📚 Academic MCP

🔬 academic-mcp is a Python-based MCP server that enables users to search, download, and read academic papers from various platforms. It provides three main tools:

🔎 paper_search: Search papers across multiple academic databases
📥 paper_download: Download paper PDFs, return paths of downloaded files
📖 paper_read: Extract and read text content from papers

PyPI

📑 Table of Contents

🎬 Screenshot
📝 TODO
✨ Features
📦 Installation
- ⚡ Quick Start
- 🛠️ For Development
🚀 Usage
🤝 Contributing
📄 License

✨ Features

🌐 Multi-Source Support: Search and download papers from arXiv, PubMed, bioRxiv, medRxiv, Google Scholar, IACR ePrint Archive, Semantic Scholar, and CrossRef.
🎯 Unified Interface: All platforms accessible through consistent paper_search, paper_download, and paper_read tools.
📊 Standardized Output: Papers are returned in a consistent dictionary format via the Paper class.
⚡ Asynchronous Operations: Efficiently handles concurrent searches and downloads using httpx and async/await.
🔌 MCP Integration: Compatible with MCP clients for LLM context enhancement.
🧩 Extensible Design: Easily add new academic platforms by extending the sources module.

🎬 Screenshot

📝 TODO

Planned Academic Platforms

📦 Installation

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

⚡ Quick Start

For users who want to quickly run the server:

Install Package:
```
pip install academic-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):
```
{
  "mcpServers": {
    "academic-mcp": {
      "command": "python",
      "args": [
        "-m",
        "academic_mcp"
      ],
      "env": {
        "SEMANTIC_SCHOLAR_API_KEY": "",
        "ACADEMIC_MCP_DOWNLOAD_PATH": "./downloads"
      }
    }
  }
}
```
Note: The SEMANTIC_SCHOLAR_API_KEY is optional and only required for enhanced Semantic Scholar features.

🛠️ For Development

For developers who want to modify the code or contribute:

Setup Environment:

# Install uv if not installed
curl -LsSf https://astral.sh/uv/install.sh | sh

# Clone repository
git clone https://github.com/LinXueyuanStdio/academic-mcp.git
cd academic-mcp

# Create and activate virtual environment
uv venv
source .venv/bin/activate  # On Windows: .venv\Scripts\activate

Install Dependencies:

# Install dependencies (recommended)
uv pip install -e .

# Add development dependencies (optional)
uv pip install pytest flake8

🚀 Usage

Once configured, academic-mcp provides three main tools accessible through Claude Desktop or any MCP-compatible client:

1. Search Papers (`paper_search`)

Search for academic papers across multiple sources:

# Search arXiv for machine learning papers
paper_search([
    {"searcher": "arxiv", "query": "machine learning", "max_results": 5}
])

# Search multiple platforms simultaneously
paper_search([
    {"searcher": "arxiv", "query": "deep learning", "max_results": 5},
    {"searcher": "pubmed", "query": "cancer immunotherapy", "max_results": 3},
    {"searcher": "semantic", "query": "climate change", "max_results": 4, "year": "2020-2023"}
])

# Search all platforms (omit "searcher" parameter)
paper_search([
    {"query": "quantum computing", "max_results": 10}
])

2. Download Papers (`paper_download`)

Download paper PDFs using their identifiers:

paper_download([
    {"searcher": "arxiv", "paper_id": "2106.12345"},
    {"searcher": "pubmed", "paper_id": "32790614"},
    {"searcher": "biorxiv", "paper_id": "10.1101/2020.01.01.123456"},
    {"searcher": "semantic", "paper_id": "DOI:10.18653/v1/N18-3011"}
])

3. Read Papers (`paper_read`)

Extract and read text content from papers:

# Read an arXiv paper
paper_read(searcher="arxiv", paper_id="2106.12345")

# Read a PubMed paper
paper_read(searcher="pubmed", paper_id="32790614")

# Read a Semantic Scholar paper
paper_read(searcher="semantic", paper_id="DOI:10.18653/v1/N18-3011")

Environment Variables

SEMANTIC_SCHOLAR_API_KEY: Optional API key for enhanced Semantic Scholar features
ACADEMIC_MCP_DOWNLOAD_PATH: Directory for downloaded PDFs (default: ./downloads)

🤝 Contributing

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

Fork the Repository: Click "Fork" on GitHub.

Clone and Set Up:

git clone https://github.com/yourusername/academic-mcp.git
cd academic-mcp
uv pip install -e .  # Install in development mode

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

📄 License

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

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

academic-mcp