All-in-MCP

# Google Scholar Integration This document describes the Google Scholar integration in the all-in-mcp server. ## Overview The Google Scholar integration provides academic paper searching capabilities through Google Scholar's web interface. Unlike other academic platforms, Google Scholar aggregates papers from multiple sources and provides citation counts and broader coverage of academic literature. ## Features - **Paper Search**: Search for academic papers across multiple disciplines - **Citation Information**: Get citation counts for papers - **Multiple Source Coverage**: Access papers from various publishers and repositories - **Year-based Filtering**: Filter search results by publication year range ## Available Tools ### search-google-scholar-papers Search for academic papers using Google Scholar. **Parameters:** - `query` (required): Search query string (e.g., "machine learning", "neural networks") - `max_results` (optional): Maximum number of papers to return (default: 10, max: 100) - `year_low` (optional): Minimum publication year for filtering results - `year_high` (optional): Maximum publication year for filtering results **Example Usage:** ```json { "query": "deep learning transformers", "max_results": 5, "year_low": 2020, "year_high": 2024 } ``` **Returns:** - Paper title - Authors list - Citation count (when available) - Publication year (when available) - Paper URL - Abstract (truncated to 300 characters) ## Limitations ### No Direct PDF Access Google Scholar doesn't provide direct PDF downloads. Users need to: 1. Use the provided paper URL to access the publisher's website 2. Check if the paper is available through institutional access 3. Look for open access versions on author websites or repositories ### Rate Limiting Google Scholar implements rate limiting to prevent automated scraping: - The implementation includes random delays between requests (1-3 seconds) - Multiple rapid requests may result in temporary blocks - Consider using other sources (IACR, arXiv) for bulk operations ### Search Result Variability - Results may vary based on geographic location and Google's algorithms - Some papers may not be accessible due to publisher restrictions - Citation counts may not be real-time accurate ## Implementation Details ### Web Scraping Approach The implementation uses web scraping with: - Random user agent rotation to avoid detection - BeautifulSoup for HTML parsing - Request session management for cookie handling - Error handling for network issues and parsing failures ### Paper Data Extraction The parser extracts: - **Title**: From `h3.gs_rt` elements, cleaned of PDF/HTML markers - **Authors**: From `div.gs_a` elements, parsed from publication info - **Abstract**: From `div.gs_rs` elements when available - **Citations**: From citation links in `div.gs_fl` elements - **Year**: Extracted from publication information using regex patterns - **URL**: From title links to source papers ### Error Handling - Network timeouts (30 seconds) - HTTP error responses (rate limiting, server errors) - Parsing failures for malformed HTML - Missing required paper elements ## Best Practices ### Responsible Usage 1. **Respect Rate Limits**: Don't make too many requests in quick succession 2. **Cache Results**: Store search results locally to avoid repeated queries 3. **Use Appropriate Delays**: The implementation includes built-in delays 4. **Monitor for Blocks**: Be prepared to handle temporary access restrictions ### Query Optimization 1. **Specific Terms**: Use specific academic terms for better results 2. **Author Names**: Include author names when searching for specific papers 3. **Publication Venues**: Include conference or journal names for focused searches 4. **Year Ranges**: Use year filters to narrow down results ### Integration with Other Sources Google Scholar works best when combined with other academic sources: - Use IACR for cryptography papers with PDF access - Use arXiv for preprints with full-text access - Use institutional repositories for open access papers ## Example Responses ### Successful Search ``` Found 3 Google Scholar papers for query 'machine learning healthcare': 1. **Machine Learning in Healthcare: A Review** - Authors: John Smith, Jane Doe, Bob Johnson - Citations: 245 - Year: 2023 - URL: https://example.com/paper1 - Abstract: This comprehensive review examines the applications of machine learning techniques in healthcare settings, covering diagnostic imaging, predictive analytics, and treatment optimization... 2. **Deep Learning for Medical Diagnosis** - Authors: Alice Brown, Charlie Wilson - Citations: 156 - Year: 2022 - URL: https://example.com/paper2 - Abstract: We present a novel deep learning framework for automated medical diagnosis using convolutional neural networks... ``` ### Empty Results ``` No papers found for query: obscure search term in year range (2025-2030) ``` ### Error Response ``` Error searching Google Scholar: HTTP 429 - Rate limit exceeded. Please try again later. ``` ## Troubleshooting ### Common Issues **No Results Found** - Check query spelling and syntax - Try broader search terms - Remove year filters if too restrictive - Verify network connectivity **Rate Limiting Errors** - Wait before making additional requests - Reduce the frequency of searches - Consider using other academic sources - Check if IP address is temporarily blocked **Parsing Errors** - Usually indicates changes in Google Scholar's HTML structure - Check logs for specific parsing failures - May require updates to the parsing logic ### Development and Testing **Testing Considerations** - Use mock responses for unit tests to avoid rate limiting - Test with various query types and edge cases - Include tests for error conditions and edge cases - Verify handling of malformed or incomplete results **Debugging Tips** - Enable detailed logging to see request/response details - Check network connectivity and DNS resolution - Verify user agent and headers are being sent correctly - Test individual parsing functions with real HTML samples ## Future Enhancements ### Potential Improvements 1. **Enhanced Parsing**: Better extraction of publication venues and DOIs 2. **Citation Tracking**: Track citation networks and related papers 3. **Advanced Filtering**: Filter by publication type, author affiliation 4. **Result Caching**: Implement intelligent caching to reduce API calls 5. **Proxy Support**: Add proxy rotation for high-volume usage ### Integration Opportunities 1. **Cross-Reference Validation**: Verify results against other academic databases 2. **Full-Text Integration**: Combine with repository APIs for full-text access 3. **Citation Analysis**: Integrate with citation analysis tools 4. **Recommendation System**: Suggest related papers based on search history