MCP Server

The Tuteliq MCP server (@tuteliq/mcp v3.7.0) exposes child safety detection as tools for AI assistants that support the Model Context Protocol — including Claude Desktop, Cursor, Windsurf, and other MCP-compatible clients.

Setup

Claude Desktop (Recommended)

Claude Desktop connects to Tuteliq via Streamable HTTP — no npm install required.

Generate a Secure Token

Go to your Tuteliq Dashboard, navigate to Settings > Plugins, and generate a Secure Token. This token authenticates the MCP connection.

Add the connector in Claude Desktop

Open Claude Desktop and go to Settings > Connectors
Click Add custom connector
Set the name to Tuteliq and the URL to:
https://api.tuteliq.ai/mcp

Connect and authenticate

Click Connect. When prompted, enter the Secure Token you generated in Step 1.That’s it — Tuteliq tools will be available in your next conversation.

Cursor

Add to your Cursor MCP settings:

{
  "mcpServers": {
    "tuteliq": {
      "url": "https://api.tuteliq.ai/mcp",
      "headers": {
        "Authorization": "Bearer your-api-key"
      }
    }
  }
}

Claude Code

Add to your project’s .mcp.json:

{
  "mcpServers": {
    "tuteliq": {
      "url": "https://api.tuteliq.ai/mcp",
      "headers": {
        "Authorization": "Bearer your-api-key"
      }
    }
  }
}

Other MCP clients (npx / stdio)

For clients that only support stdio transport:

npm install -g @tuteliq/mcp

{
  "mcpServers": {
    "tuteliq": {
      "command": "tuteliq-mcp",
      "env": {
        "TUTELIQ_API_KEY": "your_api_key"
      }
    }
  }
}

Available tools

Once configured, the following tools are available to the AI assistant:

Detection tools

All detection tools accept a common set of parameters:

Parameter	Type	Required	Description
`content`	`string`	Yes	Text content to analyze
`context`	`object`	No	Analysis context — see Context fields below
`include_evidence`	`boolean`	No	When `true`, returns supporting evidence excerpts with flagged phrases and weights
`support_threshold`	`string`	No	Minimum severity to include crisis helplines. Values: `low`, `medium`, `high` (default), `critical`. Critical severity always includes support resources regardless of this setting.
`external_id`	`string`	No	Your external tracking ID (echoed in response)
`customer_id`	`string`	No	Your customer identifier (echoed in response)

Tool	Description
`detect_unsafe`	Detect harmful content across all nine KOSA categories
`detect_bullying`	Detect bullying and harassment patterns
`detect_grooming`	Detect grooming patterns in conversations
`detect_social_engineering`	Detect social engineering tactics (pretexting, impersonation, urgency manipulation)
`detect_app_fraud`	Detect app-based fraud (fake investments, phishing, subscription traps)
`detect_romance_scam`	Detect romance scam patterns (love-bombing, financial requests, identity deception)
`detect_mule_recruitment`	Detect money mule recruitment (easy-money offers, account sharing)
`detect_gambling_harm`	Detect gambling harm indicators (chasing losses, concealment, underage gambling)
`detect_coercive_control`	Detect coercive control patterns (isolation, financial control, surveillance)
`detect_vulnerability_exploitation`	Detect exploitation targeting vulnerable individuals
`detect_radicalisation`	Detect radicalisation indicators (extremist rhetoric, recruitment patterns)

Multi-endpoint analysis

analyse_multi runs up to 10 detections on a single text in one call.

Parameter	Type	Required	Description
`content`	`string`	Yes	Text content to analyze
`endpoints`	`string[]`	Yes	List of endpoint IDs to run (see table below)
`context`	`object`	No	Analysis context — see Context fields
`include_evidence`	`boolean`	No	Include supporting evidence in each result
`support_threshold`	`string`	No	Crisis helpline threshold (`low` / `medium` / `high` / `critical`)

Valid endpoint values for analyse_multi:

Endpoint ID	Classifier
`bullying`	Bullying & Harassment
`grooming`	Grooming Detection
`unsafe`	Unsafe Content (KOSA categories)
`social-engineering`	Social Engineering
`app-fraud`	App-based Fraud
`romance-scam`	Romance Scam
`mule-recruitment`	Mule Recruitment
`gambling-harm`	Gambling Harm
`coercive-control`	Coercive Control
`vulnerability-exploitation`	Vulnerability Exploitation
`radicalisation`	Radicalisation

When vulnerability-exploitation is included, its cross-endpoint modifier automatically adjusts severity scores across all other results — amplifying risk when the content targets vulnerable individuals.

