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MCP

未认领

作者:Tuteliq

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. For Claude, Cursor, and MCP-compatible AI assistants.

aichild-safetyclaudecontent-moderationmcpmodel-context-protocolsafetytoolsbullying-detectiongrooming-detection

01 / 选择客户端

02 / 连接前的准备

认证方式尚未指定。连接前请查看项目说明。

安装项目所需的运行环境,并确保客户端能够使用它。

项目说明

输入项目说明中的变量名,用逗号分隔。只输入名称,不要输入密钥值。

03 / 添加配置

来源:根据公开安装说明生成

claude mcp add --transport stdio 'tuteliq-mcp' -- 'npx' '-y' '@tuteliq/mcp'

替换占位符后在终端中运行。

04 / 在客户端中确认

打开客户端的 MCP 设置,确认服务器已连接并列出工具。复制配置不代表连接成功。

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未认领列表

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此列表根据公开信息自动生成。认领后,你可以编辑页面、设置兼容性并解锁增长工具。全程不超两分钟。

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安全概况

已认领和已认证的服务器每周接受一次静态扫描,显示代码能触及的范围(外部服务、环境变量、shell 命令、代理配置目录)以及带有已知公告的依赖。认领此列表即可获得。 安全概况的工作原理

40 个工具中显示 40 个

文档中列出的工具 (40)

内容来自项目文档。服务器握手不会验证每个工具的说明或行为。

analyse_multi

Run multiple detection endpoints on a single piece of text in one call

analyze

Quick comprehensive safety check (bullying + unsafe)

analyze_document

Analyze PDF documents for safety concerns — per-page multi-endpoint detection with chain-of-custody hashing (max 50MB, 100 pages)

analyze_emotions

Analyze emotional content and mental state indicators — accepts single text or full conversations

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_voice

Transcribe audio and run safety analysis on the transcript

batch_analyze

Analyze up to 50 items in a single request — all twelve detection types (bullying, grooming, unsafe, emotions, socialengineering, appfraud, romancescam, mulerecruitment, gamblingharm, coercivecontrol, vulnerabilityexploitation, radicalisation) — ideal for bulk triage

batch_review_incidents

Apply one decision across many incidents in a single call

cancel_verification_session

Cancel an active session (no credits consumed)

create_verification_session

Create a session for age or identity verification — returns a URL for the user to complete the flow

create_webhook

Create a new webhook endpoint

detect_app_fraud

Detect app-based fraud (fake investment platforms, phishing apps, subscription traps)

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_coercive_control

Detect coercive control patterns (isolation, financial control, monitoring, threats)

detect_gambling_harm

Detect gambling-related harm indicators (chasing losses, concealment, distress)

detect_grooming

Detect grooming patterns and predatory behavior in conversations

detect_mule_recruitment

Detect money mule recruitment tactics (easy-money offers, bank account sharing)

detect_radicalisation

Detect radicalisation indicators (extremist rhetoric, us-vs-them framing, ideological grooming)

detect_romance_scam

Detect romance scam patterns (love-bombing, financial requests, identity deception)

detect_social_engineering

Detect social engineering tactics (pretexting, urgency fabrication, authority impersonation)

detect_synthetic_audio

Dual-signal forensics: transcript + mel spectrogram vision + quantitative audio statistics

detect_synthetic_image

6-signal forensic pipeline: vision AI, EXIF metadata, pixel stats, C2PA Content Credentials, watermarks, pHash

detect_synthetic_text

Detect AI-generated text across 10 child-safety categories (synthetic CSAM, deepfake scripts, AI grooming)

detect_synthetic_video

5-track analysis: per-frame vision, temporal face consistency, lip-sync correlation, spectral audio, transcript

detect_unsafe

Identify unsafe content (self-harm, violence, explicit material)

detect_vulnerability_exploitation

Detect exploitation of vulnerable individuals (elderly, disabled, financially distressed)

generate_report

Create incident reports from conversations

get_action_plan

Generate age-appropriate guidance for safety situations

get_audit_logs

Query the audit log

get_audit_receipt

Fetch the signed receipt for a past inference

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

get_incidents_overview

Counts by category, severity, source, status and platform over a window

get_verification_session

Poll session status — returns full document intelligence (MRZ, barcode, authenticity, face match, liveness)

list_incidents

Paginated, filterable incident list

list_webhooks

List all configured webhooks

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

update_webhook

Update webhook configuration

工具变更历史

比较相同配置的完整检查。只列出工具,未调用工具。此处不测量输入结构的变化。

尚无完整工具检查。