Summary

This project provides a Model Context Protocol (MCP) server enabling AI assistants to programmatically search and access papers from Google Scholar. It benefits researchers and developers by offering a structured interface for academic paper discovery and retrieval, facilitating AI-driven research workflows.

How It Works

The server implements the MCP, acting as a bridge between AI models and Google Scholar. It leverages Python libraries like scholarly for web scraping Google Scholar, exposing distinct tools for keyword searches, advanced queries with parameters like author and year range, and retrieving author metadata. This approach allows AI agents to integrate academic search capabilities directly into their workflows.

Quick Start & Requirements

Installation can be done manually via git clone and pip install -r requirements.txt, or automatically using the npx @smithery/cli tool for various clients (Claude Desktop, Cursor, Windsurf, CLine). Prerequisites include Python 3.10+, mcp[cli]>=1.4.1, and scholarly>=1.7.0. Specific configuration snippets are provided for integrating with clients like Claude Desktop.

Highlighted Details

• Offers three core MCP tools: search_google_scholar_key_words, search_google_scholar_advanced, and get_author_info.
• Supports advanced search parameters including author name and publication year range.
• Provides clear Python usage examples for AI assistant integration.
• Includes dedicated setup instructions for popular AI development environments.

Maintenance & Community

Contributions are welcomed via pull requests. No specific community channels, roadmap, or notable contributors are detailed in the README.

Licensing & Compatibility

The project is licensed under the MIT License. A disclaimer states the tool is for research purposes only, and users must adhere to Google Scholar's terms of service.

Limitations & Caveats

The tool is explicitly designated for research purposes only. Users are responsible for complying with Google Scholar's terms of service. Web scraping-based tools are inherently susceptible to breakage if Google Scholar modifies its website structure.