diff --git a/.gitignore b/.gitignore
index ae0a17b45..6bc67fa61 100644
--- a/.gitignore
+++ b/.gitignore
@@ -178,3 +178,4 @@ cov.xml
 *.faiss
 *-structure.csv
 *-structure.json
+metagpt/tools/schemas
\ No newline at end of file
diff --git a/metagpt/tools/libs/gpt_v_generator.py b/metagpt/tools/libs/gpt_v_generator.py
index 6a620f7e8..63fda3e81 100644
--- a/metagpt/tools/libs/gpt_v_generator.py
+++ b/metagpt/tools/libs/gpt_v_generator.py
@@ -30,9 +30,18 @@ As the design pays tribute to large companies, sometimes it is normal for some c
 Now, please generate the corresponding webpage code including HTML, CSS and JavaScript:"""
 
 
-@register_tool(tool_type=ToolTypes.IMAGE2WEBPAGE.type_name)
+@register_tool(
+    tool_type=ToolTypes.IMAGE2WEBPAGE.type_name, include_functions=["__init__", "generate_webpages", "save_webpages"]
+)
 class GPTvGenerator:
+    """Class for generating webpages at once.
+
+    This class provides methods to generate webpages including all code (HTML, CSS, and JavaScript) based on an image.
+    It utilizes a vision model to analyze the layout from an image and generate webpage codes accordingly.
+    """
+
     def __init__(self):
+        """Initialize GPTvGenerator class with default values from the configuration."""
         from metagpt.config2 import config
 
         self.api_key = config.llm.api_key
@@ -41,15 +50,42 @@ class GPTvGenerator:
         self.max_tokens = config.vision_max_tokens
 
     def analyze_layout(self, image_path):
+        """Analyze the layout of the given image and return the result.
+
+        This is a helper method to generate a layout description based on the image.
+
+        Args:
+            image_path (str): Path of the image to analyze.
+
+        Returns:
+            str: The layout analysis result.
+        """
         return self.get_result(image_path, ANALYZE_LAYOUT_PROMPT)
 
     def generate_webpages(self, image_path):
+        """Generate webpages including all code (HTML, CSS, and JavaScript) in one go based on the image.
+
+        Args:
+            image_path (str): The path of the image file.
+
+        Returns:
+            str: Generated webpages content.
+        """
         layout = self.analyze_layout(image_path)
         prompt = GENERATE_PROMPT + "\n\n # Context\n The layout information of the sketch image is: \n" + layout
         result = self.get_result(image_path, prompt)
         return result
 
     def get_result(self, image_path, prompt):
+        """Get the result from the vision model based on the given image path and prompt.
+
+        Args:
+            image_path (str): Path of the image to analyze.
+            prompt (str): Prompt to use for the analysis.
+
+        Returns:
+            str: The model's response as a string.
+        """
         base64_image = self.encode_image(image_path)
         headers = {"Content-Type": "application/json", "Authorization": f"Bearer {self.api_key}"}
         payload = {
@@ -74,11 +110,28 @@ class GPTvGenerator:
 
     @staticmethod
     def encode_image(image_path):
+        """Encode the image at the given path to a base64 string.
+
+        Args:
+            image_path (str): Path of the image to encode.
+
+        Returns:
+            str: The base64 encoded string of the image.
+        """
         with open(image_path, "rb") as image_file:
             return base64.b64encode(image_file.read()).decode("utf-8")
 
     @staticmethod
     def save_webpages(image_path, webpages) -> Path:
+        """Save webpages including all code (HTML, CSS, and JavaScript) at once.
+
+        Args:
+            image_path (str): The path of the image file.
+            webpages (str): The generated webpages content.
+
+        Returns:
+            Path: The path of the saved webpages.
+        """
         # 在workspace目录下，创建一个名为下webpages的文件夹，用于存储html、css和js文件
         webpages_path = DEFAULT_WORKSPACE_ROOT / "webpages" / Path(image_path).stem
         os.makedirs(webpages_path, exist_ok=True)
diff --git a/metagpt/tools/libs/sd_engine.py b/metagpt/tools/libs/sd_engine.py
index 6fb16993e..6229a60e3 100644
--- a/metagpt/tools/libs/sd_engine.py
+++ b/metagpt/tools/libs/sd_engine.py
@@ -53,10 +53,22 @@ payload = {
 default_negative_prompt = "(easynegative:0.8),black, dark,Low resolution"
 
 
-@register_tool(tool_type=ToolTypes.STABLE_DIFFUSION.type_name)
+@register_tool(
+    tool_type=ToolTypes.STABLE_DIFFUSION.type_name,
+    include_functions=["__init__", "simple_run_t2i", "run_t2i", "construct_payload", "save"],
+)
 class SDEngine:
+    """Generate image using stable diffusion model.
+
+    This class provides methods to interact with a stable diffusion service to generate images based on text inputs.
+    """
+
     def __init__(self, sd_url=""):
-        # Initialize the SDEngine with configuration
+        """Initialize the SDEngine instance with configuration.
+
+        Args:
+            sd_url (str, optional): URL of the stable diffusion service. Defaults to "".
+        """
         self.sd_url = sd_url
         self.sd_t2i_url = f"{self.sd_url}/sdapi/v1/txt2img"
         # Define default payload settings for SD API
@@ -71,7 +83,18 @@ class SDEngine:
         height=512,
         sd_model="galaxytimemachinesGTM_photoV20",
     ):
-        # Configure the payload with provided inputs
+        """Modify and set the API parameters for image generation.
+
+        Args:
+            prompt (str): Text input for image generation.
+            negtive_prompt (str, optional): Text input for negative prompts. Defaults to None.
+            width (int, optional): Width of the generated image in pixels. Defaults to 512.
+            height (int, optional): Height of the generated image in pixels. Defaults to 512.
+            sd_model (str, optional): The model to use for image generation. Defaults to "galaxytimemachinesGTM_photoV20".
+
+        Returns:
+            dict: Updated parameters for the stable diffusion API.
+        """
         self.payload["prompt"] = prompt
         self.payload["negative_prompt"] = negtive_prompt
         self.payload["width"] = width
@@ -81,12 +104,27 @@ class SDEngine:
         return self.payload
 
     def save(self, imgs, save_name=""):
+        """Save generated images to the output directory.
+
+        Args:
+            imgs (str): Generated images.
+            save_name (str, optional): Output image name. Default is empty.
