diff --git a/vision_agent/agent/vision_agent_v2.py b/vision_agent/agent/vision_agent_v2.py
index 04b90b19..1f93be11 100644
--- a/vision_agent/agent/vision_agent_v2.py
+++ b/vision_agent/agent/vision_agent_v2.py
@@ -235,9 +235,11 @@ def run_plan(
             f"""
 {tabulate(tabular_data=[task], headers="keys", tablefmt="mixed_grid", maxcolwidths=_MAX_TABULATE_COL_WIDTH)}"""
         )
-        tool_info = "\n".join(
-            [e["doc"] for e in tool_recommender.top_k(task["instruction"])]
-        )
+        tools = tool_recommender.top_k(task["instruction"])
+        tool_info = "\n".join([e["doc"] for e in tools])
+
+        if verbosity == 2:
+            _LOGGER.info(f"Tools retrieved: {[e['desc'] for e in tools]}")
 
         if long_term_memory is not None:
             retrieved_ltm = "\n".join(
diff --git a/vision_agent/tools/__init__.py b/vision_agent/tools/__init__.py
index 75b9830e..08a96d81 100644
--- a/vision_agent/tools/__init__.py
+++ b/vision_agent/tools/__init__.py
@@ -3,8 +3,8 @@
     CLIP,
     OCR,
     TOOLS,
-    BboxStats,
     BboxIoU,
+    BboxStats,
     BoxDistance,
     Crop,
     DINOv,
diff --git a/vision_agent/tools/tools_v2.py b/vision_agent/tools/tools_v2.py
index 8d95897c..ab36869f 100644
--- a/vision_agent/tools/tools_v2.py
+++ b/vision_agent/tools/tools_v2.py
@@ -4,12 +4,13 @@
 import tempfile
 from importlib import resources
 from pathlib import Path
-from typing import Any, Callable, Dict, List, Tuple, Union
+from typing import Any, Callable, Dict, List, Tuple, Union, cast
 
 import numpy as np
 import pandas as pd
 import requests
 from PIL import Image, ImageDraw, ImageFont
+from scipy.spatial import distance  # type: ignore
 
 from vision_agent.tools.tool_utils import _send_inference_request
 from vision_agent.utils import extract_frames_from_video
@@ -233,6 +234,54 @@ def ocr(image: np.ndarray) -> List[Dict[str, Any]]:
     return output
 
 
+def closest_mask_distance(mask1: np.ndarray, mask2: np.ndarray) -> float:
+    """'closest_mask_distance' calculates the closest distance between two masks.
+
+    Parameters:
+        mask1 (np.ndarray): The first mask.
+        mask2 (np.ndarray): The second mask.
+
+    Returns:
+        float: The closest distance between the two masks.
+
+    Example
+    -------
+    >>> closest_mask_distance(mask1, mask2)
+    0.5
+    """
+
+    mask1 = np.clip(mask1, 0, 1)
+    mask2 = np.clip(mask2, 0, 1)
+    mask1_points = np.transpose(np.nonzero(mask1))
+    mask2_points = np.transpose(np.nonzero(mask2))
+    dist_matrix = distance.cdist(mask1_points, mask2_points, "euclidean")
+    return cast(float, np.min(dist_matrix))
+
+
+def closest_box_distance(box1: List[float], box2: List[float]) -> float:
+    """'closest_box_distance' calculates the closest distance between two bounding boxes.
+
+    Parameters:
+        box1 (List[float]): The first bounding box.
+        box2 (List[float]): The second bounding box.
+
+    Returns:
+        float: The closest distance between the two bounding boxes.
+
+    Example
+    -------
+    >>> closest_box_distance([100, 100, 200, 200], [300, 300, 400, 400])
+    141.42
+    """
+
+    x11, y11, x12, y12 = box1
+    x21, y21, x22, y22 = box2
+
+    horizontal_distance = np.max([0, x21 - x12, x11 - x22])
+    vertical_distance = np.max([0, y21 - y12, y11 - y22])
+    return cast(float, np.sqrt(horizontal_distance**2 + vertical_distance**2))
+
+
 # Utility and visualization functions
 
 
@@ -429,6 +478,8 @@ def get_tools_df(funcs: List[Callable[..., Any]]) -> pd.DataFrame:
     grounding_sam,
     extract_frames,
     ocr,
+    closest_mask_distance,
+    closest_box_distance,
     load_image,
     save_image,
     overlay_bounding_boxes,