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 alondmnt · MCP Server · ★ 171
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is joplin-mcp safe to install? View the security audit →
Joplin MCP Server A FastMCP-based Model Context Protocol (MCP) server for Joplin note-taking application via its Python API joppy, enabling AI assistants to interact with your Joplin notes, notebooks, and tags through a standardized interface. Table of Contents What You Can Do Quick Start Supported Clients Example Usage Tool Permissions Notebook Allowlist Advanced Configuration Docker Project Structure Testing Complete Tool Reference Changelog What You Can Do This MCP server provides 26 optimized tools for comprehensive Joplin integration: Note Management Find & Search: (supports for trashed notes), , , CRUD Operations: , (read OCR text from attached images/PDFs), , , , , Notebook Management Organize: , , , Tag Management Categorize: , , , , gettag
| Stars | 171 |
| Forks | 17 |
| Language | Python |
| Category | MCP Server |
| License | MIT |
| Quality Score | 78.3691476813426/100 |
| Open Issues | 3 |
| Last Updated | 2026-09-12 |
| Created | 2025-07-06 |
| Platforms | mcp, python |
| Est. Tokens | ~19k |
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joplin-mcp is MCP server for the Joplin note taking app. It is categorized as a MCP Server with 171 GitHub stars.
joplin-mcp is primarily written in Python. It covers topics such as joplin, mcp, mcp-server.
You can find installation instructions and usage details in the joplin-mcp GitHub repository at github.com/alondmnt/joplin-mcp. The project has 171 stars and 17 forks, indicating an active community.
joplin-mcp is released under the MIT license, making it free to use and modify according to the license terms.
The top alternatives to joplin-mcp on Agent Skills Hub include zettelkasten-mcp, ChatCrystal, obsidian-mcp-server. 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: