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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?

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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

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Table of Contents (13 Chapters)

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Part I: Protocol Overview

  1. Why AI Agents Need a Trust Layer — The trust dilemma, shortcomings of existing mechanisms, design philosophy
  2. OTR Architecture Overview — Four-layer architecture, data flow, site type adaptation

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Part II: Scoring Methodology

  1. V/S Dimensions — Verification and Security — 13 verification signals + 15 security signals
  2. G/T/D Dimensions — Governance, Transparency, and Data Quality — 10 + 18 + 21 signals explained in detail
  3. Scoring Engine — COLD Mode — Weight formula, badge tiers, score lifecycle

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Part III: API Reference

  1. REST API Reference — Endpoints, request formats, response structures, integration examples
  2. MCP Server — Enabling AI agents to query OTR trust data directly
  3. OpenAPI Specification — OpenAPI 3.0 spec document and SDK generation

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Part IV: Integration Guide

  1. How AI Agents Call OTR — Recommendation decision flow, caching strategies
  2. How Merchants Can Improve Their Score — Dimension-by-dimension optimization guide
  3. OTR-ID Lifecycle — Domain identifier creation and usage

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Part V: Case Studies and Reference

  1. Case Studies — AI recommendations, supply chain assessment, brand monitoring
  2. Reference Manual — Signal catalog, weight tables, error codes

Look Up a Trust Score

Enter any domain to view its six-dimension trust score and signal breakdown.