> ## Documentation Index
> Fetch the complete documentation index at: https://learn.orbexa.io/llms.txt
> Use this file to discover all available pages before exploring further.

# OTR Open Trust Registry

> The trust layer for agentic commerce — OTR five-dimension scoring methodology, 77 signal references, REST API documentation, and integration guide.

# OTR Protocol — Open Trust Registry

> "How Trust Works in Agentic Commerce"

OTR (Open Trust Registry) is an open trust assessment protocol designed to answer the most fundamental question in agentic commerce: **How does an AI agent determine whether a merchant is trustworthy?**

## What This Book Covers

OTR collects publicly verifiable signals — SSL certificates, DNS security records, corporate registrations, policy quality, structured data, and more — to compute a 0-100 trust score that helps AI agents make informed recommendation decisions.

This book is the complete technical documentation for the OTR protocol. You can use this knowledge to:

* **Understand the trust scoring logic** and systematically improve your domain's score
* **Integrate trust data via API** into your own applications
* **Build your own trust assessment system** based on the open-source protocol

Open-source repository: [github.com/yb48666-ctrl/OTR-Protocol-by-orbexa](https://github.com/yb48666-ctrl/OTR-Protocol-by-orbexa)

## Table of Contents (13 Chapters)

### Part I: Protocol Overview

1. [Why AI Agents Need a Trust Layer](/en/book-2/ch1-why-trust-layer) — The trust dilemma, shortcomings of existing mechanisms, design philosophy
2. [OTR Architecture Overview](/en/book-2/ch2-architecture) — Four-layer architecture, data flow, site type adaptation

### Part II: Scoring Methodology

3. [V/S Dimensions — Verification and Security](/en/book-2/ch3-verification-signals) — 13 verification signals + 15 security signals
4. [G/T/D Dimensions — Governance, Transparency, and Data Quality](/en/book-2/ch4-governance-transparency-data) — 10 + 18 + 21 signals explained in detail
5. [Scoring Engine — COLD Mode](/en/book-2/ch5-scoring-engine) — Weight formula, badge tiers, score lifecycle

### Part III: API Reference

6. [REST API Reference](/en/book-2/ch6-rest-api) — Endpoints, request formats, response structures, integration examples
7. [MCP Server](/en/book-2/ch7-mcp-server) — Enabling AI agents to query OTR trust data directly
8. [OpenAPI Specification](/en/book-2/ch8-openapi-spec) — OpenAPI 3.0 spec document and SDK generation

### Part IV: Integration Guide

9. [How AI Agents Call OTR](/en/book-2/ch9-agent-integration) — Recommendation decision flow, caching strategies
10. [How Merchants Can Improve Their Score](/en/book-2/ch10-improve-score) — Dimension-by-dimension optimization guide
11. [OTR-ID Lifecycle](/en/book-2/ch11-otr-id) — Domain identifier creation and usage

### Part V: Case Studies and Reference

12. [Case Studies](/en/book-2/ch12-case-studies) — AI recommendations, supply chain assessment, brand monitoring
13. [Reference Manual](/en/book-2/ch13-reference) — Signal catalog, weight tables, error codes

<Card title="Look Up a Trust Score" icon="magnifying-glass" href="https://orbexa.io/verify">
  Enter any domain to view its six-dimension trust score and signal breakdown.
</Card>