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 Arenukvern · MCP Server · ★ 383
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is mcp_flutter safe to install? View the security audit →
flutter-mcp-toolkit Inspect and drive a running Flutter app from your AI assistant. 📖 Docs: docs.page/arenukvern/mcpflutter is a Dart MCP server + Flutter package that lets AI Agents (Codex, Zed, Cursor, Intent, Claude Code, Cline, etc..) take (semantic snapshots, tap widgets, type into forms, hot-reload, and read logs from a Flutter app) or create its own tools and resources at runtime using MCP Toolkit — without leaving the conversation and work with Flutter
| Stars | 383 |
| Forks | 46 |
| Language | Dart |
| Category | MCP Server |
| License | MIT |
| Quality Score | 65.1969284984793/100 |
| Open Issues | 7 |
| Last Updated | 2026-10-03 |
| Created | 2025-03-17 |
| Platforms | cli, mcp |
| Est. Tokens | ~19k |
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mcp_flutter is MCP Toolkit for Flutter AI Agent Driven Development (MCP/CLI + custom client side tools) - via closed feedback loop (visual & semantic snapshot) and high client side customization adaptable for any Fl. It is categorized as a MCP Server with 383 GitHub stars.
mcp_flutter is primarily written in Dart. It covers topics such as ai, cli-tools, dart.
You can find installation instructions and usage details in the mcp_flutter GitHub repository at github.com/Arenukvern/mcp_flutter. The project has 383 stars and 46 forks, indicating an active community.
mcp_flutter is released under the MIT license, making it free to use and modify according to the license terms.
The top alternatives to mcp_flutter on Agent Skills Hub include Benzi, mcp-client-for-ollama, volcano-agent-sdk. 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: