What is Websearch Tools

webSearch-Tools is a powerful web search and content extraction tool built with Python, utilizing the Firecrawl API for advanced web scraping, searching, and content analysis capabilities via the MCP Server.

Use cases

Use cases include performing intelligent web searches, extracting specific information from web pages, crawling websites for data collection, and scraping content for analysis or reporting.

How to use

To use webSearch-Tools, set up Claude for Desktop with the appropriate configuration, including API keys and directory paths. After configuration, access the tools through the tools menu in Claude for Desktop.

Key features

Key features include advanced web search with customizable parameters, content extraction using natural language prompts, web crawling with configurable depth, web scraping with various output formats, and seamless integration with MCP.

Where to use

webSearch-Tools can be used in various fields such as data analysis, research, content creation, and any area requiring web scraping and information extraction.

WebSearch - Advanced Web Search and Content Extraction Tool

A powerful web search and content extraction tool built with Python, leveraging the Firecrawl API for advanced web scraping, searching, and content analysis capabilities.

🚀 Features

• Advanced Web Search: Perform intelligent web searches with customizable parameters
• Content Extraction: Extract specific information from web pages using natural language prompts
• Web Crawling: Crawl websites with configurable depth and limits
• Web Scraping: Scrape web pages with support for various output formats
• MCP Integration: Built as a Model Context Protocol (MCP) server for seamless integration

📋 Prerequisites

• Python 3.8 or higher
• uv package manager
• Firecrawl API key
• OpenAI API key (optional, for enhanced features)
• Tavily API key (optional, for additional search capabilities)

🛠️ Installation

1. Install uv:

# On Windows (using pip)
pip install uv

# On Unix/MacOS
curl -LsSf https://astral.sh/uv/install.sh | sh

# Add uv to PATH (Unix/MacOS)
export PATH="$HOME/.local/bin:$PATH"

# Add uv to PATH (Windows - add to Environment Variables)
# Add: %USERPROFILE%\.local\bin

2. Clone the repository:

git clone https://github.com/yourusername/websearch.git
cd websearch

3. Create and activate a virtual environment with uv:

# Create virtual environment
uv venv

# Activate on Windows
.\.venv\Scripts\activate.ps1

# Activate on Unix/MacOS
source .venv/bin/activate

4. Install dependencies with uv:

# Install from requirements.txt
uv sync

5. Set up environment variables:

# Create .env file
touch .env

# Add your API keys
FIRECRAWL_API_KEY=your_firecrawl_api_key
OPENAI_API_KEY=your_openai_api_key

🎯 Usage

Setting Up With Claude for Desktop

Instead of running the server directly, you can configure Claude for Desktop to access the WebSearch tools:

1. Locate or create your Claude for Desktop configuration file:

   • Windows: %env:AppData%\Claude\claude_desktop_config.json
   • macOS: ~/Library/Application Support/Claude/claude_desktop_config.json

2. Add the WebSearch server configuration to the mcpServers section:

{
  "mcpServers": {
    "websearch": {
      "command": "uv",
      "args": [
        "--directory",
        "D:\\ABSOLUTE\\PATH\\TO\\WebSearch",
        "run",
        "main.py"
      ]
    }
  }
}

3. Make sure to replace the directory path with the absolute path to your WebSearch project folder.

4. Save the configuration file and restart Claude for Desktop.

5. Once configured, the WebSearch tools will appear in the tools menu (hammer icon) in Claude for Desktop.

Available Tools

1. Search

2. Extract Information

3. Crawl Websites

4. Scrape Content

📚 API Reference

Search

• query (str): The search query
• Returns: Search results in JSON format

Extract

• urls (List[str]): List of URLs to extract information from
• prompt (str): Instructions for extraction
• enableWebSearch (bool): Enable supplementary web search
• showSources (bool): Include source references
• Returns: Extracted information in specified format

Crawl

• url (str): Starting URL
• maxDepth (int): Maximum crawl depth
• limit (int): Maximum pages to crawl
• Returns: Crawled content in markdown/HTML format

Scrape

• url (str): Target URL
• Returns: Scraped content with optional screenshots

🔧 Configuration

Environment Variables

The tool requires certain API keys to function. We provide a .env.example file that you can use as a template:

1. Copy the example file:

# On Unix/MacOS
cp .env.example .env

# On Windows
copy .env.example .env

2. Edit the .env file with your API keys:

# OpenAI API key - Required for AI-powered features
OPENAI_API_KEY=your_openai_api_key_here

# Firecrawl API key - Required for web scraping and searching
FIRECRAWL_API_KEY=your_firecrawl_api_key_here

Getting the API Keys

1. OpenAI API Key:

   • Visit OpenAI’s platform
   • Sign up or log in
   • Navigate to API keys section
   • Create a new secret key

2. Firecrawl API Key:

   • Visit Firecrawl’s website
   • Create an account
   • Navigate to your dashboard
   • Generate a new API key

If everything is configured correctly, you should receive a JSON response with search results.

Troubleshooting

If you encounter errors:

1. Ensure all required API keys are set in your .env file
2. Verify the API keys are valid and have not expired
3. Check that the .env file is in the root directory of the project
4. Make sure the environment variables are being loaded correctly

🤝 Contributing

1. Fork the repository
2. Create your feature branch (git checkout -b feature/AmazingFeature)
3. Commit your changes (git commit -m 'Add some AmazingFeature')
4. Push to the branch (git push origin feature/AmazingFeature)
5. Open a Pull Request

📝 License

This project is licensed under the MIT License - see the LICENSE file for details.

🙏 Acknowledgments

• Firecrawl for their powerful web scraping API
• OpenAI for AI capabilities
• MCPThe MCP community for the protocol specification

📬 Contact

José Martín Rodriguez Mortaloni - @m4s1t425 - [email protected]

Made with ❤️ using Python and Firecrawl