Adamamer20 Paper Search Mcp Openai

ai.smithery由 smithery.ai 發布官方發布者v1.14.0更新於 Oct 24, 2026

Search and download academic papers from arXiv, PubMed, bioRxiv, medRxiv, Google Scholar, Semantic…

發布方自己的服務:222wcnm Bilistalkermcp

已驗證Streamable HTTP可網頁執行網頁搜尋與抓取知識與記憶

概覽

AI 產生的概覽

讓助理在 arXiv、PubMed、bioRxiv、medRxiv、Google Scholar、IACR 與 Semantic Scholar 上搜尋並下載學術論文。

可以做什麼
  • 在 arXiv、PubMed、bioRxiv、medRxiv、Google Scholar、IACR ePrint 與 Semantic Scholar 上檢索學術論文
  • 從支援的來源下載論文 PDF
  • 以統一的論文格式回傳結果,方便放入大型語言模型上下文
  • 提供標準化的 search 與 fetch 工具,供 OpenAI Deep Research 與 ChatGPT 連接器使用
適用情境
適合需要助理查找文獻、蒐集參考資料或下載論文 PDF 的研究工作與 AI 驅動的文獻流程。
執行需求
透過 streamable HTTP 連線至 server.smithery.ai 的遠端端點,需 OAuth 驗證與 Authorization bearer 權杖。上游專案為 Python 套件,也可用 uv 或 pip 在本機執行;選用的 SEMANTIC_SCHOLAR_API_KEY 可啟用增強的 Semantic Scholar 功能。需要能連線至各論文來源的網路。
安裝前請注意
README 將 Sci-Hub 列為選用來源,可能涉及法律或授權問題,請確認啟用了哪些來源。搜尋查詢與下載請求會送往第三方學術網站。Authorization bearer 權杖屬於機密,不應外流。

安裝

在 SourceWeft 中

  1. 開啟 儀表板中的 Adamamer20 Paper Search Mcp Openai,將其新增到工作區。
  2. 連接 所需的 憑證。它們會被私密儲存,絕不會在公開頁面上展示。
  3. 為需要使用其工具的對話啟用該服務。

Web executable,透過 Streamable HTTP。 遠端服務在工作區中設定後即可從網頁執行環境執行。

交給代理程式安裝

把這段提示詞貼上到 Claude Code、Codex、Cursor 等代理程式,或 SourceWeft 對話中。代理程式會讀取該伺服器的安裝指南,先向你展示發布者、端點和登入方式,經你同意後再把它新增到你的客戶端。金鑰不會寫進任何命令。

閱讀 https://sourceweft.com/mcp/ai.smithery%2Fadamamer20-paper-search-mcp-openai/install.md,並按其中說明把這個 MCP 伺服器接入我的客戶端。修改任何設定之前,先告訴我它的發布者、端點和登入方式,等我確認。不要把 API 金鑰寫進命令或我會提交的檔案;問我金鑰應從哪裡取得。

檢視代理程式所依據的安裝指南

其他 MCP 客戶端

把它新增到你客戶端的 mcpServers 設定中。 客戶端會在首次連接時完成 OAuth 登入。

{
  "mcpServers": {
    "adamamer20-paper-search-openai": {
      "type": "http",
      "url": "https://server.smithery.ai/@adamamer20/paper-search-mcp-openai/mcp"
    }
  }
}

README

Paper Search MCP

A Model Context Protocol (MCP) server for searching and downloading academic papers from multiple sources, including arXiv, PubMed, bioRxiv, and Sci-Hub (optional). Designed for seamless integration with large language models like Claude Desktop.

[PyPI] [License] [Python] [smithery badge]


Table of Contents


Overview

paper-search-mcp is a Python-based MCP server that enables users to search and download academic papers from various platforms. It provides tools for searching papers (e.g., search_arxiv) and downloading PDFs (e.g., download_arxiv), making it ideal for researchers and AI-driven workflows. Built with the MCP Python SDK, it integrates seamlessly with LLM clients like Claude Desktop.


Features

  • Multi-Source Support: Search and download papers from arXiv, PubMed, bioRxiv, medRxiv, Google Scholar, IACR ePrint Archive, Semantic Scholar.
  • Deep Research Ready: Provides the standardized search and fetch tools required by OpenAI Deep Research and ChatGPT connectors.
  • Standardized Output: Papers are returned in a consistent dictionary format via the Paper class.
  • Asynchronous Tools: Efficiently handles network requests using httpx.
  • MCP Integration: Compatible with MCP clients for LLM context enhancement.
  • Extensible Design: Easily add new academic platforms by extending the academic_platforms module.

Installation

paper-search-mcp can be installed using uv or pip. Below are two approaches: a quick start for immediate use and a detailed setup for development.

Installing via Smithery

To install paper-search-mcp for Claude Desktop automatically via Smithery:

bash
npx -y @smithery/cli install @openags/paper-search-mcp --client claude

Quick Start

For users who want to quickly run the server:

  1. Install Package:

    bash
    uv add paper-search-mcp
  2. Configure Claude Desktop: Add this configuration to ~/Library/Application Support/Claude/claude_desktop_config.json (Mac) or %APPDATA%\Claude\claude_desktop_config.json (Windows):

    json
    {  "mcpServers": {    "paper_search_server": {      "command": "uv",      "args": [        "run",        "--directory",        "/path/to/your/paper-search-mcp",        "-m",        "paper_search_mcp.server"      ],      "env": {        "SEMANTIC_SCHOLAR_API_KEY": "" // Optional: For enhanced Semantic Scholar features      }    }  }}

    Note: Replace /path/to/your/paper-search-mcp with your actual installation path.

For Development

For developers who want to modify the code or contribute:

  1. Setup Environment:

    bash
    # Install uv if not installedcurl -LsSf https://astral.sh/uv/install.sh | sh
    # Clone repositorygit clone https://github.com/openags/paper-search-mcp.gitcd paper-search-mcp
    # Create and activate virtual environmentuv venvsource .venv/bin/activate  # On Windows: .venv\Scripts\activate
  2. Install Dependencies:

    bash
    # Install project in editable modeuv add -e .
    # Add development dependencies (optional)uv add pytest flake8

Contributing

We welcome contributions! Here's how to get started:

  1. Fork the Repository: Click "Fork" on GitHub.

  2. Clone and Set Up:

    bash
    git clone https://github.com/yourusername/paper-search-mcp.gitcd paper-search-mcppip install -e ".[dev]"  # Install dev dependencies (if added to pyproject.toml)
  3. Make Changes:

    • Add new platforms in academic_platforms/.
    • Update tests in tests/.
  4. Submit a Pull Request: Push changes and create a PR on GitHub.


Demo

[Demo]

TODO

Planned Academic Platforms

  • [√] arXiv
  • [√] PubMed
  • [√] bioRxiv
  • [√] medRxiv
  • [√] Google Scholar
  • [√] IACR ePrint Archive
  • [√] Semantic Scholar
  • PubMed Central (PMC)
  • Science Direct
  • Springer Link
  • IEEE Xplore
  • ACM Digital Library
  • Web of Science
  • Scopus
  • JSTOR
  • ResearchGate
  • CORE
  • Microsoft Academic

License

This project is licensed under the MIT License. See the LICENSE file for details.


Happy researching with paper-search-mcp! If you encounter issues, open a GitHub issue.

來源:README.md,提交 671f572

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版本歷史

1
  1. v1.14.0最新Sep 16, 2026

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