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Google Scholar MCP Server

@shiwenbin1617

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基本情報

カテゴリ

その他

トランスポート

stdio

作者

shiwenbin1617

Google Scholar MCP Server について

概要はまだありません

Google Scholar MCP Server の設定

以下の設定を使って、このサーバーを MCP 対応クライアントに追加してください。

{
  "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 のツール

ツール一覧はまだありません

この掲載には表示できるツール一覧がまだありません。サーバーにツールがないという意味ではありません。対応機能はプロジェクトのドキュメントをご確認ください。

上のボタンで一時的なサンドボックスでこのサーバーを実行し、ツール一覧を取得できます。

概要

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

よくある質問

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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