scholar-search-mcp
An MCP server for academic paper search that integrates with AI assistants (e.g., Claude Code, Cursor), enabling them to search and retrieve academic paper metadata.
README
Scholar Search MCP
An MCP server for academic literature workflows in Claude, Cursor, and other MCP clients.
It combines Semantic Scholar + arXiv into one unified toolset, with fast parallel search, normalized outputs, source-aware deduplication, and practical research utilities (citations, references, author graph, recommendations, and arXiv source download).
Table of Contents
- Why this project
- Demo videos
- Install
- Quick setup (Claude Desktop / Cursor)
- Environment variables
- Tool list
- Testing with MCP Inspector
- Contributing
- License
Why this project
Most paper tools force you to choose one source or one API style. scholar-search-mcp provides one MCP layer for literature search and graph retrieval:
- One MCP server, multiple scholarly sources
- Free-first defaults (
arXivworks without keys) - LLM-friendly outputs for downstream reasoning and agent workflows
- Practical research actions, not only search
- Unified search:
search_papersruns Semantic Scholar + arXiv in parallel and deduplicates by normalized title. - Research graph tools: details, citations, references, author profile/papers, and recommendations.
- Batch + source workflows: fetch up to 500 papers, and download/extract arXiv LaTeX sources.
- Operational controls: built-in caching plus env-based source toggles (enable/disable channels).
- Source strategy: built-in Semantic Scholar + arXiv, free-first by default (
arXivkey-free), optional API key for higher Semantic Scholar limits.
Demo videos
Agent writes a survey paper with Scholar Search MCP.
<a href="https://youtu.be/C81rVeznoRY"><img src="./static/scholar_search_demo.jpg" alt="Agent uses Scholar Search MCP to write a survey paper" width="640" style="max-width: 100%; height: auto;"></a>
<br>
Install
pip install scholar-search-mcp
Requires Python 3.10+.
Quick setup (Claude Desktop / Cursor)
Use the same server command in both clients:
{
"mcpServers": {
"scholar-search": {
"command": "python",
"args": ["-m", "scholar_search_mcp"],
"env": {
"SCHOLAR_SEARCH_ENABLE_SEMANTIC_SCHOLAR": "true",
"SCHOLAR_SEARCH_ENABLE_ARXIV": "true"
}
}
}
}
SEMANTIC_SCHOLAR_API_KEY is optional. Add it only if you want higher Semantic Scholar rate limits:
{
"mcpServers": {
"scholar-search": {
"command": "python",
"args": ["-m", "scholar_search_mcp"],
"env": {
"SCHOLAR_SEARCH_ENABLE_SEMANTIC_SCHOLAR": "true",
"SCHOLAR_SEARCH_ENABLE_ARXIV": "true",
"SEMANTIC_SCHOLAR_API_KEY": "your-key"
}
}
}
}
Difference:
- Claude Desktop: edit local config file directly.
- Cursor: add an MCP server in Cursor settings UI (or corresponding settings JSON).
Claude Desktop config file locations:
- macOS:
~/Library/Application Support/Claude/claude_desktop_config.json - Windows:
%APPDATA%\Claude\claude_desktop_config.json - Linux:
~/.config/Claude/claude_desktop_config.json
Environment variables
| Variable | Description |
|---|---|
SEMANTIC_SCHOLAR_API_KEY |
Optional. Increases Semantic Scholar rate limits. |
SCHOLAR_SEARCH_ENABLE_SEMANTIC_SCHOLAR |
true/false, default true. |
SCHOLAR_SEARCH_ENABLE_ARXIV |
true/false, default true. |
SCHOLAR_SEARCH_CACHE_DIR |
Optional cache directory path. |
SCHOLAR_SEARCH_CACHE_TTL_SECONDS |
Cache TTL in seconds, default 86400. |
SCHOLAR_ARXIV_SOURCE_DIR |
Default parent directory for extracted arXiv sources. |
Example (arXiv only):
{
"SCHOLAR_SEARCH_ENABLE_SEMANTIC_SCHOLAR": "false",
"SCHOLAR_SEARCH_ENABLE_ARXIV": "true"
}
Tool list
| Tool | Purpose |
|---|---|
search_papers |
Search papers with optional limit, fields, year, venue. |
get_paper_details |
Get one paper by DOI, arXiv ID, S2 ID, or URL. |
get_paper_citations |
Get papers that cite a given paper. |
get_paper_references |
Get references of a given paper. |
get_author_info |
Get an author profile by ID. |
get_author_papers |
Get papers by a given author. |
get_paper_recommendations |
Get similar paper recommendations. |
batch_get_papers |
Batch fetch paper details (up to 500 IDs). |
download_arxiv_source |
Download and extract arXiv source bundle (tar.gz). |
Testing with MCP Inspector
npm install -g @modelcontextprotocol/inspector
mcp-inspector python -m scholar_search_mcp
Contributing
Issues and PRs are welcome: fork repo, create branch, add validation/tests, and open a PR with clear before/after behavior.
License
MIT
References
推荐服务器
Baidu Map
百度地图核心API现已全面兼容MCP协议,是国内首家兼容MCP协议的地图服务商。
Playwright MCP Server
一个模型上下文协议服务器,它使大型语言模型能够通过结构化的可访问性快照与网页进行交互,而无需视觉模型或屏幕截图。
Magic Component Platform (MCP)
一个由人工智能驱动的工具,可以从自然语言描述生成现代化的用户界面组件,并与流行的集成开发环境(IDE)集成,从而简化用户界面开发流程。
Audiense Insights MCP Server
通过模型上下文协议启用与 Audiense Insights 账户的交互,从而促进营销洞察和受众数据的提取和分析,包括人口统计信息、行为和影响者互动。
VeyraX
一个单一的 MCP 工具,连接你所有喜爱的工具:Gmail、日历以及其他 40 多个工具。
graphlit-mcp-server
模型上下文协议 (MCP) 服务器实现了 MCP 客户端与 Graphlit 服务之间的集成。 除了网络爬取之外,还可以将任何内容(从 Slack 到 Gmail 再到播客订阅源)导入到 Graphlit 项目中,然后从 MCP 客户端检索相关内容。
Kagi MCP Server
一个 MCP 服务器,集成了 Kagi 搜索功能和 Claude AI,使 Claude 能够在回答需要最新信息的问题时执行实时网络搜索。
e2b-mcp-server
使用 MCP 通过 e2b 运行代码。
Neon MCP Server
用于与 Neon 管理 API 和数据库交互的 MCP 服务器
Exa MCP Server
模型上下文协议(MCP)服务器允许像 Claude 这样的 AI 助手使用 Exa AI 搜索 API 进行网络搜索。这种设置允许 AI 模型以安全和受控的方式获取实时的网络信息。