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ROAS Expert free AI SEO audit

by com.roasexpert.tools
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Check whether an AI shopping agent can buy from a store

check_agent_readiness
Read-onlyIdempotent

Acts like an AI shopping agent on an online store and tests three stages: find (homepage answers, robots.txt lets shopping bots in, product feed, llms.txt), read (a product page with price, currency and stock in structured data, text readable without JavaScript, shipping and returns) and buy (product JSON, cart and checkout reachable without a login). Returns a score out of 100, a verdict (ready, partly, not), every check with its status, detail and fix, the access table for eight shopping and answer bots, and the product that was read. Read-only: about ten GET requests to public URLs, nothing is added to a cart. Shopify and WooCommerce get the full set of checks. Counts against the free daily limit unless the store was checked in the last ten minutes.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "url": "<url>"
    },
    "name": "check_agent_readiness"
  }
}

Input Schema

NameRequiredDescriptionDefault
urlYesStore domain or homepage URL, for example store.com

Schema Changelog

Changes observed during successful MCP inspections.

  1. Added

TDQS

A4/5.0
Behavior5/5

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

Annotations already declare read-only, idempotent, open-world, non-destructive; the description adds real operational context on top: about ten GET requests to public URLs, nothing added to a cart, quota consumption, and the ten-minute cache exemption. It also discloses a scope quirk (full check set only for Shopify and WooCommerce), which the annotations cannot convey.

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

Conciseness4/5

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

Front-loaded with the core metaphor, then the stage breakdown, then return shape, then operational caveats — a logical order. The middle sentence is long and packs three nested check lists, which slows scanning, but nearly every clause carries distinct information.

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

Completeness5/5

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

With no output schema, the description carries the burden of describing the return value and does so concretely: score out of 100, verdict vocabulary, per-check status/detail/fix, eight-bot access table, and the product read. Quota and caching behavior round out what an agent needs before calling.

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?

There is a single required parameter at 100% schema coverage, and the schema already provides the format example ('store.com'). The description does not add meaning beyond it, such as accepting paths, subdomains, or handling redirects, so baseline 3 applies.

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

Purpose4/5

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

States a concrete verb and scenario ('acts like an AI shopping agent') and enumerates the three tested stages (find, read, buy) with the specific signals checked, which is far more specific than the title. It does not, however, explicitly distinguish itself from sibling audits like audit_site or audit_product_page, leaving the agent to infer the boundary.

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?

Usage context is implied by the framing ('can an agent buy from this store') and useful operational conditions are given (free daily limit, ten-minute cache window, Shopify/WooCommerce get the full check set). There is no explicit when-not guidance or named alternative among the audit/check siblings.

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

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