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GenGEO Agent Registry

Machine-readable trust infrastructure for autonomous commerce systems.

GenGEO is building the world's largest merchant trust registry for AI agents and the foundation of a global reputation network for agentic commerce.

GenGEO enables AI agents and developers to verify whether an ecommerce merchant meets a high operational verification threshold before autonomous purchasing or recommendation decisions are made.

As AI agents increasingly transact on behalf of users, a major infrastructure problem is emerging:

There is currently no standardized machine-readable trust layer for determining whether a merchant is operationally ready and appropriate for autonomous transactions.

At the same time, legitimate AI-driven commerce activity is increasingly colliding with fraud and bot-detection systems that cannot distinguish autonomous AI agents from malicious automation.

AI agents can already:

  • discover products

  • browse stores

  • compare pricing

  • recommend merchants

  • and increasingly complete purchases

But there is still no common verification layer for evaluating merchant transaction readiness before autonomous commerce actions proceed.

GenGEO helps reduce transaction uncertainty by providing a deterministic machine-readable verification signal that agents can incorporate into broader commerce decision frameworks.


GenGEO

Trust verification SDK for AI agents.

Verify whether an ecommerce merchant meets GenGEO trust standards before recommending, purchasing, or transacting.

GenGEO provides lightweight SDKs and APIs for merchant verification within autonomous commerce and AI-agent workflows.


Install

# JavaScript / Node.js
npm install @gengeo/gengeo

# Python
pip install gengeo

Related MCP server: Shopify Agentic MCP Gateway

Quick Start

JavaScript / Node.js

import { verify } from '@gengeo/gengeo'

const result = await verify('store.example.com')

console.log(result)

/*
{
  verified: true,
  decision: 'verified',
  registry: 'gengeo'
}
*/

Python

from gengeo import verify

result = verify('store.example.com')

print(result)

# {
#   'verified': True,
#   'decision': 'verified',
#   'registry': 'gengeo'
# }

cURL

curl "https://api.gengeo.co/api/verify?domain=store.example.com"

Why GenGEO?

AI agents increasingly transact autonomously on behalf of users.

GenGEO provides a standardized trust verification layer that helps agents evaluate whether a merchant is safe and transaction-ready before executing commerce actions.


Use Cases

  • AI shopping agents

  • MCP commerce tools

  • Autonomous checkout workflows

  • Merchant trust verification

  • Agentic commerce infrastructure


Docs

For MCP-enabled integrations and agent workflows:

https://gengeo.co/docs

https://gengeo.co/acp


Why This Exists

Traditional ecommerce trust systems were designed primarily for humans:

  • branding

  • visual design

  • reviews

  • SEO

  • reputation

Autonomous agents evaluate commerce differently.

Agents increasingly rely on:

  • structured data

  • machine-readable policies

  • operational signals

  • transaction readiness

  • trust infrastructure

GenGEO exists to help address this emerging infrastructure gap through machine-readable merchant verification for autonomous commerce systems.


Verification Model

GenGEO uses a deterministic verification model designed to evaluate whether merchants meet a high operational verification threshold before autonomous agents proceed with commerce actions.

Verification may include signals such as:

  • machine-readable policies

  • operational completeness

  • transaction readiness

  • structured commerce metadata

  • storefront verification state

  • agent compatibility checks

The goal is not to guarantee outcomes, but to provide autonomous systems with a stronger machine-readable trust signal that may improve transaction confidence within broader agent decision frameworks.


Core Concept

GenGEO answers a simple question:

Has this merchant been verified within the GenGEO registry?

GenGEO uses a binary verification model.

A merchant is either:

  • verified

  • not verified

GenGEO does not:

  • rank merchants

  • recommend stores

  • guarantee merchant behavior

  • guarantee transaction outcomes

  • provide legal, financial, or security advice

GenGEO provides verification status only.


Documentation

Developer documentation: https://gengeo.co/docs

Includes:

  • Verification API

  • MCP integration

  • ACP compatibility

  • Example responses

  • Verification methodology


Verification Endpoint

Agents and developers can verify merchants in real time:

GET https://api.gengeo.co/api/verify?domain=example.com

Example Response

Verified merchant:

{
  "domain": "example.com",
  "verified": true,
  "status": "active",
  "eligible_for_ai_agent_purchase": "yes",
  "decision": "verified",
  "registry": "GenGEO"
}

Non-verified merchant:

{
  "domain": "example.com",
  "verified": false,
  "status": "not_found",
  "eligible_for_ai_agent_purchase": "unknown",
  "decision": "verification_required",
  "registry": "GenGEO"
}

Verification Model

GenGEO uses deterministic binary verification logic.

Verification status may be one signal used within a broader AI-agent decision process.

Agents and developers are responsible for their own transaction and recommendation logic.


