MCP server with 41 AI-powered tools for child safety, fraud detection, and content moderation. Detects bullying, grooming, sextortion, romance scams, social engineering, and more.
概览
MCP server for Tuteliq - AI-powered child safety tools for Claude API Docs • Dashboard • Trust • Changelog • Discord Tuteliq MCP Server brings AI-powered child safety tools directly into Claude, Cursor, and other MCP-compatible AI assistants. Ask Claude to check messages for bullying, detect grooming patterns, or generate safety action plans. Every detector understands coded slang, emoji, leetspeak, algospeak, and deliberate filter evasion, and weighs the conversation around a message — so it tells gaming trash-talk apart from targeted harassment instead of drowning your team in false positives. This coded-language resilience is platform-wide (it applies to grooming, fraud, radicalisation, and the rest, not just bullying) and is built from our own research into how bad actors evade moderation. In an internal benchmark of coded-language and filter-evasion cases, Tuteliq detected roughly of them than leading general-purpose moderation APIs (319-case evasion set; vendors unnamed).
README
以下为原文 README 节选，完整内容请查看原文。
Tuteliq MCP Server
MCP server for Tuteliq - AI-powered child safety tools for Claude
API Docs • Dashboard • Trust • Changelog • Discord
What is this?
Tuteliq MCP Server brings AI-powered child safety tools directly into Claude, Cursor, and other MCP-compatible AI assistants. Ask Claude to check messages for bullying, detect grooming patterns, or generate safety action plans.
Reads context, not just keywords. Every detector understands coded slang, emoji, leetspeak, algospeak, and deliberate filter evasion, and weighs the conversation around a message — so it tells gaming trash-talk apart from targeted harassment instead of drowning your team in false positives. This coded-language resilience is platform-wide (it applies to grooming, fraud, radicalisation, and the rest, not just bullying) and is built from our own research into how bad actors evade moderation. In an internal benchmark of coded-language and filter-evasion cases, Tuteliq detected roughly 1.7x more of them than leading general-purpose moderation APIs (319-case evasion set; vendors unnamed).
Fast mode. Pass verdict_only: true on detect_grooming or detect_bullying to get just the verdict (risk level, flags, recommended action) without the per-message breakdown — lower latency for real-time screening. The verdict itself is unchanged.
Interactive results. In hosts that support MCP Apps, results render as interactive cards rather than walls of JSON — see Interactive widgets below.
Interactive widgets
Twelve widgets return a rendered card instead of raw text in hosts that support MCP
Apps (Claude desktop and web, and other MCP-compatible clients). Everywhere else
the same data arrives as structuredContent, so nothing depends on the UI.
Every card carries the same frame: a chrome bar naming the tool that produced the result, the result itself, and a footer with the data-handling note and a Trust Center link. In a transcript holding a dozen results, the chrome bar is what tells you which is which.
| Widget | Tools |
|---|---|
| Detection result | detect_bullying, detect_grooming, detect_unsafe, analyze, and the other detect_* tools |
| Multi-endpoint | analyse_multi |
| Emotions | analyze_emotions |
安装
npx -y @tuteliq/mcp配置
{
"mcpServers": {
"tuteliq": {
"type": "http",
"url": "https://api.tuteliq.ai/mcp"
}
}
}