detect_bullyingLABSCO SUMMARY
Safety classification that reads context, plus the moderation record-keeping that has to follow it.
The detectors weigh the surrounding conversation and are built for coded slang, emoji and deliberate filter evasion, which is what separates gaming trash-talk from targeted harassment. The half people underestimate is the incident side: review_incident requires a reason code and emits a signed receipt, so the human decision is recorded as one rather than inferred.
What it is
A hosted MCP server for Tuteliq's child-safety and content-risk detection API. It scores text, voice, images, video and PDFs for harm across twelve detection types, produces age-appropriate action plans and incident reports, and carries the whole moderation loop — queue, decision, signed receipt — behind the same connection.
What you get
- detect_bullying, detect_grooming, detect_unsafe, analyze and analyse_multi — harassment and gaming toxicity, predatory patterns, self-harm and violence; analyse_multi runs up to ten detection endpoints over one piece of text in a single call
- batch_analyze and analyze_emotions — up to 50 items across all twelve detection types in one request, and emotional and mental-state indicators over a message or a full conversation
- detect_social_engineering, detect_app_fraud, detect_romance_scam, detect_mule_recruitment, detect_gambling_harm, detect_coercive_control, detect_vulnerability_exploitation and detect_radicalisation — the fraud and harm classifiers
- analyze_voice, analyze_image, analyze_video and analyze_document — transcribe and analyse audio, OCR and inspect images, sample video key frames, and run per-page detection over a PDF with chain-of-custody hashing
- detect_synthetic_text, detect_synthetic_image, detect_synthetic_audio and detect_synthetic_video — AI-authorship forensics, including EXIF, C2PA Content Credentials, watermarks and lip-sync correlation
- get_action_plan and generate_report — age-appropriate guidance for a situation, and an incident report built from the conversation
- get_incidents_overview, list_incidents, get_incident, get_incident_trends and moderation_queue — counts by category and severity, a filterable list, one incident's risk trajectory, and the unreviewed triage queue
- review_incident, batch_review_incidents, get_audit_receipt and get_audit_logs — record a moderator decision with a reason code and a signed receipt, apply one decision across many incidents, and query the trail
- create_verification_session, get_verification_session and cancel_verification_session — age or identity verification with document intelligence, face match and liveness
- get_policy, set_policy and the policy-rule tools — per-category flag and block thresholds, and rules that act automatically when a detection matches, dry-runnable before you enable them
- The webhook, usage and threat-intelligence tools — endpoints and their signing secrets, per-tool and per-period usage, real-time quota, and anonymised network-wide threat trends
Requirements
A Tuteliq account. The recommended path is no install at all: point the client at https://api.tuteliq.ai/mcp and sign in through the browser — it implements OAuth 2.1 with dynamic client registration and PKCE, so nothing is pasted into a config file and access is revoked from the dashboard. Headless callers send a static token in the Authorization header instead. Clients that only speak stdio can run npx -y @tuteliq/mcp with TUTELIQ_API_KEY; Node.js 18 or newer.
Setup effort
Paste a URL with a key — add the endpoint to your client
USE CASES
Screening a message before it reaches a child
Run several detectors over the text in one call and act on the verdict.
Triaging a day's flagged content
Read the queue, open the incident with its risk trajectory, and record the decision with a reason code.
Checking whether media is genuine
Run the forensic pipeline over the image or video and read the per-signal breakdown.
Helping a parent respond
Turn the detection into age-appropriate guidance and an incident report they can hand on.
Automating the obvious cases
Set the per-category thresholds, then dry-run a rule against a hypothetical result before enabling it.
75 tools
Listed in project docs. Your client may show a different list.
detect_groomingdetect_unsafeanalyzeanalyse_multibatch_analyzeanalyze_emotionsget_action_plangenerate_reportdetect_social_engineeringdetect_app_frauddetect_romance_scamdetect_mule_recruitmentdetect_gambling_harmdetect_coercive_controldetect_vulnerability_exploitationdetect_radicalisationanalyze_voiceanalyze_imageanalyze_videoanalyze_documentdetect_synthetic_textdetect_synthetic_imagedetect_synthetic_audiodetect_synthetic_videocreate_verification_sessionget_verification_sessioncancel_verification_sessionget_incidents_overviewlist_incidentsget_incidentget_incident_trendsmoderation_queuereview_incidentbatch_review_incidentsget_audit_receiptget_audit_logslist_webhookscreate_webhookupdate_webhookdelete_webhooktest_webhookregenerate_webhook_secretget_pricingget_pricing_detailsget_usage_historyget_usage_by_toolget_usage_monthlyget_usage_summaryget_usage_quotaget_policyset_policylist_policy_rulescreate_policy_ruleget_policy_ruleupdate_policy_ruledelete_policy_ruleevaluate_policy_rulesget_detection_settingsupdate_detection_settingsreset_detection_settingsget_intelligence_trendsget_emerging_threatsget_weekly_digestget_risk_trendsdelete_account_dataexport_account_datarecord_consentget_consent_statuswithdraw_consentrectify_datalog_breachlist_breachesget_breachupdate_breach_statusBEFORE YOU INSTALL
What to know first
Tools you get
75 tools named in the project's documentation.
Requires
- A client that can reach a remote MCP server over HTTP
- TUTELIQ_API_KEY in your environment
INSTALLATION
Connect your client
Add the endpoint to your client.
Server setup
https://api.tuteliq.ai/mcpMCP Clients
Claude Desktop
Settings > Connectors, Add custom connector, name it Tuteliq, URL https://api.tuteliq.ai/mcp, then Connect and approve in the browser
Claude Code, Cursor
{
"mcpServers": {
"tuteliq": {
"type": "http",
"url": "https://api.tuteliq.ai/mcp"
}
}
}Windsurf
{
"mcpServers": {
"tuteliq": {
"type": "http",
"serverUrl": "https://api.tuteliq.ai/mcp"
}
}
}Any MCP client — static token
{
"mcpServers": {
"tuteliq": {
"type": "http",
"url": "https://api.tuteliq.ai/mcp",
"headers": {
"Authorization": "Bearer <YOUR_SECURE_TOKEN>"
}
}
}
}Any MCP client — stdio
{
"mcpServers": {
"tuteliq": {
"command": "npx",
"args": [
"-y",
"@tuteliq/mcp"
],
"env": {
"TUTELIQ_API_KEY": "<YOUR_TUTELIQ_API_KEY>"
}
}
}
}