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 jztan · MCP Server · ★ 136
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is pdf-mcp safe to install? View the security audit →
pdf-mcp Surgical PDF access for AI agents — search, read, and extract without flooding context. A Model Context Protocol (MCP) server that enables AI agents to read, search, and extract content from PDF files. Built with Python and PyMuPDF, with SQLite-based caching for persistence across server restarts. mcp-name: io.github.jztan/pdf-mcp Try it in your browser See what your AI agent sees → Drop in any PDF and watch an agent skim it, search it, and read only the pages that matter — using a fraction of the tokens. 100% client-side, no install required. [<img src="https://raw.githubusercontent.com/jztan/pdf-mcp/develop/docs/images/demo.gif" alt="pdf-mcp browser de
| Stars | 136 |
| Forks | 14 |
| Language | Python |
| Category | MCP Server |
| License | MIT |
| Quality Score | 81.3392335563874/100 |
| Open Issues | 1 |
| Last Updated | 2026-09-22 |
| Created | 2026-01-28 |
| Platforms | claude-code, cli, codex, mcp, python |
| Est. Tokens | ~17k |
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pdf-mcp is MCP server that lets Claude Code and other AI agents read and search large PDFs, one file or a whole folder: agentic RAG with hybrid semantic + keyword search, selective page reads, tables, images, OC. It is categorized as a MCP Server with 136 GitHub stars.
pdf-mcp is primarily written in Python. It covers topics such as agentic-rag, ai, cjk.
You can find installation instructions and usage details in the pdf-mcp GitHub repository at github.com/jztan/pdf-mcp. The project has 136 stars and 14 forks, indicating an active community.
pdf-mcp is released under the MIT license, making it free to use and modify according to the license terms.
The top alternatives to pdf-mcp on Agent Skills Hub include pdfmux, roampal-core, octocode. 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: