Give your AI agent eyes to see the entire internet. Read & search Twitter, Reddit, YouTube, GitHub, Bilibili, XiaoHongShu — one CLI, zero API fees.
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.
Install
pip install scholar-search-mcpInstall command taken from the project's README or registry entry. Always review code before running it.
README excerpt
Read full README on GitHub →Scholar Search MCP
An MCP server for academic literature workflows in Claude, Cursor, and other MCP clients.
It combines Semantic Scholar and 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
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.
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
Similar plugs
Context7 Platform -- Up-to-date code documentation for LLMs and AI code editors
An open-source, privacy-first, self-hosted knowledge workspace where humans and AI agents work together 开源、隐私优先、自托管的知识工作空间,让人与智能体在此协作
Enhanced ChatGPT Clone: Features Agents, MCP, Skills, DeepSeek, Anthropic, AWS, OpenAI, Responses API, Azure, Groq, o1, GPT-5, Mistral, OpenRouter, Vertex AI, Gemini,…
Composio powers 1000+ toolkits, tool search, context management, authentication, and a sandboxed workbench to help you build AI agents that turn intent into action.
An autonomous agent that conducts deep research on any data using any LLM providers