fix tool desc (#102)

vision_agent/tools/tools.py

@@ -58,9 +58,10 @@ def grounding_dino(
  box_threshold: float = 0.20,
  iou_threshold: float = 0.20,
 ) -> List[Dict[str, Any]]:
- """'grounding_dino' is a tool that can detect and count objects given a text prompt
- such as category names or referring expressions. It returns a list and count of
- bounding boxes, label names and associated probability scores.
+ """'grounding_dino' is a tool that can detect and count multiple objects given a text
+ prompt such as category names or referring expressions. The categories in text prompt
+ are separated by commas or periods. It returns a list and count of bounding boxes,
+ label names and associated probability scores.
 
  Parameters:
  prompt (str): The prompt to ground to the image.
@@ -111,9 +112,10 @@ def grounding_sam(
  box_threshold: float = 0.20,
  iou_threshold: float = 0.20,
 ) -> List[Dict[str, Any]]:
- """'grounding_sam' is a tool that can detect and segment objects given a text
- prompt such as category names or referring expressions. It returns a list of
- bounding boxes, label names and masks file names and associated probability scores.
+ """'grounding_sam' is a tool that can detect and segment multiple objects given a
+ text prompt such as category names or referring expressions. The categories in text
+ prompt are separated by commas or periods. It returns a list of bounding boxes,
+ label names, mask file names and associated probability scores.
 
  Parameters:
  prompt (str): The prompt to ground to the image.
@@ -343,9 +345,9 @@ def image_question_answering(image: np.ndarray, prompt: str) -> str:
 
 
 def clip(image: np.ndarray, classes: List[str]) -> Dict[str, Any]:
- """'clip' is a tool that can classify an image given a list of input classes or tags.
- It returns the same list of the input classes along with their probability scores
- based on image content.
+ """'clip' is a tool that can classify an image or a cropped detection given a list
+ of input classes or tags. It returns the same list of the input classes along with
+ their probability scores based on image content.
 
  Parameters:
  image (np.ndarray): The image to classify or tag