Red Good Wine

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recommend_bottles

Read-onlyIdempotent

Recommend real bottles for a plain-language request: a dish, an occasion, a grape, a region or a budget (e.g. 'a red for lasagna under $40', 'Italian sparkling for a party'). Returns a short grounded answer and the bottles with their lowest current shop price and page link.

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": {
      "question": "<question>"
    },
    "name": "recommend_bottles"
  }
}

Input Schema

Name	Required	Description	Default
`question`	Yes	The shopper's request in plain language.

Output Schema

Name	Required	Description	Default
`note`	No
`answer`	Yes
`bottles`	Yes
`relaxed`	No
`question`	No

Schema Changelog

Changes observed during successful MCP inspections.

First observedNov 13, 2026

TDQS

A3.6/5.0

Behavior3/5

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

Annotations already declare readOnly, idempotent, non-destructive and closed-world, so the safety profile is covered. The description adds two genuinely new facts: the answer is 'grounded' and prices are the 'lowest current shop price' across shops, which discloses aggregation semantics rather than just restating the annotations. It says nothing about ambiguity handling, coverage limits, or latency.

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?

Two sentences, front-loaded with the capability and then the return shape; no filler. The second sentence slightly duplicates what the output schema already declares, which keeps it from being maximally tight.

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 single-parameter, read-only recommendation tool with an output schema and full annotation coverage, the definition is nearly sufficient — the agent knows what to send and roughly what comes back. It could be strengthened by noting how it differs from search_bottles/best_price when a request is a straightforward catalog lookup.

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?

With one parameter at 100% schema coverage, the schema already documents 'question' as the shopper's plain-language request. The description's example phrasings ('a red for lasagna under $40', 'Italian sparkling for a party') usefully show the expected shape and length of that string, but add no constraints or edge-case guidance beyond the schema's maxLength.

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?

Uses a specific verb+resource ('Recommend real bottles') and enumerates the request dimensions it accepts (dish, occasion, grape, region, budget). The 'plain-language request' framing implicitly separates it from a structured search like search_bottles, but no sibling is named, so the differentiation is inferred rather than stated.

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?

The list of supported request types plus two concrete example phrasings give an agent a clear picture of when this tool is the right fit. It stops short of stating when NOT to use it (e.g. exact SKU lookups or price-only questions) or naming the alternative tools for those cases.

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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