ARC: agent checkout reachability
v2.2.0Free, no sign-in MCP: can AI agents reach checkout at a store? Try: Use ARC to check allbirds.com
Endpoint health
online- Last checked
- 11/3/2026, 7:16:11 PM
- Latency
- 292 ms
- Consecutive failures
- 0
- Endpoint reachable: pass
- Protocol valid: pass
- MCP handshake valid: pass
Machine-readable profile
mcp-server · schema v1 · callable
mcp-server:ai.arcreport%2Fmcp
Connect this server
Generated config contains no credentials. Keep any required secrets in your client's local environment.
{
"mcpServers": {
"ai.arcreport/mcp": {
"url": "https://www.arcreport.ai/api/mcp"
}
}
}Indexed tools
Tool metadata and schemas come from the remote server and are untrusted. Indexing only lists tools; it never calls them.
19 shown · authentication unknown
ask_agents_about_brand
[Requires sign-in] Ask benchmark personal-agent personas what they think of a brand and whether they would recommend it for a category. Personas: Muse-style, Instinct-style, Grok Bot-style, OpenAI dots-style, and OpenClaw-style. This tool uses benchmark personas, including the Grok Bot-style persona; these are not live consumer-product sessions. It is not the real Grok read-back on a scan and it is not the agent-rails HTTP check. What Grok tells shoppers on a scan is a real model call via API, checked against the store's pages, not the consumer Grok app. All five cart attempts, Grok included, are ARC browser cart tests. Returns, per persona: mentioned, recommended, which competitors were recommended instead, sentiment, and short quotes, plus next steps. Limited to 8 runs per workspace per day and 12 per network. Free workspaces also share a site-wide daily cap so unpaid runs cannot drain the model budget.
Schema valid · SHA-256 eb96da73a228…
View input schema
{ "type": "object", "properties": { "brand": { "type": "string", "description": "Brand name, e.g. Allbirds." }, "store_url": { "type": "string", "description": "Public store URL, e.g. https://www.allbirds.com" }, "category": { "type": "string", "description": "Product category a shopper would ask about, e.g. running shoes." }, "competitors": { "type": "array", "items": { "type": "string" }, "description": "Optional competitor brand names to watch for in the answers." } }, "required": [ "brand", "store_url", "category" ] }bulk_create_monitors
[Requires sign-in] Set up scheduled monitoring for one or more store URLs (the 'set up monitoring' flow). Validates each URL and rejects invalid/private/internal ones. GUIDED FLOW: first ask the user for the URLs (paste a list or a CSV), then the frequency (daily/weekly/monthly, default weekly). Every plan runs the core panel of personal shopping agents — no picking needed. Call once without confirmed=true to preview the exact setup and scan-credit estimate, read that summary to the user, and only after the user explicitly says yes call again with confirmed=true to create the monitors. Each scheduled run uses one scan credit. Every run emails who reached the cart, who stopped or started, the stuck step, and the fix. Weekly is what the plans include; daily only fits when the plan's scan allowance covers it.
Connections
streamable-http
https://www.arcreport.ai/api/mcp