Detect grooming, bullying, fraud, and 16+ online threats across text, voice, image, and video.
Quick Install
Automated & IDE SetupCopy the AI prompt to install this server into Claude Code, Cursor, or another agent — or use 1-click editor setup below.
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Manual Client & Custom JSON ConfigExpand JSON ▾
Client Config & Setup
Choose your client or environment
Target File:
💡 Paste the JSON block into your client's configuration file under mcpServers, then restart the application.
Capabilities & Tool Schemas (75)
Inspect callable tools, capabilities, and parameters exposed to AI agents by Tuteliq MCP Server.
detect_bullyingAnalyze 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_groomingDetect grooming patterns and predatory behavior in conversations
detect_unsafeIdentify unsafe content (self-harm, violence, explicit material)
analyzeQuick comprehensive safety check (bullying + unsafe)
analyse_multiRun multiple detection endpoints on a single piece of text in one call
batch_analyzeAnalyze 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
Documentation Overview
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
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) |
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.
Related MCP Servers
Adoption & maintenance
Factual signals from GitHub, npm, and our automated checks — not a rating.
GitHub stars
3
Stargazers on the source repository.
npm downloads
3.6k
Package downloads in the last 30 days.
Last commit
4d ago
Most recent push to the default branch.
Tools exposed
75
Callable tools this server registers over MCP.
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Frequently Asked Questions about Tuteliq MCP Server
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CategoryFinance & Fintech
12/12 checks healthy over the last 46d
59Quality signal: Good · 59/100How this signal is calculated ▾
Server availabilityNot measured
Not scored for repo-hosted servers — we can't reach the running server, only its GitHub page. Hosted MCP endpoints are health-checked live.
Verified ownership10/20
Documentation & tools25/30
Adoption & activity9/15
Community engagement0/10
A guidance signal from public completeness & health data — not a user rating. New listings start lower and rise as they add docs, get verified, and grow adoption. Signals we can't observe for a listing are skipped, not counted against it.
Supply-chain signal
No high-severity advisories surfaced by our automated scan.
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Scanned 3d ago via OSV.dev · @tuteliq/mcp (npm)
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