Let an AI Agent Run Your Store

By the end of this guide an AI agent, running in your terminal or inside Claude Desktop / Cursor, can create checkouts, check payment status, and manage subscriptions for your store, in plain language instead of code you write yourself. There are two ways in, and you can set up both, they don’t conflict:

Coding agents

Claude Code, Cursor’s agent mode, or anything else that can run shell commands. Give it the SKILL.md prompt and the agenta CLI directly.

Chat agents

Claude Desktop, Cursor’s chat, Windsurf, or any MCP-compatible client. Connect the AgentaOS MCP server instead.

Before you start

An AgentaOS account. Neither path needs an API key up front, agenta login and the MCP server’s gateway key both come from the same dashboard you’d use anyway.

Path A: a coding agent with the CLI

Install the CLI and log in

curl -fsSL https://agentaos.ai/install | bash
agenta login

agenta login is the one step that needs you: it opens a browser for sign-in. Every other command an agent runs afterward is non-interactive.

Give your agent the skill

Tell your coding agent to fetch and follow the skill prompt:

curl -fsSL https://agentaos.ai/SKILL.md

It teaches the agent the exact agenta pay commands, their flags, and how to handle the response, including always running agenta status --json first and always passing --json so output is machine-parseable instead of full of ANSI color codes.

Try a prompt

Ask it something like:

Create a checkout for 50 EUR for my consulting invoice.

The agent runs agenta status --json to confirm payment tools are ready, then agenta pay checkout -a 50 --json, and hands you back the checkoutUrl to share with your client.

Path B: a chat agent with MCP

Get your keys

A gateway key for the payment tools, from app.agentaos.ai → Settings → Developers → API Keys.

Add the server to your client's config

For Claude Desktop, add this to your MCP server configuration:

{
  "mcpServers": {
    "agenta": {
      "command": "npx",
      "args": ["-y", "agentaos"],
      "env": {
        "AGENTAOS_GATEWAY_KEY": "sk_test_your-gateway-key"
      }
    }
  }
}

See MCP setup for Cursor and Windsurf equivalents.

Restart your client and ask it directly

List my last 5 checkouts.

Cancel subscription sub_4f81c0 at the end of the current period.

The agent calls the matching tool, agenta_pay_list_checkouts or agenta_pay_cancel_subscription, and reports back in the chat.

What the agent can do

The MCP server exposes these payment tools, all documented at MCP setup:

Tool	What it does
`agenta_pay_create_checkout`	Create a checkout session and get a shareable `checkoutUrl`.
`agenta_pay_get_checkout`	Get a checkout’s status, amount, currency, and expiry. Use it to check whether a payment completed.
`agenta_pay_list_checkouts`	List checkout sessions, filterable by status (`open`, `completed`, `expired`, `cancelled`).
`agenta_pay_list_subscriptions`	List subscriptions: status, amount, billing interval, and subscriber.
`agenta_pay_cancel_subscription`	Cancel a subscription. Defaults to cancel-at-period-end; pass `atPeriodEnd: false` to cancel immediately.
`agenta_pay_list_customers`	List the customers who have paid you: email, name, country, and ID.
`agenta_pay_send_receipt`	Re-send the receipt email for a paid invoice to the buyer on file.

Both paths use the same underlying API and respect the same test/live key scoping. An agent working against a test key can only see and create test-mode data, exactly like a human integration would.

Verify it worked

Ask the agent directly:

What’s my AgentaOS status?

On the CLI path, it should run agenta status --json and report paymentTools.ready: true, along with your organization. On the MCP path, ask it to list your most recent checkout and confirm the amount and status match what you see in the dashboard.

Next steps

MCP server setup

Every tool the MCP server exposes, and Cursor/Windsurf configuration.

CLI install

The full agenta CLI reference, for scripting outside an AI agent too.

Handle webhooks

Checkouts an agent creates still need your server to fulfil them on completion.

Coding agents

Chat agents

​Before you start

​Path A: a coding agent with the CLI

​Path B: a chat agent with MCP

​What the agent can do

​Verify it worked

​Next steps

MCP server setup

CLI install

Handle webhooks

Before you start

Path A: a coding agent with the CLI

Path B: a chat agent with MCP

What the agent can do

Verify it worked

Next steps