by PDFMathTranslate · MCP Server · ★ 37.6k
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is PDFMathTranslate safe to install? View the security audit →
Featured in: Document Parsing
Special thanks to: Warp, built for coding with multiple AI agents Available for macOS, Linux and Windows English 한국어 PDFMathTranslate
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 →
| Stars | 37,553 |
| Forks | 3,358 |
| Language | Python |
| Category | MCP Server |
| License | AGPL-3.0 |
| Quality Score | 65.3817370951434/100 |
| Open Issues | 165 |
| Last Updated | 2026-10-14 |
| Created | 2024-09-06 |
| Platforms | cli, docker, mcp, python |
| Est. Tokens | ~18k |
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PDFMathTranslate is [EMNLP 2025 Demo] PDF scientific paper translation with preserved formats - 基于 AI 完整保留排版的 PDF 文档全文双语翻译，支持 Google/DeepL/Ollama/OpenAI 等服务，提供 CLI/GUI/MCP/Docker/Zotero. It is categorized as a MCP Server with 37.6k GitHub stars.
PDFMathTranslate is primarily written in Python. It covers topics such as chinese, document, edit.
You can find installation instructions and usage details in the PDFMathTranslate GitHub repository at github.com/PDFMathTranslate/PDFMathTranslate. The project has 37.6k stars and 3358 forks, indicating an active community.
PDFMathTranslate is released under the AGPL-3.0 license, making it free to use and modify according to the license terms.
The top alternatives to PDFMathTranslate on Agent Skills Hub include AstrBot, ChatTTS, Skill_Seekers. 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: