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bordeaux.guru — Bordeaux en primeur & terroir

search_wines

Search Bordeaux wines from bordeaux.guru en primeur tastings by name, château or appellation. Returns wine, château, appellation, vintage, score, en primeur release price in EUR, a pointer to any bottle tasting, and URL.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
colorNoOptional color filter: red or white
queryYesSearch text — wine name, château name or appellation (e.g. 'pontet', 'margaux')
vintageNoOptional vintage filter, e.g. 2024
appellationNoOptional appellation name or slug filter

TDQS

A4.2/5.0
Behavior4/5

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

No annotations are provided, so the description must carry the behavioral burden. It discloses that this is a search operation and explicitly enumerates the returned fields: wine, château, appellation, vintage, score, en primeur release price, tasting pointer, and URL. It omits mention of result limits or ordering, but for a simple search tool this is adequate.

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

Conciseness5/5

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

Two sentences with no filler: the first states the action and scope, the second lists the return payload. It is front-loaded and every clause contributes useful information.

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 search tool with no output schema, the description adequately covers the return values and source scope. It could add details about result cardinality, sorting, or pagination, but the essential information an agent needs to invoke the tool correctly is present.

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 all four parameters are already documented in the input schema. The description restates the search dimensions but adds no new semantic detail beyond what the schema provides, so the baseline score of 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 names a specific verb ('Search') and a specific resource ('Bordeaux wines from bordeaux.guru en primeur tastings'), with clear search dimensions ('by name, château or appellation'). This distinguishes it from the targeted get_* sibling tools and makes the tool's purpose unambiguous.

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 description provides clear context: use this tool to search wines by name, château, or appellation. It does not explicitly state when not to use it or name a preferred alternative, but the search intent is evident from the phrasing and the surrounding sibling tools.

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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TDQS

A3.9/5.0
Disambiguation4/5

Most tools target clearly distinct entities or actions: château, tasting note, climate, parcel, price, lieudit. A few pairs overlap—compare_climate/get_climate_season and get_tasting_note/get_bottle_tasting—but the descriptions clarify the difference well.

Naming Consistency5/5

The naming is highly consistent: nearly all retrieval tools use the get_<noun> pattern, while compare_, lookup_, search_, and about_ represent genuinely different operation types. All names are snake_case and follow a predictable verb_noun structure.

Tool Count5/5

14 tools is well-scoped for a specialized Bordeaux en primeur and terroir data server. Each tool covers a distinct data product such as tastings, climate, phenology, terroir, prices, and château profiles, without significant redundancy.

Completeness4/5

The tool set covers the primary read-only workflows: searching wines, retrieving tasting notes, exploring château/terroir data, climate comparisons, phenology, and pricing. Minor gaps exist, such as no dedicated browse/list endpoints for appellations or vintages, but these can be worked around via search and known names.

Resources