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 ShellyDeng08 · MCP Server · ★ 71
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is youtube-connector-mcp safe to install? View the security audit →
YouTube MCP Server (youtube-connector-mcp) is a YouTube MCP Server / Connector that allows AI assistants like Claude, Cursor, Cline, Windsurf, Continue.dev, Grapes AI to interact with the YouTube Data API v3 via the Model Context Protocol (MCP). 🌐 Project Website: https://youtube-connector-mcp-website.vercel.app/ 📦 PyPI Package: https://pypi.org/project/youtube-connector-mcp/ Quick Start Prerequisites
| Stars | 71 |
| Forks | 3 |
| Language | Python |
| Category | MCP Server |
| Quality Score | 79.7265692791327/100 |
| Last Updated | 2026-03-19 |
| Created | 2026-01-28 |
| Platforms | claude-code, cli, mcp, python |
| Est. Tokens | ~10k |
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youtube-connector-mcp is MCP server for YouTube — search videos, get transcripts, channels, and playlists. Works with Claude, Cursor & any MCP client.. It is categorized as a MCP Server with 71 GitHub stars.
youtube-connector-mcp is primarily written in Python. It covers topics such as ai, ai-tools, claude.
You can find installation instructions and usage details in the youtube-connector-mcp GitHub repository at github.com/ShellyDeng08/youtube-connector-mcp. The project has 71 stars and 3 forks, indicating an active community.
The top alternatives to youtube-connector-mcp on Agent Skills Hub include claude-historian-mcp, vscode-mcp, mcp-fusion. 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: