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Google Scholar MCP Server

@shiwenbin1617

代码仓库

基本信息

分类

其他

传输方式

stdio

作者

shiwenbin1617

关于 Google Scholar MCP Server

暂无概览

Google Scholar MCP Server 配置

使用下面的配置,将此服务器添加到你的 MCP 客户端。

{
  "mcpServers": {
    "google-scholar-mcp-server-shiwenbin1617": {
      "command": "npx",
      "args": [
        "-y",
        "@smithery/cli@latest",
        "install",
        "@JackKuo666/google-scholar-mcp-server",
        "--client",
        "claude",
        "--config",
        "{}"
      ]
    }
  }
}

Google Scholar MCP Server 工具

工具列表暂未提供

当前收录暂时没有可展示的工具列表，不代表服务没有工具。请查阅项目文档了解支持的能力。

点击上方按钮,在临时沙箱中运行此服务器并实时获取工具列表。

概览

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

常见问题

1

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.

2

What are the runtime dependencies?

Python 3.10 or later, and packages `mcp[cli]`, `scholarly`, and `asyncio`. Install via `pip install -r requirements.txt`.

3

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.

4

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.

5

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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