Paper Search MCP — A Practical Guide for Researchers and Developers

Academic research often begins with a familiar challenge: finding reliable and up-to-date papers across multiple sources. Researchers may spend hours moving between platforms like arXiv, PubMed, or bioRxiv, only to repeat similar searches and manually organize results. Paper Search MCP was built to change this experience.

This guide offers a complete walkthrough of what Paper Search MCP is, what it can do, how to install and configure it, and how it fits into different research and development scenarios. The goal is simple: provide you with a clear, trustworthy, and practical resource that helps you get the most out of this tool.

Table of Contents

Introduction

Paper Search MCP is a Python-based server built on the Model Context Protocol (MCP). Its main purpose is to simplify the way researchers and developers interact with academic papers. Instead of searching different platforms separately, this tool provides a unified workflow for searching, retrieving, and downloading research papers.

The design is flexible:

  • It works as a standalone search server.
  • It integrates smoothly with AI assistants such as Claude Desktop.
  • It ensures that results are structured consistently, making automation and data processing much easier.

In other words, Paper Search MCP is more than a tool for downloading papers—it is a bridge between academic content and intelligent research workflows.

Core Features

Paper Search MCP combines a set of practical features that target real-world needs in research environments:

  1. Multi-source support

    • Search across platforms such as arXiv, PubMed, bioRxiv, medRxiv, Google Scholar, IACR ePrint Archive, and Semantic Scholar.
    • Plans include extending support to additional platforms like Springer Link, IEEE Xplore, and ACM Digital Library.

  2. Standardized results

    • All results are returned in a consistent dictionary format via the Paper class.
    • This makes integration with other systems straightforward and reduces the need for custom parsing.

  3. Asynchronous operations

    • Built with httpx, Paper Search MCP processes multiple requests efficiently.
    • This reduces waiting time when running large searches.

  4. Seamless MCP integration

    • Designed to connect with MCP-compatible clients, such as Claude Desktop.
    • Enables AI-driven assistants to provide direct academic search support.

  5. Extensible architecture

    • Developers can add new academic sources by extending the academic_platforms module.
    • The modular design supports long-term growth and flexibility.

Installation Options

Paper Search MCP can be installed in two main ways: a quick start approach for users who want to run the tool immediately, and a development setup for contributors who plan to modify or extend its functionality.

Quick Start

For users who simply want to use Paper Search MCP without modification, installation is straightforward.

Step 1: Install the package

uv add paper-search-mcp

Step 2: Configure Claude Desktop

Add the following configuration into your Claude Desktop settings file:

  • macOS: ~/Library/Application Support/Claude/claude_desktop_config.json
  • Windows: %APPDATA%\Claude\claude_desktop_config.json

Example configuration:

{
  "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": ""
      }
    }
  }
}

Replace /path/to/your/paper-search-mcp with the actual installation path.
SEMANTIC_SCHOLAR_API_KEY is optional and enhances Semantic Scholar search features.

Development Setup

For developers who plan to contribute or customize the project, a more detailed setup is required.

Step 1: Install uv

curl -LsSf https://astral.sh/uv/install.sh | sh

Step 2: Clone the repository

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

Step 3: Create and activate a virtual environment

uv venv
source .venv/bin/activate   # On Windows: .venv\Scripts\activate

Step 4: Install dependencies

uv add -e .
uv add pytest flake8   # Optional: for testing and linting

With this setup, developers can modify the source code, add new platforms, or extend testing.

Configuration for Claude Desktop

Claude Desktop users can integrate Paper Search MCP as a backend service. Once installed and configured, the client will be able to:

  • Call the MCP server automatically.
  • Run academic searches within conversation workflows.
  • Access papers across multiple platforms without manual switching.

This integration is particularly useful for researchers who rely on AI-assisted literature reviews.

Practical Use Cases

Paper Search MCP supports a wide range of scenarios. Here are a few examples:

  • Literature review preparation
    Quickly gather papers across arXiv and PubMed to form the basis of a research survey.

  • Thesis or dissertation research
    Use the tool to collect papers relevant to your topic, ensuring consistent formatting and easy citation management.

  • AI-assisted study sessions
    Ask questions through Claude Desktop, such as “What are the latest papers on neural network interpretability?” and get immediate results.

  • Research collaboration
    Teams can standardize their paper collection process by sharing MCP-based workflows.

Contribution Guide

Paper Search MCP is open source, and contributions are encouraged. Developers can help in several ways:

  1. Fork the repository
    Create a copy of the project on GitHub.

  2. Clone locally and set up

git clone https://github.com/yourusername/paper-search-mcp.git
cd paper-search-mcp
pip install -e ".[dev]"

  1. Make changes

    • Add new academic platforms under academic_platforms/.
    • Update tests under tests/.

  2. Submit a pull request
    Share your improvements with the community through GitHub.

Demo Overview

A demo is available to show how Paper Search MCP works in practice:

demo

The interface demonstrates searching and downloading papers in real time, offering a clear view of its workflow.

Future Roadmap

The project already supports key platforms such as:

  • ✅ arXiv
  • ✅ PubMed
  • ✅ bioRxiv
  • ✅ medRxiv
  • ✅ Google Scholar
  • ✅ IACR ePrint Archive
  • ✅ Semantic Scholar

Planned platforms include:

  • PubMed Central (PMC)
  • Science Direct
  • Springer Link
  • IEEE Xplore
  • ACM Digital Library
  • Web of Science
  • Scopus
  • JSTOR
  • ResearchGate
  • CORE
  • Microsoft Academic

This roadmap shows the intent to cover nearly all major academic databases, aiming for a truly unified search experience.

Frequently Asked Questions

1. Is Paper Search MCP free to use?
Yes, it is open source under the MIT License.

2. Do I need Claude Desktop to use it?
No. It can run as a standalone service, though Claude integration offers additional convenience.

3. Can it download PDFs directly?
Yes. Tools like download_arxiv allow you to download PDFs after a search.

4. Can I add my own platforms?
Yes. By extending the academic_platforms module, you can integrate new academic sources.

5. Do I need an API key?
Some platforms, like Semantic Scholar, offer optional API keys for enhanced functionality.

Conclusion

Paper Search MCP is designed for researchers and developers who want efficiency, consistency, and extensibility in their academic workflows. By combining multi-source searches, standardized results, and AI integration, it provides a foundation for smarter and faster research.

Whether you are preparing a literature review, building an academic tool, or using AI for guided research, this project delivers long-term value. With planned support for more platforms, Paper Search MCP is positioned to become a central part of the modern research process.