docs: Audit use of tabs and details (#9196)

* audit details

* tabs pt 1

* tabs pt 2

* tabs pt 3

* tabs to details

docs/docs/Agents/agents-tools.mdx

@@ -46,92 +46,92 @@ For example, the default tool name is `Agent`. Edit the name to `Agent-gpt-41`,
 
 ## Add custom components as tools {#components-as-tools}
 
-An agent can use custom components as tools.
+An agent can use [custom components](/components-custom-components) as tools.
 
 1. To add a custom component to the agent flow, click **New Custom Component**.
 
-2. Add custom Python code to the custom component.
-For example, to create a text analyzer component, paste the below code into the custom component's **Code** pane.
+2. Enter Python code into the **Code** pane to create the custom component.
 
-<details open>
-<summary>Python</summary>
+    If you don't already have code for a custom component, you can use the following code snippet as an example before creating your own.
 
-```python
-from langflow.custom import Component
-from langflow.io import MessageTextInput, Output
-from langflow.schema import Data
-import re
+    <details>
+    <summary>Text Analyzer custom component</summary>
 
-class TextAnalyzerComponent(Component):
-    display_name = "Text Analyzer"
-    description = "Analyzes and transforms input text."
-    documentation: str = "http://docs.langflow.org/components/custom"
-    icon = "chart-bar"
-    name = "TextAnalyzerComponent"
+    This code creates a text analyzer component.
 
-    inputs = [
-        MessageTextInput(
-            name="input_text",
-            display_name="Input Text",
-            info="Enter text to analyze",
-            value="Hello, World!",
-            tool_mode=True,
-        ),
-    ]
+    ```python
+    from langflow.custom import Component
+    from langflow.io import MessageTextInput, Output
+    from langflow.schema import Data
+    import re
 
-    outputs = [
-        Output(display_name="Analysis Result", name="output", method="analyze_text"),
-    ]
+    class TextAnalyzerComponent(Component):
+        display_name = "Text Analyzer"
+        description = "Analyzes and transforms input text."
+        documentation: str = "http://docs.langflow.org/components/custom"
+        icon = "chart-bar"
+        name = "TextAnalyzerComponent"
 
-    def analyze_text(self) -> Data:
-        text = self.input_text
+        inputs = [
+            MessageTextInput(
+                name="input_text",
+                display_name="Input Text",
+                info="Enter text to analyze",
+                value="Hello, World!",
+                tool_mode=True,
+            ),
+        ]
 
-        # Perform text analysis
-        word_count = len(text.split())
-        char_count = len(text)
-        sentence_count = len(re.findall(r'\w+[.!?]', text))
+        outputs = [
+            Output(display_name="Analysis Result", name="output", method="analyze_text"),
+        ]
 
-        # Transform text
-        reversed_text = text[::-1]
-        uppercase_text = text.upper()
+        def analyze_text(self) -> Data:
+            text = self.input_text
 
-        analysis_result = {
-            "original_text": text,
-            "word_count": word_count,
-            "character_count": char_count,
-            "sentence_count": sentence_count,
-            "reversed_text": reversed_text,
-            "uppercase_text": uppercase_text
-        }
+            # Perform text analysis
+            word_count = len(text.split())
+            char_count = len(text)
+            sentence_count = len(re.findall(r'\w+[.!?]', text))
 
-        data = Data(value=analysis_result)
-        self.status = data
-        return data
-```
-</details>
+            # Transform text
+            reversed_text = text[::-1]
+            uppercase_text = text.upper()
+
+            analysis_result = {
+                "original_text": text,
+                "word_count": word_count,
+                "character_count": char_count,
+                "sentence_count": sentence_count,
+                "reversed_text": reversed_text,
+                "uppercase_text": uppercase_text
+            }
+
+            data = Data(value=analysis_result)
+            self.status = data
+            return data
+    ```
+    </details>
 
 3. To use the custom component as a tool, click **Tool Mode**.
 4. Connect the custom component's tool output to the agent's tools input.
 5. Open the <Icon name="Play" aria-hidden="true" /> **Playground** and instruct the agent, `Use the text analyzer on this text: "Agents really are thinking machines!"`
 
-<details open>
-<summary>Response</summary>
-```
-AI
-gpt-4o
-Finished
-0.6s
-Here is the analysis of the text "Agents really are thinking machines!":
-Original Text: Agents really are thinking machines!
-Word Count: 5
-Character Count: 36
-Sentence Count: 1
-Reversed Text: !senihcam gnikniht era yllaer stnegA
-Uppercase Text: AGENTS REALLY ARE THINKING MACHINES!
-```
-</details>
+    Based on your instruction, the agent should call the `analyze_text` action and return the result.
+    For example:
 
-The agent correctly calls the `analyze_text` action and returns the result to the Playground.
+    ```
+    gpt-4o
+    Finished
+    0.6s
+    Here is the analysis of the text "Agents really are thinking machines!":
+    Original Text: Agents really are thinking machines!
+    Word Count: 5
+    Character Count: 36
+    Sentence Count: 1
+    Reversed Text: !senihcam gnikniht era yllaer stnegA
+    Uppercase Text: AGENTS REALLY ARE THINKING MACHINES!
+    ```
 
 ## Make any component a tool