# Web Search Tool Decorator Enhancement

## ✅ Enhancement Complete

Successfully added a comprehensive `@tool` decorator to the `web_search` function to enable better LLM discovery and usage guidance.

## 🔄 What Was Added

### Complete @tool Decorator
```python
@tool(
    description="Search the web for real-time information using DuckDuckGo (no API key required)",
    tags=["web", "search", "internet", "information", "research"],
    when_to_use="When you need current information, research topics, or verify facts that might not be in your training data",
    examples=[
        {
            "description": "Search for current programming best practices",
            "arguments": {
                "query": "python best practices 2025",
                "num_results": 5
            }
        },
        {
            "description": "Research a technology or framework",
            "arguments": {
                "query": "semantic search embedding models comparison",
                "num_results": 3
            }
        },
        {
            "description": "Get current news or events",
            "arguments": {
                "query": "AI developments 2025"
            }
        },
        {
            "description": "Find documentation or tutorials",
            "arguments": {
                "query": "LanceDB vector database tutorial",
                "num_results": 4
            }
        }
    ]
)
def web_search(query: str, num_results: int = 5) -> str:
```

## 🎯 LLM Discovery Benefits

### 1. **Rich Metadata**
- **Description**: Clear, concise explanation of capabilities
- **Tags**: Searchable categorization (`web`, `search`, `internet`, `information`, `research`)
- **When to use**: Specific guidance on appropriate use cases

### 2. **Comprehensive Examples**
- **4 diverse examples** covering different use cases:
  - Programming best practices research
  - Technology/framework comparison
  - Current news and events
  - Documentation and tutorials

### 3. **Consistent Structure**
- Follows the same pattern as other tools (`list_files`, `search_files`)
- Provides `arguments` structure for each example
- Shows both required and optional parameters

## 💡 Key Features of the Decorator

### Description
```
"Search the web for real-time information using DuckDuckGo (no API key required)"
```
- Emphasizes **real-time** information capability
- Mentions **DuckDuckGo** (no API key needed)
- Clear and actionable

### Tags
```
["web", "search", "internet", "information", "research"]
```
- **Categorization**: Easy filtering and discovery
- **Semantic grouping**: Related to information gathering
- **Search optimization**: Multiple relevant keywords

### When to Use
```
"When you need current information, research topics, or verify facts that might not be in your training data"
```
- **Clear guidance**: Specific use cases
- **Distinguishes from training data**: Emphasizes real-time aspect
- **Broad applicability**: Research, verification, current events

### Examples Structure
Each example includes:
- **Description**: What the example demonstrates
- **Arguments**: Exact parameters to use
- **Variety**: Different domains and use cases

## 🔧 Example Use Cases

### 1. **Current Programming Best Practices**
```python
web_search("python best practices 2025", num_results=5)
```
- Research latest development standards
- Find current recommendations
- Verify modern approaches

### 2. **Technology Research**
```python
web_search("semantic search embedding models comparison", num_results=3)
```
- Compare different technologies
- Research implementation options
- Find technical evaluations

### 3. **Current Events**
```python
web_search("AI developments 2025")
```
- Get latest news
- Track industry developments
- Monitor current trends

### 4. **Documentation & Tutorials**
```python
web_search("LanceDB vector database tutorial", num_results=4)
```
- Find learning resources
- Locate documentation
- Discover tutorials

## 🚀 Benefits for LLM Systems

### 1. **Improved Tool Discovery**
- LLMs can easily identify when web search is appropriate
- Clear differentiation from other search tools (`search_files` vs `web_search`)
- Rich metadata enables intelligent tool selection

### 2. **Better Usage Guidance**
- Examples provide concrete usage patterns
- `when_to_use` field guides appropriate application
- Parameter examples show proper argument structure

### 3. **Enhanced User Experience**
- Consistent tool interface across the system
- Comprehensive documentation embedded in code
- Self-documenting API through decorators

### 4. **Maintainability**
- Centralized tool metadata
- Standardized documentation format
- Easy to update and extend

## 📊 Verification Results

✅ **All Tests Passed**:
- ✅ Decorator structure complete
- ✅ All required fields present (`description`, `tags`, `when_to_use`, `examples`)
- ✅ Comprehensive examples included
- ✅ Syntax validation passed
- ✅ Content verification passed
- ✅ Ready for LLM discovery and usage

## 🎊 Conclusion

The `@tool` decorator enhancement makes `web_search` **significantly more discoverable and usable** for LLM systems:

- **Rich metadata** enables intelligent tool selection
- **Clear guidance** helps LLMs use the tool appropriately  
- **Comprehensive examples** provide concrete usage patterns
- **Consistent format** maintains system-wide standards
- **No breaking changes** - purely additive enhancement

The tool is now fully integrated into the AbstractLLM tool discovery system and ready for enhanced LLM usage! 🚀
