Tuteliq MCP

Name: Tuteliq MCP
Author: Tuteliq

作者：Tuteliq版本 4.2.6i★ –i电脑或云端

注册名称ai.tuteliq/mcp

安装

概览

作者提供的说明（英文原文）

Detect grooming, bullying, fraud, and 16+ online threats across text, voice, image, and video.

PopNx MCP 的三大支柱

这三项永远不会合并成一个分数：意见、测量和人气是不同的东西。

用户评价i暂无评价评价功能将在后续阶段与账户功能一起开放
我们的基础检查i3 项中通过 3 项依据公开信息检查；尚未扫描代码
使用情况i尚未统计★ –

它能帮你做什么

我们还没有为它写通俗的介绍。下面是我们从注册中心读取到的事实。

连接方式: 电脑或云端
传输方式: stdio, Streamable HTTP
分类: 设计、图片与音频
你需要填写：TUTELIQ_API_KEY

安装

选择你使用的 AI 应用，然后复制设置。

安装之前

基础检查：3 项中通过 3 项
在作者的服务器上运行：你向它提出的内容会经过他们的服务器。
需要你自己填写： TUTELIQ_API_KEY

标为机密的值是你账户的钥匙。千万不要分享给别人，也不要粘贴到聊天里。

你需要

在云端运行无需安装任何东西，通过互联网连接
npm已安装 Node.js（用于 npx 命令）

你用的是哪个应用？

ClaudeClaude CodeCursorVS CodeChatGPT

在上方选择你的应用。我们会记住它，下次按钮会显示“安装到 …”。

适用于网页版、桌面版和手机版 Claude：

打开 Settings → Connectors
点击 Add custom connector
粘贴下面的地址，然后点击 Add
如果要求登录，请登录作者的服务

https://api.tuteliq.ai/mcp

或者在你的电脑上运行

打开 Claude Desktop → Settings → Developer → Edit Config，把下面的内容粘贴到 claude_desktop_config.json

{
  "mcpServers": {
    "tuteliq": {
      "command": "npx",
      "args": [
        "-y",
        "@tuteliq/mcp"
      ],
      "env": {
        "TUTELIQ_API_KEY": "your-value"
      }
    }
  }
}

在终端中运行这条命令

claude mcp add --transport http tuteliq https://api.tuteliq.ai/mcp

或者在你的电脑上运行

claude mcp add tuteliq --env TUTELIQ_API_KEY=your-value -- npx -y @tuteliq/mcp

运行前请先填写空着的值。

在 Cursor 中打开

或者自己复制设置

粘贴到项目中的 .cursor/mcp.json，或 ~/.cursor/mcp.json

{
  "mcpServers": {
    "tuteliq": {
      "url": "https://api.tuteliq.ai/mcp"
    }
  }
}

或者在你的电脑上运行

在 Cursor 中打开

或者自己复制设置

粘贴到项目中的 .cursor/mcp.json，或 ~/.cursor/mcp.json

{
  "mcpServers": {
    "tuteliq": {
      "command": "npx",
      "args": [
        "-y",
        "@tuteliq/mcp"
      ],
      "env": {
        "TUTELIQ_API_KEY": "your-value"
      }
    }
  }
}

浏览器会先询问是否打开应用，然后应用会显示设置并请你确认。密钥的值会留空，由你自己填写。

在 VS Code 中打开

或者自己复制设置

粘贴到项目中的 .vscode/mcp.json

{
  "servers": {
    "tuteliq": {
      "type": "http",
      "url": "https://api.tuteliq.ai/mcp"
    }
  }
}

或者在你的电脑上运行

在 VS Code 中打开

或者自己复制设置

粘贴到项目中的 .vscode/mcp.json

{
  "servers": {
    "tuteliq": {
      "type": "stdio",
      "command": "npx",
      "args": [
        "-y",
        "@tuteliq/mcp"
      ],
      "env": {
        "TUTELIQ_API_KEY": "your-value"
      }
    }
  }
}

