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 SylphxAI · MCP Server · ★ 935
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is pdf-reader-mcp safe to install? View the security audit →
Featured in: Document Parsing
📄 @sylphx/pdf-reader-mcp Production-ready PDF processing server for AI agents 5-10x faster parallel processing • Y-coordinate content ordering • 94%+ test coverage • 103 tests passing 🚀 Overview PDF Reader MCP is a production-ready Model Context Protocol server that
| Stars | 935 |
| Forks | 82 |
| Language | TypeScript |
| Category | MCP Server |
| License | MIT |
| Quality Score | 69.7601337717112/100 |
| Open Issues | 1 |
| Last Updated | 2026-09-20 |
| Created | 2025-04-04 |
| Platforms | mcp, node |
| Est. Tokens | ~18k |
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pdf-reader-mcp is Give your AI agent eyes for PDFs — structured text, tables, OCR, visual evidence, and page-level citations via MCP. Native Rust, local-first.. It is categorized as a MCP Server with 935 GitHub stars.
pdf-reader-mcp is primarily written in TypeScript. It covers topics such as agent-document-twin, ai-agent, ai-tools.
You can find installation instructions and usage details in the pdf-reader-mcp GitHub repository at github.com/SylphxAI/pdf-reader-mcp. The project has 935 stars and 82 forks, indicating an active community.
pdf-reader-mcp is released under the MIT license, making it free to use and modify according to the license terms.
The top alternatives to pdf-reader-mcp on Agent Skills Hub include ExtractThinker, casibase, markdownify-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: