Google Scholar MCP Server
@shiwenbin1617
About Google Scholar MCP Server
No overview available yet
Config
Add this server to your MCP-compatible client using the configuration below.
{
"mcpServers": {
"google-scholar-mcp-server-shiwenbin1617": {
"command": "npx",
"args": [
"-y",
"@smithery/cli@latest",
"install",
"@JackKuo666/google-scholar-mcp-server",
"--client",
"claude",
"--config",
"{}"
]
}
}
}Tools
No tools detected
Fetch the live tool list by running this server in a temporary sandbox using the button above.
Overview
What is Google Scholar MCP Server?
Google Scholar MCP Server bridges AI assistants and Google Scholar through the Model Context Protocol (MCP). It enables AI models to search for academic papers and access paper metadata, author information, and advanced search results programmatically.
How to use Google Scholar MCP Server?
Install Python 3.10+, dependencies (pip install -r requirements.txt), then run python google_scholar_server.py. The server exposes three MCP tools: search_google_scholar_key_words, search_google_scholar_advanced, and get_author_info. Add the server configuration to Claude Desktop, Cursor, Windsurf, or Cline MCP settings.
Key features of Google Scholar MCP Server
- Search Google Scholar papers by keywords
- Perform advanced searches with author and year filters
- Retrieve detailed author information
- Fast access to paper metadata
- Easy integration with AI assistants via MCP
- Supports Claude Desktop, Cursor, Windsurf, and Cline
Use cases of Google Scholar MCP Server
- Academic literature reviews and research support
- Building AI‑powered research assistants
- Automating paper retrieval for data analysis
- Enabling chatbots to answer questions about scholarly articles
FAQ from Google Scholar MCP Server
What does Google Scholar MCP Server do compared to alternatives?
It provides a programmatic MCP interface to Google Scholar, offering search, advanced search, and author lookup tools directly for AI assistants, without requiring a separate API key.
What are the runtime dependencies?
Python 3.10 or later, and packages mcp[cli], scholarly, and asyncio. Install via pip install -r requirements.txt.
Where does the data come from and reside?
Data is fetched live from Google Scholar via web scraping using the scholarly library; no local storage is used.
What are the known limitations?
The server relies on web scraping and respects Google Scholar’s terms of service. The number of results per search can be limited by passing a num_results parameter (default 5). No authentication mechanism is provided.
How is the server transported and authenticated?
The server communicates over stdio (standard I/O) as a standard MCP server. No authentication or external transport configuration is required.
Frequently asked questions
What does Google Scholar MCP Server do compared to alternatives?
It provides a programmatic MCP interface to Google Scholar, offering search, advanced search, and author lookup tools directly for AI assistants, without requiring a separate API key.
What are the runtime dependencies?
Python 3.10 or later, and packages `mcp[cli]`, `scholarly`, and `asyncio`. Install via `pip install -r requirements.txt`.
Where does the data come from and reside?
Data is fetched live from Google Scholar via web scraping using the `scholarly` library; no local storage is used.
What are the known limitations?
The server relies on web scraping and respects Google Scholar’s terms of service. The number of results per search can be limited by passing a `num_results` parameter (default 5). No authentication mechanism is provided.
How is the server transported and authenticated?
The server communicates over stdio (standard I/O) as a standard MCP server. No authentication or external transport configuration is required.
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