+        """
         save_dir = SOURCE_ROOT / SD_OUTPUT_FILE_REPO
         if not save_dir.exists():
             save_dir.mkdir(parents=True, exist_ok=True)
         batch_decode_base64_to_image(imgs, str(save_dir), save_name=save_name)
 
     def simple_run_t2i(self, payload: dict, auto_save: bool = True):
+        """Run the stable diffusion API for multiple prompts, calling the stable diffusion API to generate images.
+
+        Args:
+            payload (dict): Dictionary of input parameters for the stable diffusion API.
+            auto_save (bool, optional): Save generated images automatically. Defaults to True.
+
+        Returns:
+            list: The generated images as a result of the API call.
+        """
         with requests.Session() as session:
             logger.debug(self.sd_t2i_url)
             rsp = session.post(self.sd_t2i_url, json=payload, timeout=600)
@@ -98,7 +136,11 @@ class SDEngine:
         return results
 
     async def run_t2i(self, payloads: List):
-        # Asynchronously run the SD API for multiple prompts
+        """Run the stable diffusion API for multiple prompts asynchronously.
+
+        Args:
+            payloads (list): List of payload, each payload is a dictionary of input parameters for the stable diffusion API.
+        """
         session = ClientSession()
         for payload_idx, payload in enumerate(payloads):
             results = await self.run(url=self.sd_t2i_url, payload=payload, session=session)
@@ -106,7 +148,16 @@ class SDEngine:
         await session.close()
 
     async def run(self, url, payload, session):
-        # Perform the HTTP POST request to the SD API
+        """Perform the HTTP POST request to the SD API.
+
+        Args:
+            url (str): The API URL.
+            payload (dict): The payload for the request.
+            session (ClientSession): The session for making HTTP requests.
+
+        Returns:
+            list: Images generated by the stable diffusion API.
+        """
         async with session.post(url, json=payload, timeout=600) as rsp:
             data = await rsp.read()
 
diff --git a/metagpt/tools/schemas/__init__.py b/metagpt/tools/schemas/__init__.py
deleted file mode 100644
index e50f67d6f..000000000
--- a/metagpt/tools/schemas/__init__.py
+++ /dev/null
@@ -1,6 +0,0 @@
-#!/usr/bin/env python
-# -*- coding: utf-8 -*-
-# @Time    : 2023/11/16 16:33
-# @Author  : lidanyang
-# @File    : __init__.py
-# @Desc    :
diff --git a/metagpt/tools/schemas/data_preprocess/FillMissingValue.yml b/metagpt/tools/schemas/data_preprocess/FillMissingValue.yml
deleted file mode 100644
index 44c830a1e..000000000
--- a/metagpt/tools/schemas/data_preprocess/FillMissingValue.yml
+++ /dev/null
@@ -1,61 +0,0 @@
-FillMissingValue:
-  type: class
-  description: "Completing missing values with simple strategies"
-  methods:
-    __init__:
-      description: "Initialize self."
-      parameters:
-        properties:
-          features:
-            type: list
-            description: "columns to be processed"
-          strategy:
-            type: str
-            description: "the imputation strategy, notice mean/median can only be used for numeric features"
-            default: mean
-            enum:
-              - mean
-              - median
-              - most_frequent
-              - constant
-          fill_value:
-            type: int
-            description: "fill_value is used to replace all occurrences of missing_values"
-            default: null
-        required:
-          - features
-    fit:
-      description: "Fit the FillMissingValue model."
-      parameters:
-        properties:
-          df:
-            type: DataFrame
-            description: "The input DataFrame."
-        required:
-          - df
-    transform:
-      description: "Transform the input DataFrame with the fitted model."
-      parameters:
-        properties:
-          df:
-            type: DataFrame
-            description: "The input DataFrame."
-        required:
-          - df
-      returns:
-        df:
-          type: DataFrame
-          description: "The transformed DataFrame."
-    fit_transform:
-      description: "Fit and transform the input DataFrame."
-      parameters:
-        properties:
-          df:
-            type: DataFrame
-            description: "The input DataFrame."
-        required:
-          - df
-      returns:
-        df:
-          type: DataFrame
-          description: "The transformed DataFrame."
diff --git a/metagpt/tools/schemas/data_preprocess/LabelEncode.yml b/metagpt/tools/schemas/data_preprocess/LabelEncode.yml
deleted file mode 100644
index 419ef60a8..000000000
--- a/metagpt/tools/schemas/data_preprocess/LabelEncode.yml
+++ /dev/null
@@ -1,48 +0,0 @@
-LabelEncode:
-  type: class
-  description: "Apply label encoding to specified categorical columns in-place."
-  methods:
-    __init__:
-      description: "Initialize self."
-      parameters:
-        properties:
-          features:
-            type: list
-            description: "Categorical columns to be label encoded"
-        required:
-          - features
-    fit:
-      description: "Fit the LabelEncode model."
-      parameters:
-        properties:
-          df:
-            type: DataFrame
-            description: "The input DataFrame."
-        required:
-          - df
-    transform:
-      description: "Transform the input DataFrame with the fitted model."
-      parameters:
-        properties:
-          df:
-            type: DataFrame
-            description: "The input DataFrame."
-        required:
-          - df
-      returns:
-        df:
-          type: DataFrame
-          description: "The transformed DataFrame."
-    fit_transform:
-      description: "Fit and transform the input DataFrame."
-      parameters:
-        properties:
-          df:
-            type: DataFrame
-            description: "The input DataFrame."
-        required:
-          - df
-      returns:
-        df:
-          type: DataFrame
-          description: "The transformed DataFrame."
diff --git a/metagpt/tools/schemas/data_preprocess/MaxAbsScale.yml b/metagpt/tools/schemas/data_preprocess/MaxAbsScale.yml
deleted file mode 100644
index 3e17cfdd0..000000000
--- a/metagpt/tools/schemas/data_preprocess/MaxAbsScale.yml
+++ /dev/null
@@ -1,48 +0,0 @@
-MaxAbsScale:
-  type: class
-  description: "cale each feature by its maximum absolute value"
-  methods:
-    __init__:
-      description: "Initialize self."
-      parameters:
-        properties:
-          features:
-            type: list
-            description: "columns to be processed"
-        required:
-          - features
-    fit:
-      description: "Fit the MaxAbsScale model."
-      parameters:
-        properties:
-          df:
-            type: DataFrame
-            description: "The input DataFrame."
-        required:
-          - df
-    transform:
-      description: "Transform the input DataFrame with the fitted model."
-      parameters:
-        properties:
-          df:
-            type: DataFrame
-            description: "The input DataFrame."
-        required:
-          - df
-      returns:
-        df:
-          type: DataFrame
-          description: "The transformed DataFrame."
