diff --git a/libs/core/langchain_core/tools/__init__.py b/libs/core/langchain_core/tools/__init__.py
index 3553ff10ec79f..d55afb1255cdf 100644
--- a/libs/core/langchain_core/tools/__init__.py
+++ b/libs/core/langchain_core/tools/__init__.py
@@ -31,6 +31,9 @@
 from langchain_core.tools.base import (
     InjectedToolArg as InjectedToolArg,
 )
+from langchain_core.tools.base import (
+    InjectedToolArgSchema as InjectedToolArgSchema,
+)
 from langchain_core.tools.base import SchemaAnnotationError as SchemaAnnotationError
 from langchain_core.tools.base import (
     ToolException as ToolException,
diff --git a/libs/core/langchain_core/tools/base.py b/libs/core/langchain_core/tools/base.py
index 9782234dfb1a0..0a5422c8b07e7 100644
--- a/libs/core/langchain_core/tools/base.py
+++ b/libs/core/langchain_core/tools/base.py
@@ -31,6 +31,7 @@
     PydanticDeprecationWarning,
     SkipValidation,
     ValidationError,
+    WithJsonSchema,
     model_validator,
     validate_arguments,
 )
@@ -103,7 +104,7 @@ def _get_filtered_args(
         for i, (k, param) in enumerate(valid_keys.items())
         if k not in filter_args
         and (i > 0 or param.name not in ("self", "cls"))
-        and (include_injected or not _is_injected_arg_type(param.annotation))
+        and (include_injected or not _check_injected_arg_type(param.annotation))
     }
 
 
@@ -263,21 +264,26 @@ def create_schema_from_function(
 
     inferred_model = validated.model  # type: ignore
 
+    # extract the function parameters
+    existing_params: list[str] = list(sig.parameters.keys())
+
+    # Create filtered arguments list
     if filter_args:
-        filter_args_ = filter_args
+        filter_args_ = list(filter_args)
+
     else:
         # Handle classmethods and instance methods
-        existing_params: list[str] = list(sig.parameters.keys())
         if existing_params and existing_params[0] in ("self", "cls") and in_class:
             filter_args_ = [existing_params[0]] + list(FILTERED_ARGS)
         else:
             filter_args_ = list(FILTERED_ARGS)
 
-        for existing_param in existing_params:
-            if not include_injected and _is_injected_arg_type(
-                sig.parameters[existing_param].annotation
-            ):
-                filter_args_.append(existing_param)
+    # add arguments with InjectedToolArg annotation to filter_args_
+    for existing_param in existing_params:
+        if not include_injected and _check_injected_arg_type(
+            sig.parameters[existing_param].annotation
+        ):
+            filter_args_.append(existing_param)
 
     description, arg_descriptions = _infer_arg_descriptions(
         func,
@@ -954,6 +960,17 @@ class InjectedToolArg:
     """Annotation for a Tool arg that is **not** meant to be generated by a model."""
 
 
+# Add Custom json schema for injected tool arguments
+InjectedToolArgSchema = Annotated[
+    InjectedToolArg,
+    WithJsonSchema(
+        {
+            "type": "Injected-Tool-Argument",
+        }
+    ),
+]
+
+
 def _is_injected_arg_type(type_: type) -> bool:
     return any(
         isinstance(arg, InjectedToolArg)
@@ -962,6 +979,19 @@ def _is_injected_arg_type(type_: type) -> bool:
     )
 
 
+# Identify if a type contains an InjectedToolArg annotation
+# Used to filter out injected arguments from the schema
+def _check_injected_arg_type(type_: type) -> bool:
+    if type_ is InjectedToolArg:
+        return True
+    for arg in get_args(type_):
+        if arg is InjectedToolArg or (
+            isinstance(arg, type) and issubclass(arg, InjectedToolArg)
+        ):
+            return True
+    return False
+
+
 def get_all_basemodel_annotations(
     cls: Union[TypeBaseModel, Any], *, default_to_bound: bool = True
 ) -> dict[str, type]:
diff --git a/libs/core/langchain_core/tools/structured.py b/libs/core/langchain_core/tools/structured.py
index 174e7b2f53704..fd6ee9c416949 100644
--- a/libs/core/langchain_core/tools/structured.py
+++ b/libs/core/langchain_core/tools/structured.py
@@ -173,12 +173,14 @@ def add(a: int, b: int) -> int:
         name = name or source_function.__name__
         if args_schema is None and infer_schema:
             # schema name is appended within function
+            # Use include_injected to exclude injected args in the schema
             args_schema = create_schema_from_function(
                 name,
                 source_function,
                 parse_docstring=parse_docstring,
                 error_on_invalid_docstring=error_on_invalid_docstring,
                 filter_args=_filter_schema_args(source_function),
+                # include_injected=False,
             )
         description_ = description
         if description is None and not parse_docstring:
diff --git a/libs/core/tests/unit_tests/test_tools.py b/libs/core/tests/unit_tests/test_tools.py
index ce7ea4894bb5a..7d2185e93a42e 100644
--- a/libs/core/tests/unit_tests/test_tools.py
+++ b/libs/core/tests/unit_tests/test_tools.py
@@ -46,6 +46,7 @@
 )
 from langchain_core.tools.base import (
     InjectedToolArg,
+    InjectedToolArgSchema,
     SchemaAnnotationError,
     _is_message_content_block,
     _is_message_content_type,
@@ -2110,3 +2111,18 @@ def injected_tool(x: int, foo: Annotated[Foo, InjectedToolArg]) -> str:
         return foo.value
 
     assert injected_tool.invoke({"x": 5, "foo": Foo()}) == "bar"  # type: ignore
+
+
+# Test user specific tools produce correct the correct json schema
+# for it's arguments using .args
+def test_user_specific_tool_without_schema() -> None:
+    @tool
+    def user_specific_tool(x: str, y: InjectedToolArgSchema) -> str:
+        """Tool that has an injected tool arg."""
+        return "User {x} processed {y}"
+
+    # Verify the tool's args schema
+    assert user_specific_tool.args == {
+        "x": {"title": "X", "type": "string"},
+        "y": {"title": "Y", "type": "Injected-Tool-Argument"},
+    }