浏览器会先询问是否打开应用，然后应用会显示设置并请你确认。密钥的值会留空，由你自己填写。

ChatGPT 只能连接互联网上的服务器：

打开网页版 ChatGPT → Settings → Connectors (Apps)
点击 Create 或 Add（部分套餐需要先在 Advanced 中开启 Developer mode）
粘贴下面的地址，起个名字，然后保存
如果要求登录，请登录作者的服务

https://api.tuteliq.ai/mcp

菜单名称可能因套餐和版本不同而略有差异。

你的选择通过 Cookie 保存在这台设备上，无需账户。

需要你填写的值

TUTELIQ_API_KEY 机密必填 env
Tuteliq API key from tuteliq.ai

这些设置是根据作者在注册中心的条目生成的。如果 README 的写法不同，请以 README 为准。

它能做什么

服务器要求先登录，才会列出它的工具。

基础检查i

我们检查的是任何人都能看到的信息；我们没有阅读代码。不适用的检查项不计入总数。 · 检查于 2026年11月8日

✓ 通过
作者与仓库一致
注册名称已通过域名 tuteliq.ai 验证（注册中心会核实域名所有权）
… 尚未检查
仍在维护
我们还没有从 GitHub 读取这个仓库
… 尚未检查
有许可证
我们还没有从 GitHub 读取这个仓库
✓ 通过
端点有响应
在 274 毫秒内响应（HTTP 401）
✓ 通过
使用 OAuth 登录
声明支持 OAuth 登录，你的 AI 应用可以带你去登录
… 尚未检查
工具描述中没有隐藏指令
尚未检查；我们会在检查端点时读取工具列表

与“设计、图片与音频”中的其他服务器比较i

星标数多于此分类中其他 2757 个服务器的 0%
发布到注册中心的时间比此分类中其他 2757 个服务器的 87% 更新
基础检查结果等于或优于此分类中其他 2757 个服务器的 100%

与注册中心里这个分类中所有活跃的服务器比较。只有当发布者拥有该仓库时，星标数才计入。

常见问题

Tuteliq MCP 安全吗？

它在 3 项适用的基础检查中通过了 3 项。我们没有阅读代码。在添加密钥或令牌之前，请先看看这个服务器要求哪些权限，并使用只具备所需权限的密钥。

它需要哪些密钥或代码？

你需要填写：TUTELIQ_API_KEY

它在我的电脑上运行还是在云端？

两种方式都可以：安装在你的电脑上，或者连接云端版本。

它还在维护吗？

GitHub 仓库最后更新于–，最新版本（4.2.6）于5天前进入注册中心。

哪些应用可以使用它？

支持 MCP 的应用，例如 Claude Desktop、Claude Code、Cursor 和 VS Code。它通过 URL 连接，所以接受远程 MCP 服务器的应用也可以使用，例如开启开发者模式的 ChatGPT。

有什么可以替代它？

同一分类中有：Figma Context MCP, Claude Real Video, Figma MCP Server

替代选择

同一分类中其他作者的服务器，按星标数排序。

如何看卡片：★ GitHub 星标i三大支柱：评价 · 检查 · 使用情况i更新 = 最新版本发布到注册中心的时间i

Figma Context MCP

作者：GLips

Give your coding agent access to your Figma data. Implement designs in any framework in one-shot.

在你的电脑上运行★ 16,165MIT5个月前更新

安装

Claude Real Video

作者：HUANGCHIHHUNGLeo

Let any LLM watch a video locally — and search everything it has ever watched.

在你的电脑上运行★ 2,284MIT2个月前更新

安装

Figma MCP Server

作者：figma

The Figma MCP server brings Figma design context directly into your AI workflow.