-    fit_transform:
-      description: "Fit and transform the input DataFrame."
-      parameters:
-        properties:
-          df:
-            type: DataFrame
-            description: "The input DataFrame."
-        required:
-          - df
-      returns:
-        df:
-          type: DataFrame
-          description: "The transformed DataFrame."
\ No newline at end of file
diff --git a/metagpt/tools/schemas/data_preprocess/MinMaxScale.yml b/metagpt/tools/schemas/data_preprocess/MinMaxScale.yml
deleted file mode 100644
index 8f050d942..000000000
--- a/metagpt/tools/schemas/data_preprocess/MinMaxScale.yml
+++ /dev/null
@@ -1,48 +0,0 @@
-MinMaxScale:
-  type: class
-  description: "Transform features by scaling each feature to a range, witch is (0, 1)"
-  methods:
-    __init__:
-      description: "Initialize self."
-      parameters:
-        properties:
-          features:
-            type: list
-            description: "columns to be processed"
-        required:
-          - features
-    fit:
-      description: "Fit the MinMaxScale model."
-      parameters:
-        properties:
-          df:
-            type: DataFrame
-            description: "The input DataFrame."
-        required:
-          - df
-    transform:
-      description: "Transform the input DataFrame with the fitted model."
-      parameters:
-        properties:
-          df:
-            type: DataFrame
-            description: "The input DataFrame."
-        required:
-          - df
-      returns:
-        df:
-          type: DataFrame
-          description: "The transformed DataFrame."
-    fit_transform:
-      description: "Fit and transform the input DataFrame."
-      parameters:
-        properties:
-          df:
-            type: DataFrame
-            description: "The input DataFrame."
-        required:
-          - df
-      returns:
-        df:
-          type: DataFrame
-          description: "The transformed DataFrame."
diff --git a/metagpt/tools/schemas/data_preprocess/OneHotEncode.yml b/metagpt/tools/schemas/data_preprocess/OneHotEncode.yml
deleted file mode 100644
index f499b2cb8..000000000
--- a/metagpt/tools/schemas/data_preprocess/OneHotEncode.yml
+++ /dev/null
@@ -1,48 +0,0 @@
-OneHotEncode:
-  type: class
-  description: "Apply one-hot encoding to specified categorical columns, the original columns will be dropped."
-  methods:
-    __init__:
-      description: "Initialize self."
-      parameters:
-        properties:
-          features:
-            type: list
-            description: "Categorical columns to be one-hot encoded and dropped"
-        required:
-          - features
-    fit:
-      description: "Fit the OneHotEncoding model."
-      parameters:
-        properties:
-          df:
-            type: DataFrame
-            description: "The input DataFrame."
-        required:
-          - df
-    transform:
-      description: "Transform the input DataFrame with the fitted model."
-      parameters:
-        properties:
-          df:
-            type: DataFrame
-            description: "The input DataFrame."
-        required:
-          - df
-      returns:
-        df:
-          type: DataFrame
-          description: "The transformed DataFrame."
-    fit_transform:
-      description: "Fit and transform the input DataFrame."
-      parameters:
-        properties:
-          df:
-            type: DataFrame
-            description: "The input DataFrame."
-        required:
-          - df
-      returns:
-        df:
-          type: DataFrame
-          description: "The transformed DataFrame."
diff --git a/metagpt/tools/schemas/data_preprocess/OrdinalEncode.yml b/metagpt/tools/schemas/data_preprocess/OrdinalEncode.yml
deleted file mode 100644
index 79ebaf37c..000000000
--- a/metagpt/tools/schemas/data_preprocess/OrdinalEncode.yml
+++ /dev/null
@@ -1,46 +0,0 @@
-OrdinalEncode:
-  type: class
-  description: Encode categorical features as ordinal integers.
-  methods:
-    __init__:
-      description: 'Initialize the OrdinalEncode instance with feature names. '
-      parameters:
-        properties:
-          features:
-            type: list
-            description: List of categorical feature names to be encoded.
-        required:
-        - features
-    fit:
-      description: 'Learn the ordinal encodings for the features. '
-      parameters:
-        properties:
-          df:
-            type: pd.DataFrame
-            description: Dataframe containing the categorical features.
-        required:
-        - df
-    fit_transform:
-      description: 'Fit and transform the input DataFrame. '
-      parameters:
-        properties:
-          df:
-            type: pd.DataFrame
-            description: The input DataFrame.
-        required:
-        - df
-      returns:
-      - type: pd.DataFrame
-        description: The transformed DataFrame.
-    transform:
-      description: 'Convert the categorical features to ordinal integers. '
-      parameters:
-        properties:
-          df:
-            type: pd.DataFrame
-            description: Dataframe containing the categorical features to be encoded.
-        required:
-        - df
-      returns:
-      - type: pd.DataFrame
-        description: A new dataframe with the encoded features.
diff --git a/metagpt/tools/schemas/data_preprocess/RobustScale.yml b/metagpt/tools/schemas/data_preprocess/RobustScale.yml
deleted file mode 100644
index 6d5dfaf3a..000000000
--- a/metagpt/tools/schemas/data_preprocess/RobustScale.yml
+++ /dev/null
@@ -1,47 +0,0 @@
-RobustScale:
-  type: class
-  description: Apply the RobustScaler to scale features using statistics that are
-    robust to outliers.
-  methods:
-    __init__:
-      description: 'Initialize the RobustScale instance with feature names. '
-      parameters:
-        properties:
-          features:
-            type: list
-            description: List of feature names to be scaled.
-        required:
-        - features
-    fit:
-      description: 'Compute the median and IQR for scaling. '
-      parameters:
-        properties:
-          df:
-            type: pd.DataFrame
-            description: Dataframe containing the features.
-        required:
-        - df
-    fit_transform:
-      description: 'Fit and transform the input DataFrame. '
-      parameters:
-        properties:
-          df:
-            type: pd.DataFrame
-            description: The input DataFrame.
-        required:
-        - df
-      returns:
-      - type: pd.DataFrame
-        description: The transformed DataFrame.
-    transform:
-      description: 'Scale features using the previously computed median and IQR. '
-      parameters:
-        properties:
-          df:
-            type: pd.DataFrame
-            description: Dataframe containing the features to be scaled.
-        required:
-        - df
-      returns:
-      - type: pd.DataFrame
-        description: A new dataframe with scaled features.
diff --git a/metagpt/tools/schemas/data_preprocess/StandardScale.yml b/metagpt/tools/schemas/data_preprocess/StandardScale.yml
deleted file mode 100644
index cf6e7d57b..000000000
--- a/metagpt/tools/schemas/data_preprocess/StandardScale.yml
+++ /dev/null
@@ -1,48 +0,0 @@
-StandardScale:
-  type: class
-  description: "Standardize features by removing the mean and scaling to unit variance"
-  methods:
-    __init__:
-      description: "Initialize self."
