The Google Scholar MCP Server provides a bridge between AI assistants and Google Scholar through the Model Context Protocol (MCP). It exposes three tools that let your AI agent search for scholarly papers, perform advanced queries, and fetch author information, enabling research workflows inside your assistant without leaving the chat interface. Tools include: search_google_scholar_key_words for keyword-based searches, search_google_scholar_advanced for refined queries with authors and year ranges, and get_author_info to pull details about a specific author. To use these tools, your agent will invoke mcp.use_tool with the tool name and a JSON payload containing the required parameters, and it will receive structured results such as paper metadata or author details that you can present or summarize to users. You can configure and run the server locally and connect it to your MCP client as shown in the examples in the README.

Prerequisites:

• Python 3.10+ installed on your system
• pip available
• Git (optional, for cloning the repository)

Install steps:

1. Clone the repository: git clone https://github.com/JackKuo666/google-scholar-MCP-Server.git cd google-scholar-MCP-Server

2. (Optional) Create and activate a virtual environment: python -m venv venv

   macOS/Linux

   source venv/bin/activate

   Windows

   venv\Scripts\activate

3. Install dependencies: pip install -r requirements.txt

4. Run the MCP server: python google_scholar_server.py

If you prefer running as a module (as suggested in the README for MCP clients): python -m google_scholar_mcp_server

Note: Ensure any required environment variables or configuration files (if provided by your deployment) are set before starting the server.

Tips and common considerations:

• The server scrapes Google Scholar for results; respect Google Scholar's terms of service and implement appropriate request throttling in production to avoid triggering rate limits.
• Install and run in a virtual environment to isolate dependencies.
• Ensure Python 3.10+ and the required dependencies (as listed in requirements.txt) are installed to avoid compatibility issues.
• When integrating with an MCP client, use the exact tool names: search_google_scholar_key_words, search_google_scholar_advanced, and get_author_info, and structure your input payloads according to the parameter definitions in the README.
• If you customize the server path or module name, update the mcp_config accordingly to reflect the correct command and arguments.
• If you encounter connectivity or parsing issues, check the server logs forGoogle Scholar access errors or changes in the scholarly library versions.