Offender safety checks for AI agents and chatbots
Give any LLM a reliable sex-offender screening tool. Define one tool call — or, in private beta, connect the MCP server — and your agent can search all 58 US registries and cite its sources.
One tool definition
Drop this tool schema into your agent framework. The handler calls POST /v1/search; the model gets back scored, source-tagged records.
- Works with any tool-calling model
- Or skip the glue code with the MCP server (private beta)
- Verifiable results the agent can cite
{
"name": "search_sex_offender_registry",
"description": "Search US sex-offender registries by name, DOB, or location.",
"input_schema": {
"type": "object",
"properties": {
"firstName": { "type": "string" },
"lastName": { "type": "string" },
"dob": { "type": "string", "format": "date" },
"state": { "type": "string" }
},
"required": ["lastName"]
}
}AI integration FAQ
How do I give an LLM access to registry data?
Define a tool that calls POST /v1/search and hand it to your model. The model receives scored, source-tagged records it can cite. An MCP server that removes the glue code is in private beta.
Can I build a safety-alert workflow?
Yes. Re-run searches against the continuously-updated corpus — and add a live block to re-verify a specific person at the source — then have your agent flag new matches. A common pattern for trust & safety and continuous monitoring.
Does the model see reliable, verifiable data?
Every record includes a confidence score, the match basis, and a source citation, so agents can present verifiable answers rather than guesses.