ARC MCP server

• Registry nameai.arcreport/askverified in the MCP registry

The registry name sits under arcreport.ai. The registry only lets a publisher use that namespace after proving control of the domain through DNS or HTTP.

• Stateanswerschecked 2026-10-27
• Tools16
• Server calls itselfARC ask — public Index read
• Server version1.0.0
• Protocol version2025-06-18

On 2026-10-27 the server answered initialize and tools/list without a sign-in. First answer in 115 ms.

• Whether its tools do the product's job has not been judged yet.
• Checked on 1 of the 7 days it takes to judge reliability.

Said by rules from the measurements above. The likely first call is a model's pick from the tool list; read its description before relying on it.

• INITE MCP scoreafter a weeka score needs 7 daily checks: 1 so far
• Answeredmeasuring1 of the 7 daily checks it takes to print a share
• Median first answer132 ms
• Space in an agent's context≈ 2,868 tokensthe definitions of all its tools (16), which an agent carries on every turn
• Tool definitionsA · 91 of 100
• Typed results0 of 16 toolsdeclared outputSchema: results come typed, read once

• Parameters without a type: in 1 of 16
• Parameters without a description: in 4 of 16
• Not saying whether it only reads or can destroy: 4 of 16

A program checks the server once a day: initialize and tools/list, no model. Tokens are the definitions' length at about four characters a token. The grade averages six checks of every tool's name, description, schema and annotations; it says how well the tools are described, not whether they work. The score weighs what was measured and nothing else: answering (25%), doing the product's job (25%), definitions (20%), context (10%), use (10%) and how readily a client connects (10%). A part that was not measured is left out, and a hidden instruction caps the score at 40. The state of vendors' MCP servers: method and numbers →

• precheck

  Check if an AI agent can reach checkout on a store before trying. Saved automated HTTP check for a product or store URL. Uses ARC's saved directory, rails, store answers and check history. When those have no row, uses the plain-HTTP…

• find_agent_ready_stores

  Find stores where a saved check shows an AI agent can reach checkout, for running shoes, beauty, clothing, home goods, or another product need. Reads real HTTP agent-catalog checks from the last thirty days whose catalog found a product…

• can_agent_checkout

  Call when you need a saved checkout-link check for any public store URL, not only Index brands. Saved HTTP checkout check for a public homepage or product URL: catalog, product, checkout link, UCP profile, blocks, and one fix. No login…

• ask_arc

  Read whether an AI agent can reach checkout for a brand the ARC Index already lists. No login and no new scan. Pass a brand, URL, or category. Returns the score, evidence, and both lanes: the agent-catalog HTTP check and the browser cart…

• agent_commerce_adoption

  Read ARC's agent commerce adoption counts, category and commerce platform rates from saved HTTP checks, including Checkout WebMCP among measured Shopify checkouts, with unknown and not checked separate; shopper approval required. Curated…

• product_data_check

  Free latest saved product data and return/shipping policy check from the store's pages and structured data. Reads history only; no store fetch or model call. A missing result is not a zero.

• google_checkout_readiness

  Read saved public Google agentic checkout readiness checks for a store. Free; no new scan or paid calls. Merchant Center feed eligibility cannot be checked from outside. Not Google approval or certification.

• get_agent_facts

  Read free ARC agent facts for a store or product with no login. Returns TypeSafe Jev typed decisions (use-case mention, price clarity, agent-friendly description, cart-blocker suspicion, readiness score) from an HTTP scrape. Not a browser…

• list_category_agent_facts

  List free ARC agent facts by category (beauty, footwear, apparel, …) with no login. Ranked by newest real Jev scores. JSON: GET /api/agent-facts?category=beauty

• dataset_info

  Latest open ARC dataset snapshot (start with store_answers: one plain answer per store on whether AI agents can find products and get a checkout link): immutable versioned download URLs plus latest/ aliases (CSV and Parquet), row counts…

• protocol_matrix

  Read ARC's neutral Protocol Matrix: for UCP, ACP, PAP, WebMCP, MCP and agent payment protocols, how many checked stores declare, validate, answer and transact, with evidence labels (Measured / Inferred / Not measured). Saved checks only…

• agent_leaderboard

  Read ARC's open agent leaderboard: cart success over the fixed ARC-100 v1 test set for each agent path ARC measures (ARC browser shoppers A-E, the UCP path, the storefront MCP path), with n, dates, evidence labels and Wilson 95%…

• submit_leaderboard_run

  Submit your agent's run against ARC-100 v1 to ARC's open leaderboard. Free. The run is listed as 'unverified' until ARC re-runs a sample. Format: arc-leaderboard-submission/v1 (see /leaderboard/spec). Runs must stop before payment. Limit…

• submit_store

  Add stores to ARC's free, open agent-commerce directory (CC-BY dataset). Pass url for one store or hosts for up to 50. Each new host is validated (live HTTPS, not parked, not redirecting to another brand); accepted hosts are queued for…

• store_card

  Read a public store card and saved brand claim. No outbound fetch or model calls. Brand statements are from the brand and not measured by ARC; claims never change scores.

• validate_ucp_profile

  Validate a Universal Commerce Protocol (UCP) business profile against the published UCP JSON Schema (current release) and spec rules: authority binding, https endpoints, orphaned extensions and, for a URL, hosting headers (no redirects…

• ai.arcreport/mcpanswers, 19 toolsverified in the MCP registry