Paper Search MCP

Name: Paper Search Openai
Author: smithery

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.

Overview
Features
Installation
- Quick Start
  - Install Package
  - Configure Claude Desktop
- For Development
  - Setup Environment
  - Install Dependencies
Contributing
Demo
License
TODO

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:

Install Package:
bash
```
uv add paper-search-mcp
```

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:

Setup Environment:

bash

# 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

Install Dependencies:

bash

# 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:

Fork the Repository: Click "Fork" on GitHub.

Clone and Set Up:

bash

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)

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