Wagner Data: supplier catalogs to Shopify

Check a Shopify product import CSV

check_shopify_csv

Read-only

Free. Checks a Shopify product import CSV for the problems that make imports fail or silently go wrong (wrong encoding, Excel-damaged barcodes, missing handles, option mistakes, prices with $ signs, broken quoting, duplicate variants) and returns each failed rule with row numbers. Use it before importing products into Shopify or when a Shopify import errors. Pass the CSV text, or a public URL to it.

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": "check_shopify_csv"
  }
}

Input Schema

Name	Required	Description	Default
`csv_url`	No	Public http(s) URL of the CSV, if csv_text is not given.
`csv_text`	No	The full CSV file contents.

Schema Changelog

Changes observed during successful MCP inspections.

First observedNov 10, 2026

TDQS

A3.9/5.0

Behavior4/5

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

The annotations already establish that this is a read-only, non-destructive, open-world operation. The description adds useful behavioral detail beyond that: it is free, validates specific categories of CSV problems, and returns failed rules with row numbers.

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?

The description is front-loaded with the core action and remains compact despite listing several validation categories. The parenthetical list is long but each item is relevant to what the checker detects.

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 validator with no output schema, the description supplies enough context: what it checks, when to use it, how input may be provided, and that failures are reported with row numbers. It leaves out edge details such as CSV size limits or behavior when no problems are found, but nothing essential is missing.

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 coverage is 100%, so the two parameters are already fully documented in the schema. The description only restates that the CSV can be supplied as text or as a public URL, adding little beyond the existing parameter descriptions.

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?

The description states a specific verb and resource: it checks a Shopify product import CSV for import-breaking problems. It clearly scopes the validation categories and the intended import context, though it does not explicitly contrast itself with the sibling tools.

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?

It gives clear timing guidance: use it before importing products into Shopify or when a Shopify import errors. It does not describe when not to use it or name an alternative tool, but the usage context is otherwise unambiguous.

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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