diff --git a/dspy/predict/react.py b/dspy/predict/react.py
index 2690d066af..e116544d68 100644
--- a/dspy/predict/react.py
+++ b/dspy/predict/react.py
@@ -9,9 +9,17 @@
 from dspy.signatures.signature import ensure_signature
 from dspy.utils.callback import with_callbacks
 
-
 class Tool:
-    def __init__(self, func: Callable, name: str = None, desc: str = None, args: dict[str, Any] = None):
+
+    def __init__(
+        self,
+        func: Callable,
+        name: str = None,
+        desc: str = None,
+        args: dict[str, Any] = None,
+        defaults: dict[str, Any] = None,
+        private_defaults: dict[str, Any] = None,
+    ):
         annotations_func = func if inspect.isfunction(func) or inspect.ismethod(func) else func.__call__
         self.func = func
         self.name = name or getattr(func, "__name__", type(func).__name__)
@@ -23,6 +31,8 @@ def __init__(self, func: Callable, name: str = None, desc: str = None, args: dic
             for k, v in (args or get_type_hints(annotations_func)).items()
             if k != "return"
         }
+        self.defaults = defaults
+        self.private_defaults = private_defaults
 
     @with_callbacks
     def __call__(self, *args, **kwargs):
@@ -63,6 +73,10 @@ def __init__(self, signature, tools: list[Callable], max_iters=5):
             args = tool.args if hasattr(tool, "args") else str({tool.input_variable: str})
             desc = (f", whose description is <desc>{tool.desc}</desc>." if tool.desc else ".").replace("\n", "  ")
             desc += f" It takes arguments {args} in JSON format."
+            if tool.defaults:
+                desc += f" Default arguments are {tool.defaults}."
+            if tool.private_defaults:
+                desc += f" Assume the following function arguments will be provided at function execution time: {tool.private_defaults.keys()}. Therefore do not propose these arguments in the `next_tool_args`."
             instr.append(f"({idx+1}) {tool.name}{desc}")
 
         react_signature = (
@@ -91,13 +105,25 @@ def format(trajectory: dict[str, Any], last_iteration: bool):
         for idx in range(self.max_iters):
             pred = self.react(**input_args, trajectory=format(trajectory, last_iteration=(idx == self.max_iters - 1)))
 
+            # extract private defaults from the tool and supply them to the next tool call
+            # do not assign the private defaults to the next_tool_args as this will be captured in the trajectory logs, which is not what we want
+            private_defaults = (
+                self.tools[pred.next_tool_name].private_defaults
+                if pred.next_tool_name in self.tools
+                and self.tools[pred.next_tool_name].private_defaults
+                else {}
+            )
+
             trajectory[f"thought_{idx}"] = pred.next_thought
             trajectory[f"tool_name_{idx}"] = pred.next_tool_name
             trajectory[f"tool_args_{idx}"] = pred.next_tool_args
 
             try:
-                trajectory[f"observation_{idx}"] = self.tools[pred.next_tool_name](**pred.next_tool_args)
+                trajectory[f"observation_{idx}"] = self.tools[pred.next_tool_name](
+                    **pred.next_tool_args, **private_defaults
+                )
             except Exception as e:
+                # risk that the error log will capture the private defaults?
                 trajectory[f"observation_{idx}"] = f"Failed to execute: {e}"
 
             if pred.next_tool_name == "finish":