Offendersearch
AI integration

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.