Compare growing-season climate aggregates (GDD, heat days, rain, sunshine, ET0…) across Bordeaux appellations and/or vintages. Collector/Cellar members only.
| Name | Required | Description | Default |
|---|---|---|---|
| years | Yes | Comma-separated years, e.g. '2024,2025' | |
| appellations | Yes | Comma-separated appellations (names or slugs), e.g. 'pauillac,margaux' |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations exist, so the description carries the full behavioral disclosure burden. It communicates that the tool computes aggregates and is restricted to Collector/Cellar members, which is helpful. However, it does not describe the output return format, how missing data is handled, or confirm the operation is read-only—leaving room for an agent to guess about runtime behavior.
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 description is two sentences with no filler. The main purpose and metric examples are front-loaded; the access restriction follows cleanly in the second sentence. Every part earns its place and is easily scannable.
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?
Given the simple two-parameter schema and no output schema, the description covers the essential context: comparison granularity ('appellations and/or vintages'), metric categories, and audience restriction. Minor gaps remain, such as not clarifying whether both parameters must be provided and not describing the actual return structure beyond passive aggregates, but these do not block reasonable invocation.
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?
Schema coverage is 100%, with both 'years' and 'appellations' and 'appellations' having clear comma-separated descriptions. The description adds only a rough relationship between the parameters ('and/or vintages') but no additional format details, default behavior, or examples beyond the schema. This matches the baseline for fully schema-described parameters.
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 states a specific verb, 'Compare,' and a specific resource: growing-season climate aggregates across Bordeaux appellations and/or vintages. Listing example metrics (GDD, heat days, rain, sunshine, ET0) further clarifies the domain. This clearly distinguishes it from sibling 'get_' tools, which retrieve singular records rather than perform comparisons.
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 first sentence clearly implies the use case: use this tool when a comparison of growing-season climate summaries is needed, whether across appellations, vintages, or both. 'Collector/Cellar members only' offers a concrete access constraint. It does not explicitly name alternatives, but the comparative scope is clear enough to avoid mistaking it for a single-season retrieval.
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.