7IT Solutions

Check if a store is ready for AI shopping agents

check_ai_shopper_readiness

Read-onlyIdempotent

Use this when someone asks whether ChatGPT, Claude, Perplexity or other AI shopping agents can find, read and recommend their online store's products. Checks what robots.txt tells AI search crawlers versus training-only crawlers, llms.txt, agents.md, the UCP commerce discovery file, and one product page's structured data (price, currency, stock, brand, GTIN or SKU, reviews, shipping and returns). Returns the gaps with fixes, compared with 7IT's study of US online stores. Uses an identified crawler that respects robots.txt.

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_ai_shopper_readiness"
  }
}

Input Schema

Name	Required	Description	Default
`url`	Yes	The store, for example shop.example.com
`product_url`	No	A product page on the same store to test; otherwise one is found from the home page

Output Schema

Name	Required	Description
`ucp`	No
`host`	No
`fixes`	No	What to fix, most important first
`llms_txt`	No
`readable`	No	False if the store refused or disallows identified crawlers
`agents_md`	No
`benchmark`	No	The 7IT study this is compared with
`report_url`	Yes	Shareable report
`product_fields`	No
`blocks_ai_search`	No	AI search and shopping crawlers robots.txt blocks
`product_data_complete`	No

Schema Changelog

Changes observed during successful MCP inspections.

AddedOct 1, 2026

TDQS

A4.1/5.0

Behavior4/5

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

Annotations already cover readOnly, idempotent, openWorld, and non-destructive hints. The description adds value beyond that: it uses an identified crawler that respects robots.txt, it returns gaps with fixes benchmarked against a study, and it names the specific inputs it examines. It does not state rate limits or runtime expectations, but the behavioral profile is well-communicated.

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?

Three sentences, front-loaded with the trigger and then enumerated checks. Efficient and well-organized. Slightly dense with the long list of structured-data fields, but every element earns its place by telling the agent what is inspected.

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

Completeness4/5

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

The tool is moderately complex, with two parameters, full schema coverage, and an output schema that presumably explains return values. The description covers what is checked, how (identified crawler respecting robots.txt), and what is returned (gaps with fixes plus benchmark comparison). This is nearly complete, though it doesn't explicitly mention that the output schema defines the return structure.

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%, so the schema already documents both url and product_url, including the fallback behavior for product_url. The description adds no additional syntax, format, or constraint details beyond what the schema provides, so baseline 3 is appropriate.

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?

States a specific verb (check) and resource (AI shopper readiness of a store), and enumerates exactly what it inspects: robots.txt AI vs training crawlers, llms.txt, agents.md, UCP discovery file, and product structured data. This distinguishes it from siblings like check_accessibility or test_store_speed, which cover different readiness dimensions.

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

Usage Guidelines4/5

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

Provides a clear usage trigger: 'Use this when someone asks whether ChatGPT, Claude, Perplexity or other AI shopping agents can find, read and recommend their online store's products.' Context is explicit, but it does not name sibling tools or say when NOT to use it versus check_accessibility or check_app_security.

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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