about_bordeaux_guru
What the bordeaux.guru MCP server offers: data coverage (en primeur tasting notes, appellation climate, phenology, terroir), access tiers and links.
Input Schema
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
What the bordeaux.guru MCP server offers: data coverage (en primeur tasting notes, appellation climate, phenology, terroir), access tiers and links.
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description is the only source for behavioral context, and it does reveal that this is an informational 'about' tool that covers categories like en primeur tasting notes, climate, phenology, terroir, access tiers, and links. It does not mention output format or limitations, but for a no-side-effect, no-parameter overview tool this is relatively complete.
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 one compact, front-loaded sentence that states the purpose and immediately enumerates the main content areas. Every phrase earns its place without filler or redundancy.
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 parameter-less tool with no output schema, the description covers all relevant information an agent likely needs: what the overview contains and how to get it. It also makes clear that this is not a data-retrieval tool, which is sufficient given the sibling list.
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 tool has zero parameters, so the schema already fully represents the input contract. The description adds no parameter-specific details, which is appropriate given there are none, so the baseline of 4 applies.
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 as a high-level overview of the bordeaux.guru server's data coverage, access tiers, and links. This goes beyond a simple restatement of the tool name and helps an agent see that it is distinct from the get_* data retrieval tools. However, it relies on a noun phrase rather than an explicit verb describing what the tool itself returns.
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 implicitly says to use this tool when you need an overview of available data areas, access tiers, and links, rather than when fetching a specific château or tasting note. It gives a clear context but does not explicitly name sibling alternatives or provide exclusionary guidance.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.
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.