# Academic 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.

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## Table of Contents

- [Overview](#overview)
- [Features](#features)
- [Installation](#installation)
  - [Quick Start](#quick-start)
  - [For Development](#for-development)
- [Usage](#usage)
  - [Search Papers](#1-search-papers-paper_search)
  - [Download Papers](#2-download-papers-paper_download)
  - [Read Papers](#3-read-papers-paper_read)
  - [Environment Variables](#environment-variables)
- [Contributing](#contributing)
- [Demo](#demo)
- [TODO](#todo)
- [License](#license)

---

## Overview

`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 path of downloaded files
- **`paper_read`**: Extract and read text content from papers

Built with FastMCP, it integrates seamlessly with LLM clients like Claude Desktop, making it ideal for researchers and AI-driven workflows.

## 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.

## 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:

1. **Install Package**:

   ```bash
   pip install academic-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": {
       "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:

1. **Setup Environment**:

   ```bash
   # 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
   ```

2. **Install Dependencies**:

   ```bash
   # 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:

```python
# 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:

```python
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:

```python
# 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:

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

2. **Clone and Set Up**:

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

3. **Make Changes**:

   - Add new platforms in `academic_mcp/sources/`.
   - 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
- [√] CrossRef
- [ ] 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.

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