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 ergut · MCP Server · ★ 338
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is mcp-logseq safe to install? View the security audit →
MCP server for LogSeq Connect Claude to your LogSeq knowledge base. Read, create, and manage pages — with optional semantic vector search and DB-mode graph support. ✨ What You Can Do Transform your LogSeq knowledge base into an AI-powered workspace! This MCP server enables Claude to seamlessly interact with your LogSeq graphs. 🎯 Real-World Examples 📊 Intelligent Knowledge Management 📝 Automated Content Creation 🔍 Smart Research & Analysis 🧠 Semantic Search (optional, requires vector setup) 🤝 Meeting & Documentation Workf
| Stars | 338 |
| Forks | 50 |
| Language | Python |
| Category | MCP Server |
| License | MIT |
| Quality Score | 71.2477296695641/100 |
| Open Issues | 2 |
| Last Updated | 2026-09-27 |
| Created | 2024-12-20 |
| Platforms | claude-code, mcp, python |
| Est. Tokens | ~16k |
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mcp-logseq is MCP server to interact with LogSeq via its Local HTTP API - enabling AI assistants like Claude to seamlessly read, write, and manage your LogSeq graph.. It is categorized as a MCP Server with 338 GitHub stars.
mcp-logseq is primarily written in Python. It covers topics such as knowledge-base, knowledge-management, logseq.
You can find installation instructions and usage details in the mcp-logseq GitHub repository at github.com/ergut/mcp-logseq. The project has 338 stars and 50 forks, indicating an active community.
mcp-logseq is released under the MIT license, making it free to use and modify according to the license terms.
The top alternatives to mcp-logseq on Agent Skills Hub include Noteriv, nocturne_memory, FirstData. 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: