Detect grooming, bullying, fraud, and 16+ online threats across text, voice, image, and video.
Config is the same across clients — only the file and path differ.
{
"mcpServers": {
"ai-tuteliq-mcp": {
"command": "<see-readme>",
"args": []
}
}
}Are you the author?
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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.
No automated test available for this server. Check the GitHub README for setup instructions.
Five weighted categories — click any category to see the underlying evidence.
No known CVEs.
No package registry to scan.
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MCP server for Tuteliq - AI-powered child safety tools for Claude
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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.
| Tool | Description |
|---|---|
detect_bullying | Analyze text for bullying, harassment, or harmful language |
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 |
analyze_emotions | Analyze emotional content and mental state indicators |
get_action_plan | Generate age-appropriate guidance for safety situations |
generate_report | Create incident reports from conversations |
| 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) |
| 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) |
| 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 |
get_synthetic_profile | Account-level 30-day rolling wind |