-      parameters:
-        properties:
-          features:
-            type: list
-            description: "columns to be processed"
-        required:
-          - features
-    fit:
-      description: "Fit the StandardScale model."
-      parameters:
-        properties:
-          df:
-            type: DataFrame
-            description: "The input DataFrame."
-        required:
-          - df
-    transform:
-      description: "Transform the input DataFrame with the fitted model."
-      parameters:
-        properties:
-          df:
-            type: DataFrame
-            description: "The input DataFrame."
-        required:
-          - df
-      returns:
-        df:
-          type: DataFrame
-          description: "The transformed DataFrame."
-    fit_transform:
-      description: "Fit and transform the input DataFrame."
-      parameters:
-        properties:
-          df:
-            type: DataFrame
-            description: "The input DataFrame."
-        required:
-          - df
-      returns:
-        df:
-          type: DataFrame
-          description: "The transformed DataFrame."
diff --git a/metagpt/tools/schemas/feature_engineering/CatCount.yml b/metagpt/tools/schemas/feature_engineering/CatCount.yml
deleted file mode 100644
index 049fc7879..000000000
--- a/metagpt/tools/schemas/feature_engineering/CatCount.yml
+++ /dev/null
@@ -1,48 +0,0 @@
-CatCount:
-  type: class
-  description: "Add value counts of a categorical column as new feature."
-  methods:
-    __init__:
-      description: "Initialize self."
-      parameters:
-        properties:
-          col:
-            type: str
-            description: "Column for value counts."
-        required:
-          - col
-    fit:
-      description: "Fit the CatCount model."
-      parameters:
-        properties:
-          df:
-            type: DataFrame
-            description: "The input DataFrame."
-        required:
-          - df
-    transform:
-      description: "Transform the input DataFrame with the fitted model."
-      parameters:
-        properties:
-          df:
-            type: DataFrame
-            description: "The input DataFrame."
-        required:
-          - df
-      returns:
-        df:
-          type: DataFrame
-          description: "The transformed DataFrame."
-    fit_transform:
-      description: "Fit and transform the input DataFrame."
-      parameters:
-        properties:
-          df:
-            type: DataFrame
-            description: "The input DataFrame."
-        required:
-          - df
-      returns:
-        df:
-          type: DataFrame
-          description: "The transformed DataFrame."
\ No newline at end of file
diff --git a/metagpt/tools/schemas/feature_engineering/CatCross.yml b/metagpt/tools/schemas/feature_engineering/CatCross.yml
deleted file mode 100644
index 5d6303439..000000000
--- a/metagpt/tools/schemas/feature_engineering/CatCross.yml
+++ /dev/null
@@ -1,52 +0,0 @@
-CatCross:
-  type: class
-  description: "Add pairwise crossed features and convert them to numerical features."
-  methods:
-    __init__:
-      description: "Initialize self."
-      parameters:
-        properties:
-          cols:
-            type: list
-            description: "Columns to be pairwise crossed, at least 2 columns."
-          max_cat_num:
-            type: int
-            description: "Maximum unique categories per crossed feature."
-            default: 100
-      required:
-        - cols
-    fit:
-      description: "Fit the CatCross model."
-      parameters:
-        properties:
-          df:
-            type: DataFrame
-            description: "The input DataFrame."
-        required:
-          - df
-    transform:
-      description: "Transform the input DataFrame with the fitted model."
-      parameters:
-        properties:
-          df:
-            type: DataFrame
-            description: "The input DataFrame."
-        required:
-          - df
-      returns:
-        df:
-          type: DataFrame
-          description: "The transformed DataFrame."
-    fit_transform:
-      description: "Fit and transform the input DataFrame."
-      parameters:
-        properties:
-          df:
-            type: DataFrame
-            description: "The input DataFrame."
-        required:
-          - df
-      returns:
-        df:
-          type: DataFrame
-          description: "The transformed DataFrame."
\ No newline at end of file
diff --git a/metagpt/tools/schemas/feature_engineering/GeneralSelection.yml b/metagpt/tools/schemas/feature_engineering/GeneralSelection.yml
deleted file mode 100644
index 2ebf5b397..000000000
--- a/metagpt/tools/schemas/feature_engineering/GeneralSelection.yml
+++ /dev/null
@@ -1,48 +0,0 @@
-GeneralSelection:
-  type: class
-  description: "Drop all nan feats and feats with only one unique value."
-  methods:
-    __init__:
-      description: "Initialize self."
-      parameters:
-        properties:
-          label_col:
-            type: str
-            description: "Label column name."
-        required:
-          - label_col
-    fit:
-      description: "Fit the GeneralSelection model."
-      parameters:
-        properties:
-          df:
-            type: DataFrame
-            description: "The input DataFrame."
-        required:
-          - df
-    transform:
-      description: "Transform the input DataFrame with the fitted model."
-      parameters:
-        properties:
-          df:
-            type: DataFrame
-            description: "The input DataFrame."
-        required:
-          - df
-      returns:
-        df:
-          type: DataFrame
-          description: "The transformed DataFrame."
-    fit_transform:
-      description: "Fit and transform the input DataFrame."
-      parameters:
-        properties:
-          df:
-            type: DataFrame
-            description: "The input DataFrame."
-        required:
-          - df
-      returns:
-        df:
-          type: DataFrame
-          description: "The transformed DataFrame."
\ No newline at end of file
diff --git a/metagpt/tools/schemas/feature_engineering/GroupStat.yml b/metagpt/tools/schemas/feature_engineering/GroupStat.yml
deleted file mode 100644
index 6e0ba2877..000000000
--- a/metagpt/tools/schemas/feature_engineering/GroupStat.yml
+++ /dev/null
@@ -1,58 +0,0 @@
-GroupStat:
-  type: class
-  description: "Aggregate specified column in a DataFrame grouped by another column, adding new features named '<agg_col>_<agg_func>_by_<group_col>'."
-  methods:
-    __init__:
-      description: "Initialize self."
-      parameters:
-        properties:
-          group_col:
-            type: str
-            description: "Column used for grouping."
-          agg_col:
-            type: str
-            description: "Column on which aggregation is performed."
-          agg_funcs:
-            type: list
-            description: >-
-              List of aggregation functions to apply, such as ['mean', 'std'].
-              Each function must be supported by pandas.
