semantic-scholar-mcp
# Semantic Scholar MCP Server
一个基于FastMCP框架的Semantic Scholar学术搜索引擎工具,可以搜索学术论文并下载PDF文件。支持通过MCP协议与各种AI助手集成。
**GitHub仓库**: https://github.com/cktbarking/semanticMCP.git
## 功能特性
1. 🔍 **学术论文搜索** - 搜索Semantic Scholar数据库
2. 📄 **PDF下载** - 自动识别出版社并下载论文PDF
3. 🔗 **DOI解析** - 支持通过DOI获取论文信息
4. 🏢 **多出版社支持** - 支持IEEE、ACM、Elsevier、Springer、arXiv等
5. 🛠️ **MCP协议** - 兼容Model Context Protocol
## 快速开始
### 使用uvx运行(推荐)
```bash
# 从GitHub仓库安装并运行MCP服务器
uvx --from git+https://github.com/cktbarking/semanticMCP.git semantic-scholar-mcp --transport stdio
# 或者使用简写方式
uvx cktbarking/semanticMCP --transport stdio
# 或者指定版本
uvx semantic-scholar-mcp@latest --transport stdio
```
### 本地开发
```bash
# 克隆项目
git clone https://github.com/cktbarking/semanticMCP.git
cd semanticMCP
# 安装依赖
uv sync
# 运行服务器
uv run semantic-scholar-mcp --transport stdio
```
### 传统方式
```bash
# 安装依赖
pip install -r requirements.txt
# 运行服务
python main.py
```
## MCP工具
### 1. search_papers - 搜索论文
搜索Semantic Scholar数据库中的学术论文。
**参数:**
- `query` (str, 必需): 搜索查询字符串
- `limit` (int, 可选): 返回结果数量,默认5,最大100
**返回:**
包含论文标题、作者、摘要、年份、引用次数、DOI、PDF链接等信息的列表。
### 2. download_paper - 下载论文PDF
根据DOI下载论文PDF文件,自动识别出版社。
**参数:**
- `doi` (str, 必需): 论文的DOI标识符
- `output_dir` (str, 可选): PDF输出目录,默认"./pdfs"
**支持的出版社:**
- IEEE (10.1109/*)
- ACM (10.1145/*)
- Elsevier (10.1016/*)
- Springer (10.1007/*)
- arXiv (10.48550/arXiv.*)
**返回:**
下载结果信息,包括成功状态、文件路径、出版社等。
## 响应格式
### 搜索响应示例
```json
{
"success": true,
"query": "machine learning",
"count": 5,
"results": [
{
"title": "Deep Learning for Computer Vision",
"authors": "John Doe, Jane Smith",
"abstract": "This paper presents...",
"year": 2023,
"citation_count": 150,
"article_url": "https://...",
"pdf_url": "https://...",
"doi": "10.1109/...",
"doi_link": "https://doi.org/10.1109/..."
}
]
}
```
### 下载响应示例
```json
{
"success": true,
"message": "PDF下载成功",
"file_path": "./pdfs/10.1109_example.pdf",
"doi": "10.1109/example",
"publisher": "ieee"
}
```
## 配置MCP客户端
### Claude Desktop配置
在Claude Desktop的配置文件中添加:
```json
{
"mcpServers": {
"semantic-scholar": {
"command": "uvx",
"args": ["--from", "git+https://github.com/cktbarking/semanticMCP.git", "semantic-scholar-mcp", "--transport", "stdio"]
}
}
}
```
### 其他MCP客户端
支持任何兼容MCP协议的客户端,只需配置命令为:
```bash
uvx semantic-scholar-mcp --transport stdio
```
## 开发说明
### 项目结构
```
semantic-scholar-mcp/
├── main.py # MCP服务器主文件
├── publishers/ # 出版社下载器
│ ├── ieee_downloader.py
│ ├── acm_downloader.py
│ ├── elsevier_downloader.py
│ ├── springer_downloader.py
│ ├── arxiv_downloader.py
│ └── generic_downloader.py
├── pyproject.toml # 项目配置
├── requirements.txt # 依赖列表
└── README.md # 说明文档
```
### 添加新的出版社支持
1. 在`publishers/`目录下创建新的下载器文件
2. 实现下载函数,接受`doi`, `doi_link`, `output_dir`参数
3. 在`publishers_dict.py`的`PUBLISHER_DICT`字典中添加DOI前缀到出版社的映射
4. 在`download_paper_by_doi()`函数中添加对应的处理分支
## 注意事项
1. **API限制** - Semantic Scholar API有调用频率限制,请合理使用
2. **版权合规** - 下载的PDF文件仅供个人学术研究使用
3. **网络要求** - 某些出版社可能需要机构访问权限,请合理使用。
4. **Python版本** - 需要Python 3.10或更高版本
## 许可证
GNU General Public License v3.0 - 详见LICENSE文件
## 贡献
欢迎提交Issue和Pull Request来改进这个项目!
## 致谢
- [Semantic Scholar](https://www.semanticscholar.org/) 提供优秀的学术搜索API
- [FastMCP](https://github.com/jlowin/fastmcp) 简化MCP服务器开发
- 各大学术出版社提供的研究资源
TDQS
Scored across 2 tools
The two tools have completely distinct purposes: one searches for academic papers, the other downloads a PDF by DOI. There is no overlap or ambiguity between them.
Both tool names follow the verb_noun pattern (search_papers, download_paper), which is consistent, predictable, and easy to understand.
With only 2 tools, the server feels significantly under-scoped for a service like Semantic Scholar. A typical integration would include many more capabilities, such as retrieving paper metadata, authors, citations, and recommendations, making this count too low for the domain.
The tool surface covers only search and PDF download by DOI. Major functionalities like fetching paper details by ID, exploring citations/references, and author information are missing, leaving obvious gaps that could cause agent failures.