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MCP Server
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ai.smithery/adamamer20-paper-search-mcp-openai MCP Server

ai.smithery/adamamer20-paper-search-mcp-openai

Search and download academic papers from arXiv, PubMed, bioRxiv, and other sources for AI-driven research workflows.

What is the ai.smithery/adamamer20-paper-search-mcp-openai MCP server?

The Paper Search MCP server is a Python-based tool that enables searching and downloading academic papers from multiple platforms including arXiv, PubMed, bioRxiv, medRxiv, Google Scholar, IACR ePrint Archive, and Semantic Scholar. It provides standardized search and fetch tools compatible with Claude Desktop and other MCP clients, making it ideal for researchers and AI-driven workflows.

Paper Search MCP lets you search and download academic papers from seven major academic platforms through a single interface. It's designed for researchers, students, and AI agents who need to access papers programmatically, with support for standardized output formats and asynchronous network requests.

How to install ai.smithery/adamamer20-paper-search-mcp-openai

Copy-paste configuration for popular MCP clients.

transport: http
Config generated by PluginBench — verify against the source before use.
Environment / auth
  • Authorization
    required
    secret

    Bearer token for Smithery authentication

~/.cursor/mcp.json
{
  "mcpServers": {
    "adamamer20-paper-search-mcp-openai": {
      "url": "https://server.smithery.ai/@adamamer20/paper-search-mcp-openai/mcp"
    }
  }
}

Tools & capabilities

Tools this server exposes to the agent.

  • search_arxiv — Search for papers on arXiv
  • download_arxiv — Download PDFs from arXiv
  • search_pubmed — Search for papers on PubMed
  • search_biorxiv — Search for papers on bioRxiv
  • search_medrxiv — Search for papers on medRxiv
  • search_google_scholar — Search for papers on Google Scholar
  • search_semantic_scholar — Search for papers on Semantic Scholar
  • search_iacr — Search for papers on IACR ePrint Archive
  • search — Standardized search tool compatible with OpenAI Deep Research
  • fetch — Standardized fetch tool for retrieving paper details

Use cases

  • Search academic papers across multiple platforms from a single query
  • Download PDF papers from arXiv for offline reading and analysis
  • Integrate paper search into AI research workflows and Claude Desktop
  • Find papers on specific topics in biomedical research (PubMed, bioRxiv, medRxiv)
  • Support OpenAI Deep Research and ChatGPT connectors with standardized search/fetch tools

ai.smithery/adamamer20-paper-search-mcp-openai MCP server FAQ

What is the Paper Search MCP server?

It's an MCP server that searches and downloads academic papers from arXiv, PubMed, bioRxiv, medRxiv, Google Scholar, IACR ePrint Archive, and Semantic Scholar, designed for integration with Claude Desktop and other LLM clients.

Is it free to use?

Yes, the server itself is free and open-source (MIT License). Access to papers depends on the underlying platforms' policies; some papers are open-access, others may require institutional access.

How do I install it in Claude Desktop?

Install via Smithery with `npx -y @smithery/cli install @openags/paper-search-mcp --client claude`, or manually add the configuration to Claude's config file and run with `uv run -m paper_search_mcp.server`.

Do I need an API key?

Most sources work without authentication. Semantic Scholar optionally accepts an API key for enhanced features, which can be set via the `SEMANTIC_SCHOLAR_API_KEY` environment variable.

What academic sources does it support?

It supports arXiv, PubMed, bioRxiv, medRxiv, Google Scholar, IACR ePrint Archive, and Semantic Scholar, with plans to add PubMed Central, Science Direct, Springer Link, IEEE Xplore, and others.

Can I use this for development?

Yes, the project is designed to be extensible. You can fork the repository, set up a development environment with `uv venv`, and add new academic platforms by extending the `academic_platforms` module.

README (reference)

Source of truth, from the repository.

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:

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:

    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):

    {
      "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:

    # Install uv if not installed
    curl -LsSf https://astral.sh/uv/install.sh | sh
    
    # Clone repository
    git clone https://github.com/openags/paper-search-mcp.git
    cd paper-search-mcp
    
    # Create and activate virtual environment
    uv venv
    source .venv/bin/activate  # On Windows: .venv\Scripts\activate
    
  2. Install Dependencies:

    # Install project in editable mode
    uv 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:

    git clone https://github.com/yourusername/paper-search-mcp.git
    cd paper-search-mcp
    pip 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

<img src="docs\images\demo.png" alt="Demo" width="800">

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.

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