diff --git a/concrete/common/bounds_measurement/inputset_eval.py b/concrete/common/bounds_measurement/inputset_eval.py
index 10d8d91e6..b2af728e4 100644
--- a/concrete/common/bounds_measurement/inputset_eval.py
+++ b/concrete/common/bounds_measurement/inputset_eval.py
@@ -1,7 +1,7 @@
 """Code to evaluate the IR graph on inputsets."""
 
 import sys
-from typing import Any, Callable, Dict, Iterable, Tuple
+from typing import Any, Callable, Dict, Iterable, Tuple, Union
 
 from ..compilation import CompilationConfiguration
 from ..data_types.dtypes_helpers import (
@@ -103,7 +103,7 @@ def _print_input_coherency_warnings(
 
 def eval_op_graph_bounds_on_inputset(
     op_graph: OPGraph,
-    inputset: Iterable[Tuple[Any, ...]],
+    inputset: Union[Iterable[Any], Iterable[Tuple[Any, ...]]],
     compilation_configuration: CompilationConfiguration,
     min_func: Callable[[Any, Any], Any] = min,
     max_func: Callable[[Any, Any], Any] = max,
@@ -118,8 +118,9 @@ def eval_op_graph_bounds_on_inputset(
 
     Args:
         op_graph (OPGraph): The graph for which we want to determine the bounds
-        inputset (Iterable[Tuple[Any, ...]]): The inputset over which op_graph is evaluated. It
-            needs to be an iterable on tuples which are of the same length than the number of
+        inputset (Union[Iterable[Any], Iterable[Tuple[Any, ...]]]): The inputset over which op_graph
+            is evaluated. It needs to be an iterable on tuples (can be single values in case the
+            function has only one argument) which are of the same length than the number of
             parameters in the function, and in the same order than these same parameters
         compilation_configuration (CompilationConfiguration): Configuration object to use
             during determining input checking strategy
@@ -138,15 +139,25 @@ def eval_op_graph_bounds_on_inputset(
             as key and a dict with keys "min", "max" and "sample" as value.
     """
 
+    num_input_nodes = len(op_graph.input_nodes)
+
     def check_inputset_input_len_is_valid(data_to_check):
-        assert_true(
-            len(data_to_check) == len(op_graph.input_nodes),
-            (
-                f"Got input data from inputset of len: {len(data_to_check)}, "
-                f"function being evaluated has {len(op_graph.input_nodes)} inputs, please make "
-                f"sure your data generator returns valid tuples of input values"
-            ),
-        )
+        # Only check if there are more than one input node, otherwise accept the value as the sole
+        # argument passed to the OPGraph for evaluation
+        if num_input_nodes > 1:
+            assert_true(
+                len(data_to_check) == num_input_nodes,
+                (
+                    f"Got input data from inputset of len: {len(data_to_check)}, "
+                    f"function being evaluated has {num_input_nodes} inputs, please make "
+                    f"sure your data generator returns valid tuples of input values"
+                ),
+            )
+
+    def generate_input_values_dict(input_data) -> Dict[int, Any]:
+        if num_input_nodes > 1:
+            return dict(enumerate(input_data))
+        return dict(enumerate(input_data)) if isinstance(input_data, tuple) else {0: input_data}
 
     # TODO: do we want to check coherence between the input data type and the corresponding Input ir
     # node expected data type ? Not considering bit_width as they may not make sense at this stage
@@ -161,7 +172,7 @@ def eval_op_graph_bounds_on_inputset(
     inputset_iterator = iter(inputset)
     inputset_size = 0
 
-    current_input_data = dict(enumerate(next(inputset_iterator)))
+    current_input_data = generate_input_values_dict(next(inputset_iterator))
     inputset_size += 1
 
     check_inputset_input_len_is_valid(current_input_data.values())
@@ -189,7 +200,7 @@ def eval_op_graph_bounds_on_inputset(
 
     for input_data in inputset_iterator:
         inputset_size += 1
-        current_input_data = dict(enumerate(input_data))
+        current_input_data = generate_input_values_dict(input_data)
 
         check_inputset_input_len_is_valid(current_input_data.values())
         if compilation_configuration.check_every_input_in_inputset:
diff --git a/tests/common/bounds_measurement/test_inputset_eval.py b/tests/common/bounds_measurement/test_inputset_eval.py
index ddf1fa975..ffef45e41 100644
--- a/tests/common/bounds_measurement/test_inputset_eval.py
+++ b/tests/common/bounds_measurement/test_inputset_eval.py
@@ -473,3 +473,34 @@ def test_eval_op_graph_bounds_on_non_conformant_inputset_treating_warnings_as_er
             max_func=numpy_max_func,
             get_base_value_for_constant_data_func=get_base_value_for_numpy_or_python_constant_data,
         )
+
+
+def test_inpuset_eval_1_input(default_compilation_configuration):
+    """Test case for a function with a single parameter and passing the inputset without tuples."""
+
+    def f(x):
+        return x + 42
+
+    x = EncryptedScalar(UnsignedInteger(4))
+
+    inputset = range(10)
+
+    op_graph = trace_numpy_function(f, {"x": x})
+
+    eval_op_graph_bounds_on_inputset(
+        op_graph,
+        inputset,
+        compilation_configuration=default_compilation_configuration,
+        min_func=numpy_min_func,
+        max_func=numpy_max_func,
+        get_base_value_for_constant_data_func=get_base_value_for_numpy_or_python_constant_data,
+    )
+
+    input_node = op_graph.input_nodes[0]
+
+    assert input_node.inputs[0] == input_node.outputs[0]
+    assert input_node.inputs[0] == EncryptedScalar(UnsignedInteger(4))
+
+    output_node = op_graph.output_nodes[0]
+
+    assert output_node.outputs[0] == EncryptedScalar(UnsignedInteger(6))