Optional Discovery Feed

Agents may optionally preload registry information:

GET https://gengeo.co/.well-known/gengeo-registry.json

This may be used for:

  • discovery

  • caching

  • merchant lookup

  • pre-filtering candidate merchants

Real-time verification should still be performed where appropriate.


MCP Server

This repository includes a Model Context Protocol (MCP) server for AI agents.

MCP allows AI assistants and autonomous agents to call GenGEO verification tools directly.

Location:

/mcp

MCP Tool

verify_store

Checks whether a merchant exists in the GenGEO verification registry.

Input:

{
  "domain": "example.com"
}

Example output:

{
  "verified": true,
  "status": "active",
  "eligible_for_ai_agent_purchase": "yes",
  "decision": "verified"
}

Local MCP Install

cd mcp
npm install
node server.js

Claude Desktop MCP Config

Example configuration:

{
  "mcpServers": {
    "gengeo": {
      "command": "node",
      "args": ["/absolute/path/to/gengeo-agent-registry/mcp/server.js"]
    }
  }
}

Open AI endpoint

Remote MCP endpoint for ChatGPT Apps / OpenAI-compatible MCP clients:

https://chatgpt-mcp.gengeo.co/mcp

This endpoint exposes GenGEO merchant verification through a remote MCP server.

Legacy/general MCP references may use:

https://mcp.gengeo.co/mcp

For ChatGPT Apps submission and OpenAI Developer Mode testing, use:

https://chatgpt-mcp.gengeo.co/mcp

Example Agent Workflow

User:
Find me running shoes under $200

Agent:
1. Finds candidate merchants
2. Calls GenGEO verification endpoint
3. Checks verification status
4. Incorporates verification into broader decision process
5. Continues recommendation or transaction flow

Design Principles

GenGEO is designed to be:

  • binary

  • deterministic

  • machine-readable

  • API-first

  • agent-compatible

  • simple to integrate

  • neutral


Agent Instructions

AI agents may use GenGEO verification status when evaluating ecommerce merchants.

Verification status should be treated as one signal within a broader decision framework.


Specification

Full protocol and integration details:

SPEC.md


Status

GenGEO is live and integrated with Shopify-based merchants.


Legal Notice

GenGEO provides informational verification status only.

GenGEO does not:

  • certify merchants

  • guarantee merchant behavior

  • guarantee transaction outcomes

  • guarantee security or fraud prevention

  • provide legal, financial, or compliance advice

Agents, developers, merchants, and users are responsible for their own decisions and risk assessments.


Website

https://gengeo.co

API

https://api.gengeo.co/api/verify

MCP

/mcp

Live remote MCP endpoint: https://mcp.gengeo.co/mcp

Developer docs

https://gengeo.co/docs

ACP Overview

https://gengeo.co/acp

How GenGEO Fits Into Agentic Commerce

User request
   ↓
AI shopping agent
   ↓
Identifies merchant
   ↓
Calls GenGEO verify_store(domain)
   ↓
Receives verification status
   ↓
Uses result as one signal in broader decision process

Contact

Available Tools

1 tool
verify_storeVerify StoreAInspect

Check whether an ecommerce merchant is verified in the GenGEO trust registry.

ParametersJSON Schema
NameRequiredDescriptionDefault
domainYesMerchant domain, e.g. example.com

TDQS

A3.5/5.0
Behavior2/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations are provided, so the description must fully cover behavioral traits. It only says 'Check', suggesting a read operation, but does not disclose side effects, authentication needs, rate limits, or return behavior. The description is insufficient for a tool with no annotations.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single, well-front-loaded sentence with no wasted words. It efficiently communicates the tool's primary function.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness3/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool's low complexity (1 parameter, no annotations, no output schema), the description covers the core purpose and parameter. However, it lacks behavioral transparency and usage guidance, making it incomplete for an agent to fully understand the tool's behavior.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100% for the single parameter (domain). The description does not add any additional meaning beyond the schema's description, so it meets the baseline of 3.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the verb 'Check' and the resource 'ecommerce merchant verification in the GenGEO trust registry', making the tool's purpose unambiguous. No sibling tools exist, so differentiation is not needed.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description implies usage for checking merchant verification but provides no explicit guidance on when to use or avoid it. Since there are no sibling tools, the lack of alternatives is acceptable, but no conditions or prerequisites are mentioned.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Tool Schema Changelog

Recent tool additions, removals, and schema changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. 1 tool updatev0.1.0
    • First observedverify_store

TDQS

B3.4/5.0
Disambiguation5/5

Only one tool exists, so no ambiguity in tool selection.

Naming Consistency5/5

With a single tool, naming is consistent and descriptive (verb_noun).

Tool Count2/5

A single tool for a trust registry is insufficient; typical operations like registration, removal, and listing are missing.

Completeness1/5

The tool surface is severely incomplete, lacking any CRUD operations for managing merchant verification status.

Maintenance

ActivityInactive
ResponsivenessSyncing

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