shopware-mcp

Ready for AI shopping agents?

agent_readiness

Read-onlyIdempotent

Checks whether AI shopping agents can find and sell a Shopware shop's products by building a product feed, validating required fields, and verifying checkout pages.

Instructions

Can AI shopping agents (ChatGPT, Gemini, Perplexity, Copilot) find and sell this shop's products? Builds the product feed agents read (Agentic Commerce feed fields, as Google Merchant) from the storefront's visible products and checks each row: name, description, brand, image and price are required; GTIN with a valid check digit, manufacturer number and weight help agents match and ship. Also checks HTTPS and the privacy, terms and revocation pages an agent checkout links to. Read-only. Returns { score (0 to 100), products, gaps[] with share and samples, shop[], checkoutReady, feedSample[] }.

Tool call template

This tool declares no required parameters. Add optional parameters from the input schema as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {},
    "name": "agent_readiness"
  }
}

Input Schema

Name	Required	Description	Default
`maxItems`	No	Sample products per gap
`salesChannelId`	No	Storefront to check; default: the first one

Schema Changelog

Changes observed during successful MCP inspections.

First observedv0.11.1Nov 6, 2026

TDQS

A3.9/5.0

Behavior4/5

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

With readOnlyHint/idempotentHint already supplied, the description still adds substantial behavior: which products are inspected (visible storefront products), the exact required fields (name, description, brand, image, price), the GTIN check-digit rule, and the HTTPS/legal-page verification. It also compensates for the missing output schema by naming the returned shape (score 0-100, products, gaps[] with share and samples, checkoutReady, feedSample[]). The one soft spot is the phrase 'Builds the product feed', which momentarily reads like a write even though the tool is declared read-only and merely samples the feed.

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?

It is a single dense paragraph, but it is front-loaded with the value question and then proceeds in a logical order: what is built, what is validated, what is checked, and what is returned. Nearly every clause carries information, though the run-on 'Agentic Commerce feed fields, as Google Merchant' parenthetical is slightly awkward.

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?

For a two-parameter, zero-required audit tool with no output schema, the definition covers inputs' effect (visible products, sample size), validation rules, and the return structure, which is what an agent needs to call it and interpret results. It stops short of explaining how the 0-100 score is computed or what threshold counts as 'ready', a minor gap given the tool's evaluative nature.

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% for both parameters, so maxItems ('Sample products per gap') and salesChannelId ('Storefront to check; default: the first one') are already fully documented in the schema. The description alludes to the storefront scope but adds no syntax, defaults, or meaning beyond what the schema supplies, so the baseline 3 applies.

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?

The description opens with a concrete question ('Can AI shopping agents ... find and sell this shop's products?') and then states precisely what it does: builds an Agentic Commerce product feed from visible storefront products and audits required fields, GTIN check digits, HTTPS and legal pages. That scope is clearly distinct from the diagnostic/reporting siblings such as shop_audit, shop_diagnose and shop_settings.

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 is implied rather than stated: an agent can infer 'use this to check agent/AI-commerce readiness', and 'Read-only' signals it is safe to run casually. However, there is no explicit when-to-use guidance, no exclusions, and no routing away from the plausible alternatives (shop_audit, shop_diagnose) that an agent might confuse with this check.

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