diff --git a/integrations/langchain/src/databricks_langchain/vector_search_retriever_tool.py b/integrations/langchain/src/databricks_langchain/vector_search_retriever_tool.py
index ac8b62b8..edd26099 100644
--- a/integrations/langchain/src/databricks_langchain/vector_search_retriever_tool.py
+++ b/integrations/langchain/src/databricks_langchain/vector_search_retriever_tool.py
@@ -1,4 +1,4 @@
-from typing import List, Optional, Type
+from typing import List, Optional, Type, Dict, Any
 
 from databricks_ai_bridge.utils.vector_search import IndexDetails
 from databricks_ai_bridge.vector_search_retriever_tool import (
@@ -21,26 +21,35 @@ class VectorSearchRetrieverTool(BaseTool, VectorSearchRetrieverToolMixin):
     This class integrates with Databricks Vector Search and provides a convenient interface
     for building a retriever tool for agents.
 
-    **Note**: Any additional keyword arguments passed to the constructor will be passed along to
-    `databricks.vector_search.client.VectorSearchIndex.similarity_search` when executing the tool. `See
-    documentation <https://api-docs.databricks.com/python/vector-search/databricks.vector_search.html#databricks.vector_search.index.VectorSearchIndex.similarity_search>`_
-    to see the full set of supported keyword arguments,
-    e.g. `score_threshold`. Also, see documentation for
-    :class:`~databricks_ai_bridge.vector_search_retriever_tool.VectorSearchRetrieverToolMixin` for additional supported constructor
-    arguments not listed below, including `query_type` and `num_results`.
+    **Note**: Any additional keyword arguments passed to the constructor will be forwarded to
+    `databricks.vector_search.index.VectorSearchIndex.similarity_search` when executing the tool.
+    See documentation for the full set of supported keyword arguments (e.g., `score_threshold`).
+    Also see the mixin docs for additional supported constructor arguments (e.g., `query_type`, `num_results`).
+
+    **New**: `client_args` (optional) is forwarded to `VectorSearchClient` via `DatabricksVectorSearch`.
+    Use this to pass service principal credentials (e.g., `service_principal_client_id`,
+    `service_principal_client_secret`) or other client options such as `disable_notice`.
     """
 
     text_column: Optional[str] = Field(
         None,
         description="The name of the text column to use for the embeddings. "
-        "Required for direct-access index or delta-sync index with "
-        "self-managed embeddings.",
+        "Required for direct-access index or delta-sync index with self-managed embeddings.",
     )
     embedding: Optional[Embeddings] = Field(
         None, description="Embedding model for self-managed embeddings."
     )
 
-    # The BaseTool class requires 'name' and 'description' fields which we will populate in validate_tool_inputs()
+    # Optional pass-through for VectorSearchClient (SP/M2M auth, flags like disable_notice, etc.)
+    client_args: Optional[Dict[str, Any]] = Field(
+        default=None,
+        description=(
+            "Additional args forwarded to VectorSearchClient via DatabricksVectorSearch "
+            "(e.g., service_principal_client_id/service_principal_client_secret, disable_notice)."
+        ),
+    )
+
+    # BaseTool requires these; populated in validate_tool_inputs()
     name: str = Field(default="", description="The name of the tool")
     description: str = Field(default="", description="The description of the tool")
     args_schema: Type[BaseModel] = VectorSearchRetrieverToolInput
@@ -49,7 +58,7 @@ class VectorSearchRetrieverTool(BaseTool, VectorSearchRetrieverToolMixin):
 
     @model_validator(mode="after")
     def _validate_tool_inputs(self):
-        kwargs = {
+        kwargs: Dict[str, Any] = {
             "index_name": self.index_name,
             "embedding": self.embedding,
             "text_column": self.text_column,
@@ -59,6 +68,9 @@ def _validate_tool_inputs(self):
             "workspace_client": self.workspace_client,
             "include_score": self.include_score,
         }
+        if self.client_args:
+            kwargs["client_args"] = self.client_args  # <-- pass-through
+
         dbvs = DatabricksVectorSearch(**kwargs)
         self._vector_store = dbvs
 
@@ -71,7 +83,6 @@ def _validate_tool_inputs(self):
             (self.embedding.endpoint if isinstance(self.embedding, DatabricksEmbeddings) else None),
             IndexDetails(dbvs.index),
         )
-
         return self
 
     @vector_search_retriever_tool_trace
diff --git a/integrations/langchain/tests/unit_tests/test_vs_retriever_tool_client_args.py b/integrations/langchain/tests/unit_tests/test_vs_retriever_tool_client_args.py
new file mode 100644
index 00000000..2c8aea78
--- /dev/null
+++ b/integrations/langchain/tests/unit_tests/test_vs_retriever_tool_client_args.py
@@ -0,0 +1,56 @@
+# integrations/langchain/tests/unit_tests/test_vs_retriever_tool_client_args.py
+
+from typing import List
+from langchain_core.embeddings import Embeddings
+
+def test_client_args_pass_through(monkeypatch):
+    captured = {}
+
+    class FakeVSClient:
+        def __init__(self, **client_kwargs):
+            captured["kwargs"] = client_kwargs
+
+        def get_index(self, **_):
+            # Return a fake index object with a minimal describe() the tool expects
+            class _Idx:
+                def describe(self):
+                    return {
+                        "name": "catalog.schema.index",
+                        "endpoint_name": "vs_endpoint",
+                        "index_type": "DELTA_SYNC",  # validator sees delta-sync
+                        "primary_key": "id",
+                        "status": {"status": "ONLINE"},
+                        # (No need to declare managed vs self-managed if we supply embedding+text_column)
+                    }
+            return _Idx()
+
+    # Patch the canonical SDK client path
+    monkeypatch.setattr(
+        "databricks.vector_search.client.VectorSearchClient",
+        FakeVSClient,
+        raising=True,
+    )
+
+    # Minimal embeddings stub to satisfy self-managed requirement
+    class DummyEmbeddings(Embeddings):
+        def embed_documents(self, texts: List[str]) -> List[List[float]]:
+            return [[0.0] * 3 for _ in texts]
+        def embed_query(self, text: str) -> List[float]:
+            return [0.0, 0.0, 0.0]
+
+    from databricks_langchain.vector_search_retriever_tool import VectorSearchRetrieverTool
+
+    tool = VectorSearchRetrieverTool(
+        index_name="catalog.schema.index",
+        text_column="body",               # required with self-managed/delta-sync
+        embedding=DummyEmbeddings(),      # satisfy validator
+        client_args={
+            "service_principal_client_id": "abc",
+            "service_principal_client_secret": "xyz",
+            "disable_notice": True,
+        },
+    )
+
+    assert captured["kwargs"]["service_principal_client_id"] == "abc"
+    assert captured["kwargs"]["service_principal_client_secret"] == "xyz"
+    assert captured["kwargs"]["disable_notice"] is True