Parcel-level grape varieties of a Bordeaux château (digitized by bordeaux.guru from official château brochures — 'údaje château'). Aggregate for everyone; per-parcel detail (variety × soil × hectares) for members.
| Name | Required | Description | Default |
|---|---|---|---|
| chateau | Yes | Château name or slug, e.g. 'gazin' |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description must disclose behavior itself. It usefully states the data provenance, château brochure digitization, and the access-tier difference between aggregate and per-parcel results. It does not cover error behavior, membership authentication, or response formatting, but for a primarily read-like data lookup 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.
Is the description appropriately sized, front-loaded, and free of redundancy?
The purpose is front-loaded and the access distinction is expressed in a compact sentence. The parenthetical about digitized official château data adds credibility, though the odd quoted phrase 'údaje château' contributes little and keeps this from a perfect score.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a one-parameter tool with no output schema, the description provides enough shape: aggregate varieties for everyone, and per-parcel variety × soil × hectares for members. It is mostly self-contained, though membership mechanics and return-structure details are left implicit.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The only parameter 'chateau' is already fully described in the schema, including an example, so the description does not need to compensate for missing parameter documentation. The description adds no new parameter semantics beyond the general subject matter, which matches a 100% schema-coverage baseline.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly identifies the resource: parcel-level grape varieties for a Bordeaux château, and it clarifies the output scope by separating aggregate data from per-parcel member data. It is more specific than a tautology, but it does not name a sibling tool or use an explicit verb, so it stops short of maximum differentiation.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The membership distinction gives usable context: aggregate results are available to everyone, while variety × soil × hectares detail is gated to members. However, it does not say when to prefer this tool over related tools such as get_terroir or get_chateau, leaving some selection decisions to inference.
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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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.
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.
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.
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.