Terroir profile of a Bordeaux appellation from bordeaux.guru: dominant soils, microclimate, relief (real Copernicus DEM), BRGM gravel terraces (Dubreuilh types 1–6) and named vineyard sites (lieux-dits) coverage.
| Name | Required | Description | Default |
|---|---|---|---|
| appellation | Yes | Appellation name or slug, e.g. 'pauillac', 'saint-emilion' |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the behavioral disclosure burden. It does disclose meaningful output composition and data sources: 'real Copernicus DEM', 'BRGM gravel terraces (Dubreuilh types 1–6)', and 'lieux-dits coverage.' It does not state that this is a read-only lookup, does not mention caching, errors, or how 'coverage' is represented, and the 'get' verb only implicitly signals safety.
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?
One dense, front-loaded sentence contains the core purpose and all relevant content dimensions without filler like 'useful' or 'comprehensive'. It is slightly awkward and compressed in the final clause, but every part contributes meaningful differentiation.
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?
With a single required parameter and 100% schema coverage, this is close to complete: the description tells an agent what kind of object it will receive and what thematic layers are included. The lack of output schema references a few unspecified conversion details, but the practical invocation context is covered.
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 parameter schema already fully documents `appellation` with an example, so the description is not required to add much here. The description repeats that the appellation is Bordeaux-specific but does not enrich the parameter semantics beyond the existing 100% schema coverage.
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 uses a specific verb plus resource: 'Terroir profile of a Bordeaux appellation'. It then enumerates the exact content—dominant soils, microclimate, relief, BRGM gravel terraces, and lieux-dits coverage—so it is far more specific than a generic 'get terroir' statement and can be distinguished from climate-only or wine-related siblings.
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 description implies the tool is used when a terroir profile of a Bordeaux appellation is needed, listing the exact output categories. However, it gives no explicit when-to-use or when-not-to-use guidance and does not mention alternatives such as get_climate_season or lookup_lieudit, even though those are closely related siblings.
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.