在云端运行★ 2,15213个月前更新

安装

作者的 README

打开 README

按原文从作者的仓库复制。PopNx MCP 没有审查或背书其内容。HTML、图片和脚本已被移除。链接会离开本站。

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`
Media	`analyze_voice`, `analyze_image`, `analyze_video`, `analyze_document`
Synthetic media	`detect_synthetic_text`, `detect_synthetic_image`, `detect_synthetic_audio`, `detect_synthetic_video`
Action plan	`get_action_plan`
Incident report	`generate_report`
Incidents overview	`get_incidents_overview`
Incidents list	`list_incidents`
Incident detail	`get_incident`
Incident trends	`get_incident_trends`
Moderation queue	`moderation_queue`

Severity is rankable by colour. The ramp runs monotonically from safe to critical, so two chips can be compared without reading their labels:

Level	Colour
`critical`	`#9C3A29`
`high`	`#C2543A`
`medium`	`#D98A3D`
`low`	`#B7C2D4`
`safe`	`#19B79A`

Design notes. The widgets are deliberately calm. They report on grooming, self-harm, and abuse, and a card that animates or pulses at the reader turns material that is already distressing into an alarm they cannot dismiss. Severity is carried by a rule and a glyph, not by motion. The crisis-support card leads with reassurance rather than the severity colour, and its helpline numbers are the largest targets on the card because transcribing digits under stress is where people fail.

Widgets are read-only renderers by design. Selecting incidents in the list widget assembles the ID list for a batch_review_incidents call you fire yourself — the mutating call still goes through your host's approval step, so the human-in-the-loop stays in the loop.

Working on the widgets

npm run preview:ui   # builds every widget against fixture data

open dist-preview/__preview.html

Widget source lives in ui/src. Design tokens are centralised in ui/src/theme.ts; prefer them over colour literals so the palette stays in one place.

Available Tools (81 MCP)

Safety Detection

Tool	Description
`detect_bullying`	Analyze text for bullying, harassment, and gaming toxicity — including coded slang, emoji, and deliberate filter evasion, with context that tells trash-talk apart from genuine harm
`detect_grooming`	Detect grooming patterns and predatory behavior in conversations
`detect_unsafe`	Identify unsafe content (self-harm, violence, explicit material)
`analyze`	Quick comprehensive safety check (bullying + unsafe)
`analyse_multi`	Run multiple detection endpoints on a single piece of text in one call
`batch_analyze`	Analyze up to 50 items in a single request — all twelve detection types (bullying, grooming, unsafe, emotions, social_engineering, app_fraud, romance_scam, mule_recruitment, gambling_harm, coercive_control, vulnerability_exploitation, radicalisation) — ideal for bulk triage
`analyze_emotions`	Analyze emotional content and mental state indicators — accepts single text or full conversations
`get_action_plan`	Generate age-appropriate guidance for safety situations
`generate_report`	Create incident reports from conversations

Fraud & Harm Detection

Tool	Description
`detect_social_engineering`	Detect social engineering tactics (pretexting, urgency fabrication, authority impersonation)
`detect_app_fraud`	Detect app-based fraud (fake investment platforms, phishing apps, subscription traps)
`detect_romance_scam`	Detect romance scam patterns (love-bombing, financial requests, identity deception)
`detect_mule_recruitment`	Detect money mule recruitment tactics (easy-money offers, bank account sharing)
`detect_gambling_harm`	Detect gambling-related harm indicators (chasing losses, concealment, distress)
`detect_coercive_control`	Detect coercive control patterns (isolation, financial control, monitoring, threats)
`detect_vulnerability_exploitation`	Detect exploitation of vulnerable individuals (elderly, disabled, financially distressed)
`detect_radicalisation`	Detect radicalisation indicators (extremist rhetoric, us-vs-them framing, ideological grooming)

Voice, Image, Video & Document Analysis

Tool	Description
`analyze_voice`	Transcribe audio and run safety analysis on the transcript
`analyze_image`	Analyze images for visual safety + OCR text extraction
`analyze_video`	Analyze video files for safety concerns via key frame extraction (supports mp4, mov, avi, webm, mkv)
`analyze_document`	Analyze PDF documents for safety concerns — per-page multi-endpoint detection with chain-of-custody hashing (max 50MB, 100 pages)

Synthetic Content Detection

Tool	Description
`detect_synthetic_text`	Detect AI-generated text across 10 child-safety categories (synthetic CSAM, deepfake scripts, AI grooming)
`detect_synthetic_image`	6-signal forensic pipeline: vision AI, EXIF metadata, pixel stats, C2PA Content Credentials, watermarks, pHash
`detect_synthetic_audio`	Dual-signal forensics: transcript + mel spectrogram vision + quantitative audio statistics
`detect_synthetic_video`	5-track analysis: per-frame vision, temporal face consistency, lip-sync correlation, spectral audio, transcript

