What is Google Scholar MCP Server?
The Google Scholar MCP Server is a tool that enables AI assistants to search and access academic papers from Google Scholar through a simple Model Context Protocol (MCP) interface.
How to use Google Scholar MCP Server?
To use the Google Scholar MCP Server, install it via Smithery or clone the repository from GitHub. After installation, start the server and use the provided MCP tools to search for papers or retrieve author information.
Key features of Google Scholar MCP Server?
- 🔎 Paper Search: Query Google Scholar papers with custom search strings or advanced search parameters.
- 🚀 Efficient Retrieval: Fast access to paper metadata.
- 👤 Author Information: Retrieve detailed information about authors.
- 📊 Research Support: Facilitate academic research and analysis.
Use cases of Google Scholar MCP Server?
- Searching for academic papers on specific topics.
- Performing advanced searches by author or publication year.
- Retrieving detailed author information for research purposes.
FAQ from Google Scholar MCP Server?
- Can I use this server for all academic fields?
Yes! The server can search across various academic disciplines available on Google Scholar.
- Is there a limit to the number of results I can retrieve?
By default, the server returns 5 results, but you can specify a different number in your queries.
- Is the Google Scholar MCP Server free to use?
Yes! The server is open-source and free for everyone to use.
Google Scholar MCP Server
🔍 Enable AI assistants to search and access Google Scholar papers through a simple MCP interface.
The Google Scholar MCP Server provides a bridge between AI assistants and Google Scholar through the Model Context Protocol (MCP). It allows AI models to search for academic papers and access their content in a programmatic way.
✨ Core Features
- 🔎 Paper Search: Query Google Scholar papers with custom search strings or advanced search parameters ✅
- 🚀 Efficient Retrieval: Fast access to paper metadata ✅
- 👤 Author Information: Retrieve detailed information about authors ✅
- 📊 Research Support: Facilitate academic research and analysis ✅
🚀 Quick Start
Installing Manually
Installing via Smithery
To install google-scholar Server for Claude Desktop automatically via Smithery:
claude
npx -y @smithery/cli@latest install @JackKuo666/google-scholar-mcp-server --client claude --config "{}"
Cursor
Paste the following into Settings → Cursor Settings → MCP → Add new server:
- Mac/Linux
npx -y @smithery/cli@latest run @JackKuo666/google-scholar-mcp-server --client cursor --config "{}"
Windsurf
npx -y @smithery/cli@latest install @JackKuo666/google-scholar-mcp-server --client windsurf --config "{}"
CLine
npx -y @smithery/cli@latest install @JackKuo666/google-scholar-mcp-server --client cline --config "{}"
-
Clone the repository:
git clone https://github.com/JackKuo666/google-scholar-MCP-Server.git cd google-scholar-MCP-Server
-
Install the required dependencies:
pip install -r requirements.txt
For development:
# Clone and set up development environment
git clone https://github.com/JackKuo666/Google-Scholar-MCP-Server.git
cd Google-Scholar-MCP-Server
# Create and activate virtual environment
python -m venv venv
source venv/bin/activate # On Windows use `venv\Scripts\activate`
# Install dependencies
pip install -r requirements.txt
📊 Usage
Start the MCP server:
python google_scholar_server.py
Once the server is running, you can use the provided MCP tools in your AI assistant or application. Here are some examples of how to use the tools:
Example 1: Search for papers using keywords
result = await mcp.use_tool("search_google_scholar_key_words", {
"query": "artificial intelligence ethics",
"num_results": 5
})
print(result)
Example 2: Perform an advanced search
result = await mcp.use_tool("search_google_scholar_advanced", {
"query": "machine learning",
"author": "Hinton",
"year_range": [2020, 2023],
"num_results": 3
})
print(result)
Example 3: Get author information
result = await mcp.use_tool("get_author_info", {
"author_name": "Geoffrey Hinton"
})
print(result)
These examples demonstrate how to use the three main tools provided by the Google Scholar MCP Server. Adjust the parameters as needed for your specific use case.
Usage with Claude Desktop
Add this configuration to your claude_desktop_config.json
:
(Mac OS)
{
"mcpServers": {
"google-scholar": {
"command": "python",
"args": ["-m", "google_scholar_mcp_server"]
}
}
}
(Windows version):
{
"mcpServers": {
"google-scholar": {
"command": "C:\\Users\\YOUR\\PATH\\miniconda3\\envs\\mcp_server\\python.exe",
"args": [
"D:\\code\\YOUR\\PATH\\Google-Scholar-MCP-Server\\google_scholar_server.py"
],
"env": {},
"disabled": false,
"autoApprove": []
}
}
}
Using with Cline
{
"mcpServers": {
"google-scholar": {
"command": "bash",
"args": [
"-c",
"source /home/YOUR/PATH/.venv/bin/activate && python /home/YOUR/PATH/google_scholar_mcp_server.py"
],
"env": {},
"disabled": false,
"autoApprove": []
}
}
}
🛠 MCP Tools
The Google Scholar MCP Server provides the following tools:
search_google_scholar_key_words
Search for articles on Google Scholar using key words.
Parameters:
query
(str): Search query stringnum_results
(int, optional): Number of results to return (default: 5)
Returns: List of dictionaries containing article information
search_google_scholar_advanced
Perform an advanced search for articles on Google Scholar.
Parameters:
query
(str): General search queryauthor
(str, optional): Author nameyear_range
(tuple, optional): Tuple containing (start_year, end_year)num_results
(int, optional): Number of results to return (default: 5)
Returns: List of dictionaries containing article information
get_author_info
Get detailed information about an author from Google Scholar.
Parameters:
author_name
(str): Name of the author to search for
Returns: Dictionary containing author information
📁 Project Structure
google_scholar_server.py
: The main MCP server implementation using FastMCPgoogle_scholar_web_search.py
: Contains the web scraping logic for searching Google Scholar
🔧 Dependencies
- Python 3.10+
- mcp[cli]>=1.4.1
- scholarly>=1.7.0
- asyncio>=3.4.3
You can install the required dependencies using:
pip install -r requirements.txt
🤝 Contributing
Contributions are welcome! Please feel free to submit a Pull Request.
📄 License
This project is licensed under the MIT License.
⚠️ Disclaimer
This tool is for research purposes only. Please respect Google Scholar's terms of service and use this tool responsibly.