-        required:
-          - group_col
-          - agg_col
-          - agg_funcs
-    fit:
-      description: "Fit the GroupStat model."
-      parameters:
-        properties:
-          df:
-            type: DataFrame
-            description: "The input DataFrame."
-        required:
-          - df
-    transform:
-      description: "Transform the input DataFrame with the fitted model."
-      parameters:
-        properties:
-          df:
-            type: DataFrame
-            description: "The input DataFrame."
-        required:
-          - df
-      returns:
-        df:
-          type: DataFrame
-          description: "The transformed DataFrame."
-    fit_transform:
-      description: "Fit and transform the input DataFrame."
-      parameters:
-        properties:
-          df:
-            type: DataFrame
-            description: "The input DataFrame."
-        required:
-          - df
-      returns:
-        df:
-          type: DataFrame
-          description: "The transformed DataFrame."
\ No newline at end of file
diff --git a/metagpt/tools/schemas/feature_engineering/KFoldTargetMeanEncoder.yml b/metagpt/tools/schemas/feature_engineering/KFoldTargetMeanEncoder.yml
deleted file mode 100644
index 79a673f9f..000000000
--- a/metagpt/tools/schemas/feature_engineering/KFoldTargetMeanEncoder.yml
+++ /dev/null
@@ -1,60 +0,0 @@
-KFoldTargetMeanEncoder:
-  type: class
-  description: "Adds a new feature to the DataFrame by k-fold mean encoding of a categorical column using the label column."
-  methods:
-    __init__:
-      description: "Initialize self."
-      parameters:
-        properties:
-          col:
-            type: str
-            description: "Column to be k-fold mean encoded."
-          label:
-            type: str
-            description: "Predicted label column."
-          n_splits:
-            type: int
-            description: "Number of splits for K-fold."
-            default: 5
-          random_state:
-            type: int
-            description: "Random seed."
-            default: 2021
-        required:
-          - col
-          - label
-    fit:
-      description: "Fit the KFoldTargetMeanEncoder model."
-      parameters:
-        properties:
-          df:
-            type: DataFrame
-            description: "The input DataFrame."
-        required:
-          - df
-    transform:
-      description: "Transform the input DataFrame with the fitted model."
-      parameters:
-        properties:
-          df:
-            type: DataFrame
-            description: "The input DataFrame."
-        required:
-          - df
-      returns:
-        df:
-          type: DataFrame
-          description: "The transformed DataFrame."
-    fit_transform:
-      description: "Fit and transform the input DataFrame."
-      parameters:
-        properties:
-          df:
-            type: DataFrame
-            description: "The input DataFrame."
-        required:
-          - df
-      returns:
-        df:
-          type: DataFrame
-          description: "The transformed DataFrame."
\ No newline at end of file
diff --git a/metagpt/tools/schemas/feature_engineering/PolynomialExpansion.yml b/metagpt/tools/schemas/feature_engineering/PolynomialExpansion.yml
deleted file mode 100644
index 62e6ad5b3..000000000
--- a/metagpt/tools/schemas/feature_engineering/PolynomialExpansion.yml
+++ /dev/null
@@ -1,548 +0,0 @@
-PolynomialExpansion:
-  type: class
-  description: "Add polynomial and interaction features from selected numeric columns to input DataFrame."
-  methods:
-    __init__:
-      description: "Initialize self."
-      parameters:
-        properties:
-          cols:
-            type: list
-            description: "Columns for polynomial expansion."
-          label_col:
-            type: str
-            description: "Label column name."
-          degree:
-            type: int
-            description: "The degree of the polynomial features."
-            default: 2
-        required:
-          - cols
-          - label_col
-    fit:
-      description: "Fit the PolynomialExpansion model."
-      parameters:
-        properties:
-          df:
-            type: DataFrame
-            description: "The input DataFrame."
-        required:
-          - df
-    transform:
-      description: "Transform the input DataFrame with the fitted model."
-      parameters:
-        properties:
-          df:
-            type: DataFrame
-            description: "The input DataFrame."
-        required:
-          - df
-      returns:
-        df:
-          type: DataFrame
-          description: "The transformed DataFrame without duplicated columns."
-    fit_transform:
-      description: "Fit and transform the input DataFrame."
-      parameters:
-        properties:
-          df:
-            type: DataFrame
-            description: "The input DataFrame without duplicated columns."
-        required:
-          - df
-      returns:
-        df:
-          type: DataFrame
-          description: "The transformed DataFrame."
-
-CatCount:
-  type: class
-  description: "Add value counts of a categorical column as new feature."
-  methods:
-    __init__:
-      description: "Initialize self."
-      parameters:
-        properties:
-          col:
-            type: str
-            description: "Column for value counts."
-        required:
-          - col
-    fit:
-      description: "Fit the CatCount model."
-      parameters:
-        properties:
-          df:
-            type: DataFrame
-            description: "The input DataFrame."
-        required:
-          - df
-    transform:
-      description: "Transform the input DataFrame with the fitted model."
-      parameters:
-        properties:
-          df:
-            type: DataFrame
-            description: "The input DataFrame."
-        required:
-          - df
-      returns:
-        df:
-          type: DataFrame
-          description: "The transformed DataFrame."
-    fit_transform:
-      description: "Fit and transform the input DataFrame."
-      parameters:
-        properties:
-          df:
-            type: DataFrame
-            description: "The input DataFrame."
-        required:
-          - df
-      returns:
-        df:
-          type: DataFrame
-          description: "The transformed DataFrame."
-
-TargetMeanEncoder:
-  type: class
-  description: "Encodes a categorical column by the mean of the label column, and adds the result as a new feature."
-  methods:
-    __init__:
-      description: "Initialize self."
-      parameters:
-        properties:
-          col:
-            type: str
-            description: "Column to be mean encoded."
-          label:
-            type: str
-            description: "Predicted label column."
-        required:
-          - col
-          - label
-    fit:
-      description: "Fit the TargetMeanEncoder model."
-      parameters:
-        properties:
-          df:
-            type: DataFrame
-            description: "The input DataFrame."
-        required:
-          - df
-    transform:
-      description: "Transform the input DataFrame with the fitted model."
-      parameters:
-        properties:
-          df:
-            type: DataFrame
-            description: "The input DataFrame."
-        required:
-          - df
-      returns:
-        df:
-          type: DataFrame
-          description: "The transformed DataFrame."
-    fit_transform:
-      description: "Fit and transform the input DataFrame."
-      parameters:
-        properties:
-          df:
-            type: DataFrame
-            description: "The input DataFrame."