Media analysis tools

Tool	Parameters	Description
`analyze_voice`	`file_path`, `age_group`	Transcribe and analyze audio files for safety
`analyze_image`	`file_path`, `age_group`	Analyze image files for visual content risks
`analyze_video`	`file_path`, `age_group`	Analyze video files with per-frame safety findings

Guidance & reporting tools

Tool	Parameters	Description
`analyze_emotions`	`content`, `context`	Analyze emotional well-being from text
`get_action_plan`	`detection_result`, `audience`	Generate age-appropriate guidance (`child`, `parent`, `professional`)
`generate_report`	`messages`, `childAge`, `incidentType`	Create structured incident reports for law enforcement or safeguarding teams

Context fields

Pass a context object with any detection tool to improve accuracy:

Field	Type	Effect
`ageGroup` / `age_group`	`string`	Triggers age-calibrated scoring. Values: `"under 10"`, `"10-12"`, `"13-15"`, `"16-17"`, `"under 18"`
`language`	`string`	ISO 639-1 code (e.g., `"en"`, `"de"`, `"sv"`). Auto-detected if omitted.
`platform`	`string`	Platform name (e.g., `"Discord"`, `"Roblox"`, `"WhatsApp"`). Adjusts for platform-specific norms.
`conversation_history`	`array`	Prior messages for context-aware analysis. Returns per-message `message_analysis`.
`sender_trust`	`string`	`"verified"`, `"trusted"`, or `"unknown"`.
`sender_name`	`string`	Sender identifier (used with `sender_trust`).
`country`	`string`	ISO 3166-1 alpha-2 code (e.g., `"GB"`, `"US"`, `"SE"`). Enables geo-localised crisis helpline data. Falls back to user profile country if omitted.

When sender_trust is "verified", the API fully suppresses AUTH_IMPERSONATION — a verified sender cannot be impersonating an authority by definition. Routine urgency (schedules, deadlines) is also suppressed. Only genuinely malicious content (credential theft, phishing links, financial demands) will be flagged.

Example usage

Once the MCP server is running, you can ask your AI assistant to use Tuteliq tools directly in conversation:

“Check this message for safety: ‘Let’s meet at the park after school, don’t tell your parents’ — the user is 10-12 years old”

The assistant will call detect_unsafe and return the full safety analysis including severity, categories, risk score, and rationale.

“Analyze this conversation for grooming patterns” (with a conversation pasted or in a file)

The assistant will call detect_grooming and provide a detailed breakdown of any detected grooming stages.

“Check this message for social engineering: ‘If you really trusted me you’d send your address’”

The assistant will call detect_social_engineering and return whether manipulation tactics were detected.

“Run grooming, romance scam, and social engineering detection on this message”

The assistant will call analyse_multi with all three endpoints and return combined results with an overall risk level.

“Analyze this video for safety concerns” (with a file path)

The assistant will call analyze_video and return frame-by-frame safety findings.

Resources

The MCP server also exposes resources for context:

Resource	Description
`tuteliq://kosa-categories`	List of all nine KOSA harm categories
`tuteliq://age-groups`	Available age group brackets and their calibration
`tuteliq://credit-costs`	Per-endpoint credit costs

Stateless by Design

Tuteliq is fully stateless — no conversation text, context, or session state is retained between requests. This is a deliberate privacy-by-design decision. When processing messages from minors under GDPR/COPPA, the safest data is data you never store. Each request arrives, is analyzed, and the content is discarded. Pass context (age_group, platform, conversation_history) with every call that needs it.

You: "Check this message for grooming. The user is 13-15 on Discord: 'Hey, you seem really mature for your age'"
→ Tuteliq analyzes with age-calibrated scoring — no data retained

You: "Now check this follow-up with the previous message as context: 'Don't tell your parents about our chats'"
→ Pass conversation_history in context — Tuteliq detects escalation patterns across the full conversation

Privacy-first: No session store to breach, no conversation cache to leak, no accumulated history to subpoena. Your integration handles context; Tuteliq handles detection.

Sandbox mode

For testing without consuming credits, use a sandbox API key. Create one in your Dashboard under Settings > API Keys > Environment: Sandbox. Sandbox keys:

Run real analysis (not mocked) so you can validate integration behavior
Don’t consume credits — credits_used is always 0
Have a daily limit of 50 calls and 10 requests per minute
Return "sandbox": true in every response

Sandbox mode is for integration testing only. For production use, switch to a production API key.

Error handling

If the API key is invalid or credits are exhausted, the tool will return a structured error message that the AI assistant can interpret and relay to the user.

Configuration options

Environment variables:

Variable	Description	Default
`TUTELIQ_API_KEY`	Your Tuteliq API key	Required
`TUTELIQ_BASE_URL`	API base URL	`https://api.tuteliq.ai`
`TUTELIQ_TIMEOUT`	Request timeout in ms	`30000`

Setup

Claude Desktop (Recommended)

Cursor

Claude Code

Other MCP clients (npx / stdio)

Available tools

Detection tools

Multi-endpoint analysis

Media analysis tools

Guidance & reporting tools

Context fields

Example usage

Resources

Stateless by Design

Sandbox mode

Error handling

Configuration options

Next steps

API Reference

CLI

​Setup

​Claude Desktop (Recommended)

​Cursor

​Claude Code

​Other MCP clients (npx / stdio)

​Available tools

​Detection tools

​Multi-endpoint analysis

​Media analysis tools

​Guidance & reporting tools

​Context fields

​Example usage

​Resources

​Stateless by Design

​Sandbox mode

​Error handling

​Configuration options

​Next steps

API Reference

CLI

Setup

Claude Desktop (Recommended)

Cursor

Claude Code

Other MCP clients (npx / stdio)

Available tools

Detection tools

Multi-endpoint analysis

Media analysis tools

Guidance & reporting tools

Context fields

Example usage

Resources

Stateless by Design

Sandbox mode

Error handling

Configuration options

Next steps