Identity & Age Verification

Tool	Description
`create_verification_session`	Create a session for age or identity verification — returns a URL for the user to complete the flow
`get_verification_session`	Poll session status — returns full document intelligence (MRZ, barcode, authenticity, face match, liveness)
`cancel_verification_session`	Cancel an active session (no credits consumed)

These three tools are registered when you run this package yourself. The hosted connector does not serve them.

Incidents & Moderation

Read the incident store, triage a queue, and record moderator decisions. The review tools emit signed receipts for EU AI Act Art 14 human-oversight evidence.

Tool	Description
`get_incidents_overview`	Counts by category, severity, source, status and platform over a window
`list_incidents`	Paginated, filterable incident list
`get_incident`	Full detail for one incident, including the risk trajectory across messages
`get_incident_trends`	Incident volume bucketed by hour, day or week, split by severity
`moderation_queue`	Moderator triage console: the unreviewed queue, the next item, and — optionally — your own analysis trace and recommended decision, rendered for human sign-off. Read-only
`review_incident`	Record a moderator decision (confirm / downgrade / escalate / reclassify / dismiss) with a signed receipt
`batch_review_incidents`	Apply one decision across many incidents in a single call
`get_audit_receipt`	Fetch the signed receipt for a past inference
`get_audit_logs`	Query the audit log

The decision is the moderator's, and the card makes them take it. The action buttons call review_incident through the host, because a moderator clicking "Escalate" *is* the human decision. They do not fire on one click: review_incident persists an override and emits a signed Art 12 audit receipt and requires a reason_code, so the button opens a reason picker and a second click commits. Nothing is ever defaulted into that receipt on the moderator's behalf.

The reasoning, confidence and analysis trace on the card are supplied by the calling assistant and are labelled as such — an argument for a human to weigh, not a Tuteliq measurement.

Pass operator_name to brand the header with the customer or team name. Omit it and the card is unbranded — it is never defaulted to a placeholder.

Webhook Management

Tool	Description
`list_webhooks`	List all configured webhooks
`create_webhook`	Create a new webhook endpoint
`update_webhook`	Update webhook configuration
`delete_webhook`	Delete a webhook
`test_webhook`	Send a test payload to verify webhook
`regenerate_webhook_secret`	Regenerate webhook signing secret

Pricing

Tool	Description
`get_pricing`	Get available pricing plans
`get_pricing_details`	Get detailed pricing with features and limits

Usage & Billing

Tool	Description
`get_usage_history`	Get daily usage history
`get_usage_by_tool`	Get usage by tool/endpoint
`get_usage_monthly`	Get monthly usage with billing info
`get_usage_summary`	Get today's usage: requests made, succeeded and failed, remaining today, and the rate limit
`get_usage_quota`	Get real-time rate-limit status — pre-flight check before batch runs

Policy Configuratio

只显示了开头部分；其余内容请在仓库中阅读

Tuteliq MCP

概览

PopNx MCP 的三大支柱

它能帮你做什么

安装

安装之前

或者在你的电脑上运行

或者在你的电脑上运行

或者在你的电脑上运行

或者在你的电脑上运行

需要你填写的值

它能做什么

基础检查i

作者与仓库一致

仍在维护

有许可证

端点有响应

使用 OAuth 登录

工具描述中没有隐藏指令

与“设计、图片与音频”中的其他服务器比较i

常见问题

替代选择

Figma Context MCP

Claude Real Video

Figma MCP Server

作者的 README

What is this?

Interactive widgets

Working on the widgets

Available Tools (81 MCP)

Safety Detection

Fraud & Harm Detection

Voice, Image, Video & Document Analysis

Synthetic Content Detection

Identity & Age Verification

Incidents & Moderation

Webhook Management

Pricing

Usage & Billing

Policy Configuratio