-        required:
-          - df
-      returns:
-        df:
-          type: DataFrame
-          description: "The transformed DataFrame."
-
-KFoldTargetMeanEncoder:
-  type: class
-  description: "Adds a new feature to the DataFrame by k-fold mean encoding of a categorical column using the label column."
-  methods:
-    __init__:
-      description: "Initialize self."
-      parameters:
-        properties:
-          col:
-            type: str
-            description: "Column to be k-fold mean encoded."
-          label:
-            type: str
-            description: "Predicted label column."
-          n_splits:
-            type: int
-            description: "Number of splits for K-fold."
-            default: 5
-          random_state:
-            type: int
-            description: "Random seed."
-            default: 2021
-        required:
-          - col
-          - label
-    fit:
-      description: "Fit the KFoldTargetMeanEncoder model."
-      parameters:
-        properties:
-          df:
-            type: DataFrame
-            description: "The input DataFrame."
-        required:
-          - df
-    transform:
-      description: "Transform the input DataFrame with the fitted model."
-      parameters:
-        properties:
-          df:
-            type: DataFrame
-            description: "The input DataFrame."
-        required:
-          - df
-      returns:
-        df:
-          type: DataFrame
-          description: "The transformed DataFrame."
-    fit_transform:
-      description: "Fit and transform the input DataFrame."
-      parameters:
-        properties:
-          df:
-            type: DataFrame
-            description: "The input DataFrame."
-        required:
-          - df
-      returns:
-        df:
-          type: DataFrame
-          description: "The transformed DataFrame."
-
-CatCross:
-  type: class
-  description: "Add pairwise crossed features and convert them to numerical features."
-  methods:
-    __init__:
-      description: "Initialize self."
-      parameters:
-        properties:
-          cols:
-            type: list
-            description: "Columns to be pairwise crossed, at least 2 columns."
-          max_cat_num:
-            type: int
-            description: "Maximum unique categories per crossed feature."
-            default: 100
-      required:
-        - cols
-    fit:
-      description: "Fit the CatCross model."
-      parameters:
-        properties:
-          df:
-            type: DataFrame
-            description: "The input DataFrame."
-        required:
-          - df
-    transform:
-      description: "Transform the input DataFrame with the fitted model."
-      parameters:
-        properties:
-          df:
-            type: DataFrame
-            description: "The input DataFrame."
-        required:
-          - df
-      returns:
-        df:
-          type: DataFrame
-          description: "The transformed DataFrame."
-    fit_transform:
-      description: "Fit and transform the input DataFrame."
-      parameters:
-        properties:
-          df:
-            type: DataFrame
-            description: "The input DataFrame."
-        required:
-          - df
-      returns:
-        df:
-          type: DataFrame
-          description: "The transformed DataFrame."
-
-GroupStat:
-  type: class
-  description: "Aggregate specified column in a DataFrame grouped by another column, adding new features named '<agg_col>_<agg_func>_by_<group_col>'."
-  methods:
-    __init__:
-      description: "Initialize self."
-      parameters:
-        properties:
-          group_col:
-            type: str
-            description: "Column used for grouping."
-          agg_col:
-            type: str
-            description: "Column on which aggregation is performed."
-          agg_funcs:
-            type: list
-            description: >-
-              List of aggregation functions to apply, such as ['mean', 'std'].
-              Each function must be supported by pandas.
-        required:
-          - group_col
-          - agg_col
-          - agg_funcs
-    fit:
-      description: "Fit the GroupStat model."
-      parameters:
-        properties:
-          df:
-            type: DataFrame
-            description: "The input DataFrame."
-        required:
-          - df
-    transform:
-      description: "Transform the input DataFrame with the fitted model."
-      parameters:
-        properties:
-          df:
-            type: DataFrame
-            description: "The input DataFrame."
-        required:
-          - df
-      returns:
-        df:
-          type: DataFrame
-          description: "The transformed DataFrame."
-    fit_transform:
-      description: "Fit and transform the input DataFrame."
-      parameters:
-        properties:
-          df:
-            type: DataFrame
-            description: "The input DataFrame."
-        required:
-          - df
-      returns:
-        df:
-          type: DataFrame
-          description: "The transformed DataFrame."
-
-SplitBins:
-  type: class
-  description: "Inplace binning of continuous data into intervals, returning integer-encoded bin identifiers directly."
-  methods:
-    __init__:
-      description: "Initialize self."
-      parameters:
-        properties:
-          cols:
-            type: list
-            description: "Columns to be binned inplace."
-          strategy:
-            type: str
-            description: "Strategy used to define the widths of the bins."
-            default: quantile
-            enum:
-              - quantile
-              - uniform
-              - kmeans
-        required:
-          - cols
-    fit:
-      description: "Fit the SplitBins model."
-      parameters:
-        properties:
-          df:
-            type: DataFrame
-            description: "The input DataFrame."
-        required:
-          - df
-    transform:
-      description: "Transform the input DataFrame with the fitted model."
-      parameters:
-        properties:
-          df:
-            type: DataFrame
-            description: "The input DataFrame."
-        required:
-          - df
-      returns:
-        df:
-          type: DataFrame
-          description: "The transformed DataFrame."
-    fit_transform:
-      description: "Fit and transform the input DataFrame."
-      parameters:
-        properties:
-          df:
-            type: DataFrame
-            description: "The input DataFrame."
-        required:
-          - df
-      returns:
-        df:
-          type: DataFrame
-          description: "The transformed DataFrame."
-
-GeneralSelection:
-  type: class
-  description: "Drop all nan feats and feats with only one unique value."
-  methods:
-    __init__:
-      description: "Initialize self."
-      parameters:
-        properties:
-          label_col:
-            type: str
-            description: "Label column name."
-        required:
-          - label_col
-    fit:
-      description: "Fit the GeneralSelection model."
-      parameters:
-        properties:
-          df:
-            type: DataFrame
-            description: "The input DataFrame."
-        required:
-          - df
-    transform:
-      description: "Transform the input DataFrame with the fitted model."
-      parameters:
-        properties:
-          df:
-            type: DataFrame
-            description: "The input DataFrame."
-        required:
-          - df
-      returns:
-        df:
-          type: DataFrame
-          description: "The transformed DataFrame."
-    fit_transform:
-      description: "Fit and transform the input DataFrame."
-      parameters:
-        properties:
-          df:
-            type: DataFrame
-            description: "The input DataFrame."
-        required:
-          - df
-      returns:
-        df:
-          type: DataFrame
-          description: "The transformed DataFrame."
-
-
-TreeBasedSelection:
-  type: class
-  description: "Select features based on tree-based model and remove features with low importance."
-  methods:
-    __init__:
-      description: "Initialize self."
-      parameters:
-        properties:
-          label_col:
-            type: str
-            description: "Label column name."
-          task_type:
-            type: str
-            description: "Task type, 'cls' for classification, 'mcls' for multi-class classification, 'reg' for regression."
-            enum:
-              - cls
-              - mcls
-              - reg
-        required:
-          - label_col
-          - task_type
-    fit:
-      description: "Fit the TreeBasedSelection model."
-      parameters:
-        properties:
-          df:
-            type: DataFrame
-            description: "The input DataFrame."
-        required:
-          - df
-    transform:
-      description: "Transform the input DataFrame with the fitted model."
-      parameters:
-        properties:
-          df:
-            type: DataFrame
-            description: "The input DataFrame."
-        required:
-          - df
-      returns:
-        df:
-          type: DataFrame
-          description: "The transformed DataFrame contain label_col."
-    fit_transform:
-      description: "Fit and transform the input DataFrame."
-      parameters:
-        properties:
-          df:
-            type: DataFrame
-            description: "The input DataFrame."
-        required:
-          - df
-      returns:
-        df:
-          type: DataFrame
-          description: "The transformed DataFrame contain label_col."
-
-VarianceBasedSelection:
-  type: class
-  description: "Select features based on variance and remove features with low variance."
-  methods:
-    __init__:
-      description: "Initialize self."
-      parameters:
-        properties:
-          label_col:
-            type: str
-            description: "Label column name."
-          threshold:
-            type: float
-            description: "Threshold for variance."
-            default: 0.0
-        required:
-          - label_col
-    fit:
-      description: "Fit the VarianceBasedSelection model."
-      parameters:
-        properties:
-          df:
-            type: DataFrame
-            description: "The input DataFrame."
-        required:
-          - df
-    transform:
-      description: "Transform the input DataFrame with the fitted model."
-      parameters:
-        properties:
-          df:
-            type: DataFrame
-            description: "The input DataFrame."
-        required:
-          - df
-      returns:
-        df:
-          type: DataFrame
-          description: "The transformed DataFrame contain label_col."
-    fit_transform:
-      description: "Fit and transform the input DataFrame."
-      parameters:
-        properties:
-          df:
-            type: DataFrame
-            description: "The input DataFrame."
-        required:
-          - df
-      returns:
-        df:
-          type: DataFrame
-          description: "The transformed DataFrame contain label_col."
\ No newline at end of file
diff --git a/metagpt/tools/schemas/feature_engineering/SplitBins.yml b/metagpt/tools/schemas/feature_engineering/SplitBins.yml
deleted file mode 100644
index 4e0171406..000000000
--- a/metagpt/tools/schemas/feature_engineering/SplitBins.yml
+++ /dev/null
@@ -1,56 +0,0 @@
-SplitBins:
-  type: class
-  description: "Inplace binning of continuous data into intervals, returning integer-encoded bin identifiers directly."
-  methods:
-    __init__:
-      description: "Initialize self."
-      parameters:
-        properties:
-          cols:
-            type: list
-            description: "Columns to be binned inplace."
-          strategy:
-            type: str
-            description: "Strategy used to define the widths of the bins."
-            default: quantile
-            enum:
-              - quantile
-              - uniform
-              - kmeans
-        required:
-          - cols
-    fit:
-      description: "Fit the SplitBins model."
-      parameters:
-        properties:
-          df:
-            type: DataFrame
-            description: "The input DataFrame."
-        required:
-          - df
-    transform:
-      description: "Transform the input DataFrame with the fitted model."
-      parameters:
-        properties:
-          df:
-            type: DataFrame
-            description: "The input DataFrame."
-        required:
-          - df
-      returns:
-        df:
-          type: DataFrame
-          description: "The transformed DataFrame."
-    fit_transform:
-      description: "Fit and transform the input DataFrame."
-      parameters:
-        properties:
-          df:
-            type: DataFrame
-            description: "The input DataFrame."
-        required:
-          - df
-      returns:
-        df:
-          type: DataFrame
-          description: "The transformed DataFrame."
\ No newline at end of file
diff --git a/metagpt/tools/schemas/feature_engineering/TargetMeanEncoder.yml b/metagpt/tools/schemas/feature_engineering/TargetMeanEncoder.yml
deleted file mode 100644
index 86416ccbb..000000000
--- a/metagpt/tools/schemas/feature_engineering/TargetMeanEncoder.yml
+++ /dev/null
@@ -1,52 +0,0 @@
-TargetMeanEncoder:
-  type: class
-  description: "Encodes a categorical column by the mean of the label column, and adds the result as a new feature."
-  methods:
-    __init__:
-      description: "Initialize self."
-      parameters:
-        properties:
-          col:
-            type: str
-            description: "Column to be mean encoded."
-          label:
-            type: str
-            description: "Predicted label column."
-        required:
-          - col
-          - label
-    fit:
-      description: "Fit the TargetMeanEncoder model."
-      parameters:
-        properties:
-          df:
-            type: DataFrame
-            description: "The input DataFrame."
-        required:
-          - df
-    transform:
-      description: "Transform the input DataFrame with the fitted model."
-      parameters:
-        properties:
-          df:
-            type: DataFrame
-            description: "The input DataFrame."
-        required:
-          - df
-      returns:
-        df:
-          type: DataFrame
-          description: "The transformed DataFrame."
-    fit_transform:
-      description: "Fit and transform the input DataFrame."
-      parameters:
-        properties:
-          df:
-            type: DataFrame
-            description: "The input DataFrame."
-        required:
-          - df
-      returns:
-        df:
-          type: DataFrame
-          description: "The transformed DataFrame."
\ No newline at end of file
diff --git a/metagpt/tools/schemas/feature_engineering/TreeBasedSelection.yml b/metagpt/tools/schemas/feature_engineering/TreeBasedSelection.yml
deleted file mode 100644
index c210effea..000000000
--- a/metagpt/tools/schemas/feature_engineering/TreeBasedSelection.yml
+++ /dev/null
@@ -1,56 +0,0 @@
-TreeBasedSelection:
-  type: class
-  description: "Select features based on tree-based model and remove features with low importance."
-  methods:
-    __init__:
-      description: "Initialize self."
-      parameters:
-        properties:
-          label_col:
-            type: str
-            description: "Label column name."
-          task_type:
-            type: str
-            description: "Task type, 'cls' for classification, 'mcls' for multi-class classification, 'reg' for regression."
-            enum:
-              - cls
-              - mcls
-              - reg
-        required:
-          - label_col
-          - task_type
-    fit:
-      description: "Fit the TreeBasedSelection model."
-      parameters:
-        properties:
-          df:
-            type: DataFrame
-            description: "The input DataFrame."
-        required:
-          - df
-    transform:
-      description: "Transform the input DataFrame with the fitted model."
-      parameters:
-        properties:
-          df:
-            type: DataFrame
-            description: "The input DataFrame."
-        required:
-          - df
-      returns:
-        df:
-          type: DataFrame
-          description: "The transformed DataFrame contain label_col."
-    fit_transform:
-      description: "Fit and transform the input DataFrame."
-      parameters:
-        properties:
-          df:
-            type: DataFrame
-            description: "The input DataFrame."
-        required:
-          - df
-      returns:
-        df:
-          type: DataFrame
-          description: "The transformed DataFrame contain label_col."
\ No newline at end of file
diff --git a/metagpt/tools/schemas/feature_engineering/VarianceBasedSelection.yml b/metagpt/tools/schemas/feature_engineering/VarianceBasedSelection.yml
deleted file mode 100644
index 6da4c3e7f..000000000
--- a/metagpt/tools/schemas/feature_engineering/VarianceBasedSelection.yml
+++ /dev/null
@@ -1,52 +0,0 @@
-VarianceBasedSelection:
-  type: class
-  description: "Select features based on variance and remove features with low variance."
-  methods:
-    __init__:
-      description: "Initialize self."
-      parameters:
-        properties:
-          label_col:
-            type: str
-            description: "Label column name."
-          threshold:
-            type: float
-            description: "Threshold for variance."
-            default: 0.0
-        required:
-          - label_col
-    fit:
-      description: "Fit the VarianceBasedSelection model."
-      parameters:
-        properties:
-          df:
-            type: DataFrame
-            description: "The input DataFrame."
-        required:
-          - df
-    transform:
-      description: "Transform the input DataFrame with the fitted model."
-      parameters:
-        properties:
-          df:
-            type: DataFrame
-            description: "The input DataFrame."
-        required:
-          - df
-      returns:
-        df:
-          type: DataFrame
-          description: "The transformed DataFrame contain label_col."
-    fit_transform:
-      description: "Fit and transform the input DataFrame."
-      parameters:
-        properties:
-          df:
-            type: DataFrame
-            description: "The input DataFrame."
-        required:
-          - df
-      returns:
-        df:
-          type: DataFrame
-          description: "The transformed DataFrame contain label_col."
\ No newline at end of file
diff --git a/metagpt/tools/schemas/image2webpage/GPTvGenerator.yml b/metagpt/tools/schemas/image2webpage/GPTvGenerator.yml
deleted file mode 100644
index 1ba2c2b08..000000000
--- a/metagpt/tools/schemas/image2webpage/GPTvGenerator.yml
+++ /dev/null
@@ -1,36 +0,0 @@
-GPTvGenerator:
-  type: class
-  description: "Class for generating webpages at once."
-  methods:
-    __init__:
-      description: "Initialize Vision class with default values."
-
-    generate_webpages:
-      description: "Generate webpages including all code(HTML, CSS and JavaScript) in one go based on the image."
-      parameters:
-        properties:
-          image_path:
-            type: str
-            description: "The path of the image file"
-      required:
-        - image_path
-      returns:
-        type: str
-        description: "Generated webpages content."
-
-    save_webpages:
-      description: "Save webpages including all code(HTML, CSS and JavaScript) at once"
-      parameters:
-        properties:
-          image_path:
-            type: str
-            description: "The path of the image file"
-          webpages:
-            type: str
-            description: "The generated webpages content"
-      required:
-        - image_path
-        - webpages
-      returns:
-        type: Path
-        description: "The path of the saved webpages"
\ No newline at end of file
diff --git a/metagpt/tools/schemas/stable_diffusion/SDEngine.yml b/metagpt/tools/schemas/stable_diffusion/SDEngine.yml
deleted file mode 100644
index a93742a1d..000000000
--- a/metagpt/tools/schemas/stable_diffusion/SDEngine.yml
+++ /dev/null
@@ -1,58 +0,0 @@
-SDEngine:
-  type: class
-  description: "Generate image using stable diffusion model"
-  methods:
-    __init__:
-      description: "Initialize the SDEngine instance."
-      parameters:
-        properties:
-          sd_url:
-            type: str
-            description: "URL of the stable diffusion service."
-    simple_run_t2i:
-      description: "Run the stable diffusion API for multiple prompts, calling the stable diffusion API to generate images."
-      parameters:
-        properties:
-          payload:
-            type: dict
-            description: "Dictionary of input parameters for the stable diffusion API."
-          auto_save:
-            type: bool
-            description: "Save generated images automatically."
-        required:
-          - prompts
-    run_t2i:
-      type: async function
-      description: "Run the stable diffusion API for multiple prompts, calling the stable diffusion API to generate images."
-      parameters:
-        properties:
-          payloads:
-            type: list
-            description: "List of payload, each payload is a dictionary of input parameters for the stable diffusion API."
-        required:
-          - payloads
-    construct_payload:
-      description: "Modify and set the API parameters for image generation."
-      parameters:
-        properties:
-          prompt:
-            type: str
-            description: "Text input for image generation."
-        required:
-          - prompt
-      returns:
-        payload:
-          type: dict
-          description: "Updated parameters for the stable diffusion API."
-    save:
-      description: "Save generated images to the output directory."
-      parameters:
-        properties:
-          imgs:
-            type: str
-            description: "Generated images."
-          save_name:
-            type: str
-            description: "Output image name. Default is empty."
-        required:
-          - imgs
diff --git a/metagpt/tools/schemas/web_scraping/scrape_web_playwright.yml b/metagpt/tools/schemas/web_scraping/scrape_web_playwright.yml
deleted file mode 100644
index a6ff7d6c7..000000000
--- a/metagpt/tools/schemas/web_scraping/scrape_web_playwright.yml
+++ /dev/null
@@ -1,21 +0,0 @@
-scrape_web_playwright:
-  type: async funciton
-  description: "Scrape and save the HTML structure and inner text content of a web page using Playwright."
-  parameters:
-    properties:
-      url:
-        type: str
-        description: "web url"
-      \*url:
-        type: Non-Keyword Arguments
-        description: "other web urls, you can assagin sub url link to it."
-    required:
-      - url
-  returns:
-    inner_text:
-      type: str
-      description: The inner text content of the web page.
-    html:
-      type: str
-      description: The html structure of the web page.
-