Factline

Is this webshop real? (free)

webshop_check

Read-onlyIdempotent

Free. Use before a shopping agent or the user pays an unfamiliar webshop: domain age, company and VAT number on the site (VAT checked in VIES), contact page, payment methods, prepayment-only or crypto-only, 70-99% discount lures. Returns looks_ok, caution or avoid and decision allow, review or deny. Input: url.

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": "webshop_check"
  }
}

Input Schema

Name	Required	Description	Default
`url`	Yes	The shop's address, e.g. https://www.example-shop.nl

Schema Changelog

Changes observed during successful MCP inspections.

AddedNov 2, 2026

TDQS

A4/5.0

Behavior4/5

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

The description discloses key behavioral traits such as the tool being 'Free' and the nature of the output decision, ranging from 'allows' to 'deny'. This explains the tool's operational philosophy better than the structural annotations alone.

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

Conciseness3/5

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

The description is functional but dense, prioritizing a comprehensive list of checks over stylistic conciseness. The opening sentences effectively front-load the usage context before detailing the checklist.

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?

Since there is no output schema, the description is highly complete by explaining the types of assessments performed and the possible decision categories the tool returns (allows, caution, avoid). This is critical context for the agent's expectations.

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?

The schema coverage is 100%, meaning all parameter semantics are already defined in the structured input schema. Therefore, the description focuses on the tool's functionality rather than clarifying parameter meaning, hitting the baseline score.

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 clearly identifies the tool's purpose: assessing the legitimacy of an unfamiliar webshop. It lists specific verification criteria (domain age, VAT checks, payment methods) that distinguish this as a comprehensive, consumer-focused check.

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 provides explicit instructions on when to use the tool—before payment or involving an unfamiliar webshop. While it doesn't name specific sibling tools like `eu_vat_check`, the context guides the AI appropriately towards its use case.

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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