vishalkg/web-search

WebSearch MCP Server

High-performance Model Context Protocol (MCP) server for web search and content extraction with intelligent fallback system.

✨ Features

• 🚀 Fast: Async implementation with parallel execution
• 🔍 Multi-Engine: Google, Bing, DuckDuckGo, Startpage, Brave Search
• 🛡️ Intelligent Fallbacks: Google→Startpage, Bing→DuckDuckGo, Brave (standalone)
• 📄 Content Extraction: Clean text extraction from web pages
• 💾 Smart Caching: LRU cache with compression and deduplication
• 🔑 API Integration: Google Custom Search, Brave Search APIs with quota management
• 🔄 Auto-Rotation: Timestamped logs (weekly) and metrics (monthly) with auto-cleanup
• ⚡ Resilient: Automatic failover and comprehensive error handling

📦 Installation

Quick Start (Recommended)

# Install uv
brew install uv

# Run directly - no setup needed
uvx --from git+https://github.com/vishalkg/web-search websearch-server

Development

git clone https://github.com/vishalkg/web-search.git
cd web-search
uv pip install -e .

⚙️ Configuration

API Keys (Optional but Recommended)

For best results, configure API keys for Google Custom Search and Brave Search. Without API keys, the server falls back to web scraping which is less reliable.

Get API Keys:

• Google: Custom Search API
• Brave: Brave Search API

Q CLI

# Add to Q CLI with API keys
q mcp add --name websearch --command "uvx --from git+https://github.com/vishalkg/web-search websearch-server"

# Then edit ~/.aws/amazonq/mcp.json to add API keys in the env section:
{
  "websearch": {
    "command": "/opt/homebrew/bin/uvx",
    "args": ["--from", "git+https://github.com/vishalkg/web-search", "websearch-server"]
    "env": {
      "GOOGLE_CSE_API_KEY": "your-google-api-key",
      "GOOGLE_CSE_ID": "your-search-engine-id",
      "BRAVE_SEARCH_API_KEY": "your-brave-api-key"
    }
  }
}

Test

q chat "search for python tutorials"

Claude Desktop

Add to your MCP settings file with API keys:

{
  "mcpServers": {
    "websearch": {
      "command": "uvx",
      "args": ["--from", "git+https://github.com/vishalkg/web-search", "websearch-server"],
      "env": {
        "GOOGLE_CSE_API_KEY": "your-google-api-key",
        "GOOGLE_CSE_ID": "your-search-engine-id",
        "BRAVE_SEARCH_API_KEY": "your-brave-api-key"
      }
    }
  }
}

🗂️ File Structure

The server automatically manages files in OS-appropriate locations:

macOS:

~/Library/Application Support/websearch/  # Data
~/Library/Logs/websearch/                 # Logs
~/Library/Application Support/websearch/  # Config

Linux:

~/.local/share/websearch/    # Data
~/.local/state/websearch/    # Logs
~/.config/websearch/         # Config

Files:

data/
├── search-metrics.jsonl     # Search analytics (auto-rotated)
└── quota/
    └── quotas.json          # API quota tracking
logs/
└── web-search.log           # Application logs (auto-rotated)
config/
└── .env                     # Configuration file
└── cache/                  # Optional caching

Environment Variable Overrides

• WEBSEARCH_HOME: Base directory (default: ~/.websearch)
• WEBSEARCH_CONFIG_DIR: Config directory override
• WEBSEARCH_LOG_DIR: Log directory override

🔧 Usage

The server provides two main tools with multiple search modes:

Search Web

# Standard 5-engine search (backward compatible)
search_web("quantum computing applications", num_results=10)

# New 3-engine fallback search (optimized)
search_web_fallback("machine learning tutorials", num_results=5)

Search Engines:

• Google Custom Search API (with Startpage fallback)
• Bing (with DuckDuckGo fallback)
• Brave Search API (standalone)
• DuckDuckGo (scraping)
• Startpage (scraping)

Fetch Page Content

# Extract clean text from URLs
fetch_page_content("https://example.com")
fetch_page_content(["https://site1.com", "https://site2.com"])  # Batch processing

🏗️ Architecture

websearch/
├── core/
│   ├── search.py              # Sync search orchestration
│   ├── async_search.py        # Async search orchestration
│   ├── fallback_search.py     # 3-engine fallback system
│   ├── async_fallback_search.py # Async fallback system
│   ├── ranking.py             # Quality-first result ranking
│   └── common.py              # Shared utilities
├── engines/
│   ├── google_api.py          # Google Custom Search API
│   ├── brave_api.py           # Brave Search API
│   ├── bing.py                # Bing scraping
│   ├── duckduckgo.py          # DuckDuckGo scraping
│   └── startpage.py           # Startpage scraping
├── utils/
│   ├── unified_quota.py       # Unified API quota management
│   ├── deduplication.py       # Result deduplication
│   ├── advanced_cache.py      # Enhanced caching system
│   └── http.py                # HTTP utilities
└── server.py                  # FastMCP server

🔧 Advanced Configuration

Environment Variables

# API Configuration
export GOOGLE_CSE_API_KEY=your_google_api_key
export GOOGLE_CSE_ID=your_google_cse_id
export BRAVE_SEARCH_API_KEY=your_brave_api_key

# Quota Management (Optional)
export GOOGLE_DAILY_QUOTA=100        # Default: 100 requests/day
export BRAVE_MONTHLY_QUOTA=2000      # Default: 2000 requests/month

# Performance Tuning
export WEBSEARCH_CACHE_SIZE=1000
export WEBSEARCH_TIMEOUT=10
export WEBSEARCH_LOG_LEVEL=INFO

How to Get API Keys

Google Custom Search API

1. API Key: Go to https://developers.google.com/custom-search/v1/introduction and click "Get a Key"
2. CSE ID: Go to https://cse.google.com/cse/ and follow prompts to create a search engine

Brave Search API

1. Go to Brave Search API
2. Sign up for a free account
3. Go to your dashboard
4. Copy the API key as BRAVE_API_KEY
5. Free tier: 2000 requests/month

Quota Management

• Unified System: Single quota manager for all APIs
• Google: Daily quota (default 100 requests/day)
• Brave: Monthly quota (default 2000 requests/month)
• Storage: Quota files stored in ~/.websearch/ directory
• Auto-reset: Quotas automatically reset at period boundaries
• Fallback: Automatic fallback to scraping when quotas exhausted

Search Modes

• Standard Mode: Uses all 5 engines for maximum coverage
• Fallback Mode: Uses 3 engines with intelligent fallbacks for efficiency
• API-First Mode: Prioritizes API calls over scraping when keys available

🐛 Troubleshooting

Debug Mode

# Enable detailed logging
export WEBSEARCH_LOG_LEVEL=DEBUG
python -m websearch.server

API Status Check

# Test API connectivity
cd debug/
python test_brave_api.py      # Test Brave API
python test_fallback.py       # Test fallback system

📈 Performance & Monitoring

Metrics

• Pylint Score: 10.00/10 (perfect code quality)
• Search Speed: ~2-3 seconds for 5-engine search
• Fallback Speed: ~1-2 seconds for 3-engine search
• Cache Hit Rate: ~85% for repeated queries
• API Quota Efficiency: Automatic fallback prevents service interruption

Monitoring

Logs are written to web-search.log with structured format:

tail -f web-search.log | grep "search completed"

🔒 Security

• No hardcoded secrets: All API keys via environment variables
• Clean git history: Secrets scrubbed from all commits
• Input validation: Comprehensive sanitization of search queries
• Rate limiting: Built-in quota management for API calls
• Secure defaults: HTTPS-only requests, timeout protection

🚀 Performance Tips

1. Use fallback mode for faster searches when you don't need maximum coverage
2. Set API keys to reduce reliance on scraping (faster + more reliable)
3. Enable caching for repeated queries (enabled by default)
4. Tune batch sizes for content extraction based on your needs

🤝 Contributing

1. Fork the repository
2. Create feature branch (git checkout -b feature/amazing-feature)
3. Run tests (pytest)
4. Commit changes (git commit -m 'Add amazing feature')
5. Push to branch (git push origin feature/amazing-feature)
6. Open Pull Request

📄 License

MIT License - see file for details.