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WebSearch - Advanced Web Search and Content Extraction Tool

WebSearch - Advanced Web Search and Content Extraction Tool

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github.com/m4s1t4Search1Updated 86w ago

About WebSearch - Advanced Web Search and Content Extraction Tool

WebSearch is a Python MCP server for web search and page processing through the Firecrawl API. It provides tools to search, extract information, crawl sites, and scrape pages, with an optional OpenAI API key for enhanced features.

Key features

  1. 01Search the web with configurable parameters
  2. 02Extract information using natural-language prompts
  3. 03Crawl sites with depth and page limits
  4. 04Scrape pages with multiple output formats

Use cases

  1. 01Find web results for a query
  2. 02Extract specified details from a set of URLs
  3. 03Collect page content across a site
  4. 04Scrape a page for content or screenshots

MCP support

The server exposes Search, Extract, Crawl, and Scrape as MCP tools for clients such as Claude for Desktop.

Configuration

Local server config is the same for every client. Copy it into your client’s MCP config file.

No install method listed yet

We haven’t collected a package or remote endpoint for this server. Follow the instructions in the project README.

Repository

Tools

No tool list yet

Local servers expose tools only once running on your machine, and the maintainer has not provided a tool list.

Details

From the project README

WebSearch - Advanced Web Search and Content Extraction Tool

License Python Version Firecrawl uv

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
  1. Clone the repository:
git clone https://github.com/yourusername/websearch.git
cd websearch
  1. 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
  1. Install dependencies with uv:
# Install from requirements.txt
uv sync
  1. 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"
      ]
    }
  }
}
  1. Make sure to replace the directory path with the absolute path to your WebSearch project folder.

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

  3. 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
  1. 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

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