diff --git a/vision_agent/tools/meta_tools.py b/vision_agent/tools/meta_tools.py
index c5860a60..290133eb 100644
--- a/vision_agent/tools/meta_tools.py
+++ b/vision_agent/tools/meta_tools.py
@@ -1,5 +1,6 @@
 import os
 import subprocess
+from uuid import UUID
 from pathlib import Path
 from typing import Any, Dict, List, Union
 
@@ -7,6 +8,9 @@
 from vision_agent.lmm.types import Message
 from vision_agent.tools.tool_utils import get_tool_documentation
 from vision_agent.tools.tools import TOOL_DESCRIPTIONS
+from vision_agent.utils.image_utils import convert_to_b64
+from vision_agent.clients.landing_public_api import LandingPublicAPI
+from vision_agent.tools.meta_tools_types import BboxInput, BboxInputBase64, PromptTask
 
 # These tools are adapted from SWE-Agent https://github.com/princeton-nlp/SWE-agent
 
@@ -385,6 +389,46 @@ def get_tool_descriptions() -> str:
     return TOOL_DESCRIPTIONS
 
 
+def florencev2_fine_tuning(bboxes: List[Dict[str, Any]], task: str) -> UUID:
+    """'florencev2_fine_tuning' is a tool that fine-tune florencev2 to be able
+    to detect objects in an image based on a given dataset. It returns the fine
+    tuning job id.
+
+    Parameters:
+        bboxes (List[BboxInput]): A list of BboxInput containing the
+            image object, image filename, labels and bounding boxes.
+        task (PromptTask): The florencev2 fine-tuning task. The options are
+            CAPTION, CAPTION_TO_PHRASE_GROUNDING and OBJECT_DETECTION.
+
+    Returns:
+        UUID: The fine tuning job id, this id will used to retrieve the fine
+            tuned model.
+
+    Example
+    -------
+        >>> fine_tuning_job_id = florencev2_fine_tuning(
+            [{'image': image, 'filename': 'filename.png', 'label': ['screw'], 'bbox': [[370, 30, 560, 290]]},
+             {'image': image, 'filename': 'filename.png', 'label': ['screw'], 'bbox': [[120, 0, 300, 170]]}],
+             "OBJECT_DETECTION"
+        )
+    """
+    bboxes_input = [BboxInput.model_validate(bbox) for bbox in bboxes]
+    task_input = PromptTask[task]
+    fine_tuning_request = [
+        BboxInputBase64(
+            image=convert_to_b64(bbox_input.image),
+            filename=bbox_input.filename,
+            labels=bbox_input.labels,
+            bboxes=bbox_input.bboxes,
+        )
+        for bbox_input in bboxes_input
+    ]
+    landing_api = LandingPublicAPI()
+    return landing_api.launch_fine_tuning_job(
+        "florencev2", task_input, fine_tuning_request
+    )
+
+
 META_TOOL_DOCSTRING = get_tool_documentation(
     [
         get_tool_descriptions,
@@ -398,5 +442,6 @@ def get_tool_descriptions() -> str:
         search_dir,
         search_file,
         find_file,
+        florencev2_fine_tuning,
     ]
 )
diff --git a/vision_agent/tools/tool_types.py b/vision_agent/tools/meta_tools_types.py
similarity index 100%
rename from vision_agent/tools/tool_types.py
rename to vision_agent/tools/meta_tools_types.py
diff --git a/vision_agent/tools/tools.py b/vision_agent/tools/tools.py
index d40b13c1..a78457ed 100644
--- a/vision_agent/tools/tools.py
+++ b/vision_agent/tools/tools.py
@@ -2,7 +2,6 @@
 import json
 import logging
 import tempfile
-from uuid import UUID
 from pathlib import Path
 from importlib import resources
 from typing import Any, Dict, List, Optional, Tuple, Union, cast
@@ -15,8 +14,6 @@
 from PIL import Image, ImageDraw, ImageFont
 from pillow_heif import register_heif_opener  # type: ignore
 
-from vision_agent.clients.landing_public_api import LandingPublicAPI
-from vision_agent.tools.tool_types import BboxInput, BboxInputBase64, PromptTask
 from vision_agent.tools.tool_utils import (
     send_inference_request,
     get_tool_descriptions,
@@ -662,46 +659,6 @@ def florencev2_object_detection(image: np.ndarray) -> List[Dict[str, Any]]:
     return return_data
 
 
-def florencev2_fine_tuning(bboxes: List[Dict[str, Any]], task: str) -> UUID:
-    """'florencev2_fine_tuning' is a tool that fine-tune florencev2 to be able
-    to detect objects in an image based on a given dataset. It returns the fine
-    tuning job id.
-
-    Parameters:
-        bboxes (List[BboxInput]): A list of BboxInput containing the
-            image object, image filename, labels and bounding boxes.
-        task (PromptTask): The florencev2 fine-tuning task. The options are
-            CAPTION, CAPTION_TO_PHRASE_GROUNDING and OBJECT_DETECTION.
-
-    Returns:
-        UUID: The fine tuning job id, this id will used to retrieve the fine
-            tuned model.
-
-    Example
-    -------
-        >>> fine_tuning_job_id = florencev2_fine_tuning(
-            [{'image': image, 'filename': 'filename.png', 'label': ['screw'], 'bbox': [[370, 30, 560, 290]]},
-             {'image': image, 'filename': 'filename.png', 'label': ['screw'], 'bbox': [[120, 0, 300, 170]]}],
-             "OBJECT_DETECTION"
-        )
-    """
-    bboxes_input = [BboxInput.model_validate(bbox) for bbox in bboxes]
-    task_input = PromptTask[task]
-    fine_tuning_request = [
-        BboxInputBase64(
-            image=convert_to_b64(bbox_input.image),
-            filename=bbox_input.filename,
-            labels=bbox_input.labels,
-            bboxes=bbox_input.bboxes,
-        )
-        for bbox_input in bboxes_input
-    ]
-    landing_api = LandingPublicAPI()
-    return landing_api.launch_fine_tuning_job(
-        "florencev2", task_input, fine_tuning_request
-    )
-
-
 def detr_segmentation(image: np.ndarray) -> List[Dict[str, Any]]:
     """'detr_segmentation' is a tool that can segment common objects in an
     image without any text prompt. It returns a list of detected objects
@@ -1297,7 +1254,6 @@ def overlay_heat_map(
     florencev2_roberta_vqa,
     florencev2_image_caption,
     florencev2_object_detection,
-    florencev2_fine_tuning,
     detr_segmentation,
     depth_anything_v2,
     generate_soft_edge_image,