Vaper Club Agent

compare_products

Read-onlyIdempotent

Compare offers for the same product model. Takes the same filters as search_products, identifies the best-matching standardized model, then returns up to 10 shop products of that model sorted by price so shops, prices, stock and ratings can be compared. Returns a warning and no products when no model can be identified. Provides objective product data and user feedback for adults only; it does not provide guidance on circumventing age verification, purchase restrictions or other legal limits.

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": {
      "query": "<query>"
    },
    "name": "compare_products"
  }
}

Input Schema

Name	Required	Description
`shop`	No	Optional shop name used to find the anchor product.
`limit`	No	Maximum number of products, 1-10. Default 10.
`query`	Yes	Product name, brand, model or search keywords. Required.
`country`	No	Optional ISO 3166-1 alpha-2 country code of the shop location.
`language`	No	Optional language code.
`brand_name`	No	Optional brand name.
`model_name`	No	Optional model name.
`category_name`	No	Optional product category name or code.

Schema Changelog

Changes observed during successful MCP inspections.

First observedNov 11, 2026

TDQS

A4.4/5.0

Behavior5/5

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

Beyond the readOnlyHint/idempotentHint annotations, the description discloses the result cardinality cap (10), the sort order (price), what fields are comparable (shops, prices, stock, ratings), and the exact degenerate-case behavior (warning plus empty result). It also declares a content-scope boundary around adult products and legal-limit advice, which an agent needs to know before answering.

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 core is two tightly packed sentences that front-load purpose, mechanism, and output shape. The third sentence on adults-only data and non-assistance with age verification is a scope caveat rather than instruction for calling the tool, so it is minor padding — but the whole description is still short and well-ordered.

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 and eight parameters, the description carries the burden well: it explains what is returned (model-matched shop products with price, stock, ratings), how it is ordered, the cap, and the no-match case. An agent can decide to call it and interpret the result without further documentation.

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 every parameter's meaning is already documented in the input schema, including the anchor-product role of `shop`. The description adds only the framing that these filters match search_products' semantics; it adds no per-parameter syntax or default detail beyond the schema, so 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 specific verb and resource: 'Compare offers for the same product model.' It then specifies the mechanism (reuse search_products filters, resolve the best-matching standardized model) and the output shape (up to 10 shop products sorted by price), which clearly separates it from the candidate sibling search_products.

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 context for when this tool is the right pick — you already have a specific product model in mind and want cross-shop price/stock/rating comparison — and states the negative case ('returns a warning and no products when no model can be identified'). It does not name an explicit alternative for the broad-discovery case, but the 'same filters as search_products' line establishes the relationship.

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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