# π Academic MCP
[English](README.md) | [δΈζ](README_zh.md)
π¬ `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:
- π **`paper_search`**: Search papers across multiple academic databases
- π₯ **`paper_download`**: Download paper PDFs, return paths of downloaded files
- π **`paper_read`**: Extract and read text content from papers
  
---
## π Table of Contents
- [π¬ Screenshot](#-screenshot)
- [π TODO](#-todo)
- [β¨ Features](#-features)
- [π¦ Installation](#-installation)
- [β‘ Quick Start](#-quick-start)
- [π οΈ For Development](#οΈ-for-development)
- [π Usage](#-usage)
- [π Search Papers](#1-search-papers-paper_search)
- [π₯ Download Papers](#2-download-papers-paper_download)
- [π Read Papers](#3-read-papers-paper_read)
- [βοΈ Environment Variables](#οΈ-environment-variables)
- [π€ Contributing](#-contributing)
- [π License](#-license)
---
## β¨ Features
- π **Multi-Source Support**: Search and download papers from arXiv, PubMed, bioRxiv, medRxiv, Google Scholar, IACR ePrint Archive, Semantic Scholar, and CrossRef.
- π― **Unified Interface**: All platforms accessible through consistent `paper_search`, `paper_download`, and `paper_read` tools.
- π **Standardized Output**: Papers are returned in a consistent dictionary format via the `Paper` class.
- β‘ **Asynchronous Operations**: Efficiently handles concurrent searches and downloads using `httpx` and async/await.
- π **MCP Integration**: Compatible with MCP clients for LLM context enhancement.
- 𧩠**Extensible Design**: Easily add new academic platforms by extending the `sources` module.
## π¬ Screenshot
<img src="assets/screenshot.png" alt="Screenshot" width="800">
## π TODO
Planned Academic Platforms
- [x] arXiv
- [x] PubMed
- [x] bioRxiv
- [x] medRxiv
- [x] Google Scholar
- [x] IACR ePrint Archive
- [x] Semantic Scholar
- [x] CrossRef
- [ ] PubMed Central (PMC)
- [ ] Science Direct
- [ ] Springer Link
- [ ] IEEE Xplore
- [ ] ACM Digital Library
- [ ] Web of Science
- [ ] Scopus
- [ ] JSTOR
- [ ] ResearchGate
- [ ] CORE
- [ ] Microsoft Academic
## π¦ 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.
### β‘ 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.
### π οΈ 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
```
---
## π 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`)
---
## π€ 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.
## π 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.
