kwargs = {
+            "model": "openai/gpt-4o",
+            "tools": [
+                {"type": "tool_search_tool_bm25_20251119", "name": "tool_search"},
+                {"name": "deferred_tool", "defer_loading": True, "description": "A tool"},
+                {"name": "always_available", "description": "Always available"},
+            ],
+        }
+        result = await hook.async_pre_call_deployment_hook(
+            kwargs, CallTypes.anthropic_messages
+        )
+
+        assert result is not None
+        assert "_tool_search_config" in result
+        assert "_deferred_tools" in result
+        assert len(result["_deferred_tools"]) == 1
+
+        # Check tools were transformed (all in Anthropic format)
+        tool_names = [t.get("name") for t in result["tools"]]
+        assert "always_available" in tool_names
+        assert "tool_search" in tool_names  # Synthetic function in Anthropic format
+        assert "deferred_tool" not in tool_names  # Removed