Add comprehensive documentation for all tools

- Added documentation for file operation tools
- Added documentation for search tools
- Added documentation for web scraping tools
- Added documentation for specialized tools (RAG, code interpreter)
- Added documentation for API-based tools (SerpApi, Serply)

Link to Devin run: https://app.devin.ai/sessions/d2f72a2dfb214659aeb3e9f67ed961f7

Co-Authored-By: Joe Moura <joao@crewai.com>

docs/tools/serpapi-google-shopping-tool.mdx

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+---
+title: SerpApi Google Shopping Tool
+description: A tool for searching Google Shopping using the SerpApi service
+---
+
+# SerpApi Google Shopping Tool
+
+The SerpApi Google Shopping Tool enables searching Google Shopping results using the SerpApi service. It provides location-aware shopping search capabilities with comprehensive result filtering.
+
+## Installation
+
+```bash
+pip install 'crewai[tools]'
+pip install serpapi
+```
+
+## Prerequisites
+
+You need a SerpApi API key to use this tool. You can get one from [SerpApi's website](https://serpapi.com/manage-api-key).
+
+Set your API key as an environment variable:
+```bash
+export SERPAPI_API_KEY="your_api_key_here"
+```
+
+## Usage
+
+Here's how to use the SerpApi Google Shopping Tool:
+
+```python
+from crewai import Agent
+from crewai_tools import SerpApiGoogleShoppingTool
+
+# Initialize the tool
+shopping_tool = SerpApiGoogleShoppingTool()
+
+# Create an agent with the tool
+shopping_agent = Agent(
+    role='Shopping Researcher',
+    goal='Find the best shopping deals',
+    backstory='I help find and analyze shopping options',
+    tools=[shopping_tool]
+)
+
+# Use in a task
+task = Task(
+    description="Find best deals for gaming laptops",
+    agent=shopping_agent,
+    context={
+        "search_query": "gaming laptop deals",
+        "location": "United States"  # Optional
+    }
+)
+```
+
+## Input Schema
+
+```python
+class SerpApiGoogleShoppingToolSchema(BaseModel):
+    search_query: str  # The search query for Google Shopping
+    location: Optional[str] = None  # Optional location for localized results
+```
+
+## Function Signatures
+
+### Base Tool Initialization
+```python
+def __init__(self, **kwargs):
+    """
+    Initialize the SerpApi tool with API credentials.
+    
+    Raises:
+        ImportError: If serpapi package is not installed
+        ValueError: If SERPAPI_API_KEY environment variable is not set
+    """
+```
+
+### Search Execution
+```python
+def _run(
+    self,
+    **kwargs: Any,
+) -> dict:
+    """
+    Execute the Google Shopping search.
+    
+    Args:
+        search_query (str): The search query for Google Shopping
+        location (Optional[str]): Optional location for results
+    
+    Returns:
+        dict: Filtered search results from Google Shopping
+        
+    Raises:
+        HTTPError: If the API request fails
+    """
+```
+
+## Best Practices
+
+1. **API Key Management**:
+   - Store the API key securely in environment variables
+   - Never hardcode the API key in your code
+   - Verify API key validity before making requests
+
+2. **Search Optimization**:
+   - Use specific, targeted search queries
+   - Include relevant product details in queries
+   - Leverage location parameter for regional pricing
+
+3. **Error Handling**:
+   - Handle API rate limits gracefully
+   - Implement retry logic for failed requests
+   - Validate input parameters before making requests
+
+## Example Integration
+
+Here's a complete example showing how to integrate the SerpApi Google Shopping Tool with CrewAI:
+
+```python
+from crewai import Agent, Task, Crew
+from crewai_tools import SerpApiGoogleShoppingTool
+
+# Initialize the tool
+shopping_tool = SerpApiGoogleShoppingTool()
+
+# Create an agent with the tool
+researcher = Agent(
+    role='Shopping Analyst',
+    goal='Find and analyze the best shopping deals',
+    backstory="""I am an expert at finding the best shopping deals 
+    and analyzing product offerings across different regions.""",
+    tools=[shopping_tool]
+)
+
+# Create tasks
+search_task = Task(
+    description="""
+    Research gaming laptops with the following criteria:
+    1. Price range: $800-$1500
+    2. Released in the last year
+    3. Compare prices across different retailers
+    
+    Provide a comprehensive analysis of the findings.
+    """,
+    agent=researcher,
+    context={
+        "search_query": "gaming laptop RTX 4060 2023",
+        "location": "United States"
+    }
+)
+
+# Create and run the crew
+crew = Crew(
+    agents=[researcher],
+    tasks=[search_task]
+)
+
+result = crew.kickoff()
+```
+
+## Error Handling
+
+The tool handles various error scenarios:
+
+1. **Missing API Key**:
+   ```python
+   try:
+       tool = SerpApiGoogleShoppingTool()
+   except ValueError as e:
+       print("API key not found. Set SERPAPI_API_KEY environment variable.")
+   ```
+
+2. **API Request Errors**:
+   ```python
+   try:
+       results = tool._run(
+           search_query="gaming laptop",
+           location="United States"
+       )
+   except HTTPError as e:
+       print(f"API request failed: {str(e)}")
+   ```
+
+3. **Invalid Parameters**:
+   ```python
+   try:
+       results = tool._run(
+           search_query="",  # Empty query
+           location="Invalid Location"
+       )
+   except ValueError as e:
+       print("Invalid search parameters provided.")
+   ```
+
+## Response Format
+
+The tool returns a filtered dictionary containing Google Shopping results. Example response structure:
+
+```python
+{
+    "shopping_results": [
+        {
+            "title": "Product Title",
+            "price": "$999.99",
+            "link": "https://...",
+            "source": "Retailer Name",
+            "rating": 4.5,
+            "reviews": 123,
+            "thumbnail": "https://..."
+        }
+        # Additional results...
+    ],
+    "organic_results": [
+        {
+            "title": "Related Product",
+            "link": "https://...",
+            "snippet": "Product description..."
+        }
+        # Additional organic results...
+    ]
+}
+```
+
+The response is automatically filtered to remove metadata and unnecessary fields, focusing on the most relevant shopping information.