No red flags found in any of the 11 categories — no credential harvesting, no data exfiltration, no curl-pipe-shell installer. Scanned against the SlowMist agent-security taxonomy, refreshed every 8 hours. Full audit →
by openags · MCP Server · ★ 2.7k
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is paper-search-mcp safe to install? View the security audit →
Paper Search MCP A Model Context Protocol (MCP) server for searching and downloading academic papers from multiple sources. The project follows a free-first strategy: prioritize open and public data sources, support optional API keys when they improve stability or coverage, and keep source-specific connectors extensible for advanced users. Table of Contents Overview Project Principles Features Source Strategy Sci-Hub Notice Installation Claude Code (Skill) Method 1 — Smithery Method 2 — uvx Method 3 — uv Method 4 — pip Method 5 — npx Method 6 — Docker Method 7 — Clone & run from source [Environment Variables](#env
| Stars | 2,679 |
| Forks | 276 |
| Language | Python |
| Category | MCP Server |
| License | MIT |
| Quality Score | 71.8471065045925/100 |
| Open Issues | 29 |
| Last Updated | 2026-09-22 |
| Created | 2025-04-06 |
| Platforms | cli, mcp, python |
| Est. Tokens | ~19k |
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paper-search-mcp is MCP, CLI, Skills for searching and downloading academic papers from multiple sources like arXiv, PubMed, bioRxiv, etc.. It is categorized as a MCP Server with 2.7k GitHub stars.
paper-search-mcp is primarily written in Python. It covers topics such as ai-scientist, arxiv-papers, mcp-server.
You can find installation instructions and usage details in the paper-search-mcp GitHub repository at github.com/openags/paper-search-mcp. The project has 2.7k stars and 276 forks, indicating an active community.
paper-search-mcp is released under the MIT license, making it free to use and modify according to the license terms.
The top alternatives to paper-search-mcp on Agent Skills Hub include XHS-Downloader, ida-pro-mcp, fastapi_mcp. Each offers a different approach to the same problem space — compare them side-by-side by stars, quality score, and community activity.
Grades come from a rule-based scan built on the SlowMist agent-security taxonomy, covering 11 red-flag categories including credential harvesting, data exfiltration, and curl | sh installers. It is a first-layer scan, not a manual audit — we say so rather than overstate it.
The scale of the problem is documented independently: Liu et al. (2026), in a study of 31,132 agent skills, report that 26.1% contain security vulnerabilities. Our own full-catalog census is published as a citable open dataset.
Sources & who's responsible: