# PaperMCP 智能学术论文检索系统
[](https://smithery.ai/server/@guangxiangdebizi/PaperMCP)
欢迎使用 **PaperMCP 智能学术论文检索系统**！这是一个基于 Model Context Protocol (MCP) 的高级学术论文搜索服务器，专为研究员和教授设计。通过 **OpenAlex API** 和智能算法，为AI助手提供精准的学术文献检索能力，大幅提升科研效率。
## 🌟 Features
### 📚 Comprehensive Paper Search
Search academic papers with flexible filtering options:
* **Keyword Search** - Find papers by title, abstract, or full-text content
* **Country Filter** - Limit results to papers from specific countries (CN, US, GB, etc.)
* **Year Filter** - Search papers from specific publication years
* **Result Limit** - Control the number of results (up to 50 papers)
* **Sort Options** - Sort by citation count, publication date, or relevance
* **Open Access Filter** - Find only freely accessible papers
### 📊 Rich Paper Information
Get comprehensive details for each paper:
* **Basic Info** - Title, authors, publication year, document type
* **Abstract** - Full abstract text with intelligent reconstruction from inverted index
* **Publication Details** - Journal/venue, DOI, URLs
* **Citation Data** - Citation count and related works
* **Institutional Info** - Author affiliations and institutions
* **Subject Classification** - Topics, subfields, fields, and domains
* **Open Access Status** - OA status and APC (Article Processing Charge) information
### 🔍 Advanced Filtering
* **Institution-based Filtering** - Find papers from specific countries' institutions
* **Temporal Filtering** - Search within specific publication years
* **Access-based Filtering** - Filter by open access availability
* **Quality Indicators** - Sort by citation impact or publication date
### 🤖 MCP Integration
Seamless integration with MCP-compatible clients (like Claude) for intelligent academic research
## 🚦 Requirements
Before getting started, please ensure you have:
1. **Node.js and npm**:
* Requires Node.js version >= 18
* Download and install from [nodejs.org](https://nodejs.org/)
2. **Email Address**:
* Provide a valid email address for OpenAlex API access
* OpenAlex requires an email for rate limiting and contact purposes
* No API key needed - OpenAlex is free to use!
## 🛠️ Installation & Setup
### Install via Smithery (Recommended)
If you're using Claude Desktop, you can quickly install via [Smithery](https://smithery.ai/server/@guangxiangdebizi/paper-mcp):
```bash
npx -y @smithery/cli install @guangxiangdebizi/paper-mcp --client claude
```
### Manual Installation
1. **Get the code**:
```bash
git clone https://github.com/guangxiangdebizi/PaperMCP.git
cd PaperMCP
```
2. **Install dependencies**:
```bash
npm install
```
3. **Configure Email Address**:
* Create a `.env` file in the project root directory
* Add the following content:
```
OPENALEX_EMAIL=your_email@example.com
```
* Or set it directly in the `src/config.ts` file
4. **Build the project**:
```bash
npm run build
```
## 🚀 Running the Server
There are two ways to start the server:
### Method 1: Using stdio mode (Direct run)
```bash
node build/index.js
```
### Method 2: Using Supergateway (Recommended for development)
```bash
npx supergateway --stdio "node build/index.js" --port 3100
```
## 📝 Configuring MCP Clients
To use this server in Claude or other MCP clients, you need the following configuration:
### Claude Configuration
Add the following to Claude's configuration file:
```json
{
"mcpServers": {
"paper-search-server": {
"url": "http://localhost:3100/sse", // If using Supergateway
"type": "sse",
"disabled": false,
"autoApprove": [
"paper_search"
]
}
}
}
```
If using stdio mode directly (without Supergateway), configure as follows:
```json
{
"mcpServers": {
"paper-search-server": {
"command": "C:/path/to/PaperMCP/build/index.js", // Modify to actual path
"type": "stdio",
"disabled": false,
"autoApprove": [
"paper_search"
]
}
}
}
```
## 💡 Usage Examples
Here are some example queries using the PaperMCP server:
### 1. Basic Paper Search
You can ask Claude:
**General Search:**
> "Search for papers about machine learning published in 2024"
**Country-specific Search:**
> "Find papers about artificial intelligence from Chinese institutions in 2023"
**Author/Institution Focus:**
> "Search for papers about LLM from US universities in the last 2 years"
### 2. Advanced Filtering
**Citation-based Search:**
> "Find the most-cited papers about deep learning from 2022, limited to 20 results"
**Open Access Papers:**
> "Search for open access papers about natural language processing from 2024"
**Specific Year Range:**
> "Find papers about computer vision published in 2023, sorted by citation count"
### 3. Research-focused Queries
**Literature Review:**
> "Help me find recent papers about transformer architectures for my literature review"
**Trend Analysis:**
> "Search for papers about quantum computing from different countries to analyze research trends"
**Interdisciplinary Research:**
> "Find papers that combine AI and biology, focusing on recent publications"
### 4. Complex Research Queries
**Comparative Analysis:**
> "Compare recent AI research output between China and the US by finding papers from both countries in 2024"
**Field Evolution:**
> "Show me how research in reinforcement learning has evolved by finding papers from 2020-2024"
**Open Science Focus:**
> "Find highly-cited open access papers in machine learning to understand accessible research trends"
This will use the `paper_search` tool to retrieve comprehensive academic paper information.
## 📊 Supported Search Parameters
The PaperMCP server supports the following search parameters:
| Parameter | Type | Description | Example |
|-----------|------|-------------|---------|
| `query` | string | Search keywords (required) | "machine learning", "deep learning" |
| `country_code` | string | Filter by country code | "CN" (China), "US" (USA), "GB" (UK) |
| `year` | number | Filter by publication year | 2024, 2023 |
| `num_results` | number | Number of results (max 50) | 10, 20, 50 |
| `sort_by` | string | Sort method | "cited_by_count", "publication_date", "relevance_score" |
| `open_access` | boolean | Filter open access papers | true, false |
## 📈 Data Sources
This server uses the **OpenAlex API**, which provides:
- **260M+ papers** from across all disciplines
- **Real-time updates** with new publications
- **Comprehensive metadata** including citations, authors, institutions
- **Open access information** and APC data
- **Subject classification** at multiple levels
- **Institution and country data** for geographic analysis
## 🔮 Future Plans
Future enhancements may include:
1. **Author Search** - Find papers by specific authors
2. **Institution Search** - Search within specific institutions
3. **Journal/Venue Filtering** - Filter by publication venue
4. **Citation Network Analysis** - Explore citation relationships
5. **Concept-based Search** - Search by research concepts and topics
6. **Export Functionality** - Export results in various formats (BibTeX, etc.)
## 📄 License
This project is licensed under the MIT License. See the [LICENSE](LICENSE) file for details.
## 👨💻 Author
- Name: Xingyu_Chen
- Email: guangxiangdebizi@gmail.com
- GitHub: [guangxiangdebizi](https://github.com/guangxiangdebizi)
## 🙏 Acknowledgments
This project uses the [OpenAlex](https://openalex.org/) API, a free and open catalog of scholarly papers, authors, institutions, and more. Special thanks to the OpenAlex team for providing this invaluable resource to the research community.
