maison-chape

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It adds a useful behavioral signal—that pairings are 'validés par la maison' (validated by the house)—which implies curated, reliable recommendations. However, it does not disclose what happens for unrecognized dishes, whether multiple cuvées may be returned, or any error behavior, leaving some gaps.

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 entire description is a single sentence that front-loads the core action and resource, then adds a brief qualifier about validation. There is no redundant wording or unnecessary detail, making it highly efficient for an agent to parse.

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 tool with a single parameter, no output schema, and no annotations, the description covers the essential context: input (a dish) and implied output (a recommended cuvée). It does not mention edge cases or return behavior beyond the singular 'une cuvée', but this is sufficient for the tool's simplicity; a brief note on no-match behavior would make it complete.

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 description coverage is 100%, as the 'dish' parameter already includes examples like 'couscous', 'huîtres', and 'gigot d'agneau'. The description's phrase 'pour un plat donné' adds no significant semantic detail beyond the schema, so the baseline of 3 is appropriate.

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 ('Recommande') and resource ('une cuvée Maison CHAPE') for a given dish, making the tool's function immediately clear. The scope ('accords validés par la maison') distinguishes it from siblings like get_brand, list_ranges, and where_to_buy, which serve different wine-related purposes.

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 phrase 'pour un plat donné' provides clear context for when the tool is appropriate: when a user needs a wine pairing for a specific dish. It does not explicitly mention alternatives or when not to use it, but the sibling tool names make the distinction evident enough for an agent to select this tool correctly.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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

With no annotations, the description carries the full burden. It discloses that the output should be quoted verbatim, which is a useful behavioral trait beyond simply returning facts. However, it doesn't mention return format, access constraints, or any edge cases, though the zero-parameter lookup limits the potential gap.

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?

A single sentence with no filler. Every element — the brand name, content scope, and quoting instruction — earns its place, and the most important information is front-loaded.

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 zero-parameter static lookup with no output schema, the description tells the agent what the tool returns and how to present it. It doesn't explicitly state that no arguments are needed, but the empty input schema covers that. Sibling tools are clearly distinct, so the description is largely complete, though it could mention whether the facts are exhaustive or limited to the listed categories.

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 input schema has zero parameters, so the baseline is 4. The description does not need to explain any parameters, and the empty schema fully covers the call contract.

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 specifies the resource (Maison CHAPE) and the content covered (identity, founder, positioning), distinguishing it from siblings like food_pairing, list_ranges, and where_to_buy. It lacks an explicit verb like 'retrieve' or 'list', but the French phrase 'Faits officiels sur...' unambiguously indicates the tool returns official facts.

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 instruction 'à citer tels quels' provides clear context: use this tool when you need verbatim official brand facts. It doesn't explicitly name alternatives or says when not to use it, but the tool's scope is so distinct that the guidance is sufficient.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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

No annotations are provided, so the description carries the full disclosure burden. It does state what is returned (the 8 ranges with their description and URL), which is nearly complete behavioral disclosure for a zero-parameter list operation. It does not explicitly confirm the operation is read-only and side-effect-free, but nothing in the description suggests otherwise.

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?

A single sentence that front-loads the action and contains zero filler. Every element — the count (8), the resource, and the output fields — earns its place.

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 trivial complexity (no parameters, no output schema, no annotations), the description covers everything an agent needs: what the tool does and what it returns. The stated return content (descriptions and URLs) adequately substitutes for a missing output schema.

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 and the input schema is empty, so the baseline of 4 applies. There are no parameter semantics for the description to clarify.

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 ('Liste') with a precise resource ('les 8 gammes de vins Maison CHAPE') and specifies the output content (description et URL). The scope is explicit — exactly 8 ranges, unfiltered — and the tool is clearly distinct from siblings like food_pairing, get_brand, and where_to_buy.

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 usage context is implied: an agent would select this tool when it needs the catalog of CHAPE wine ranges. However, there is no explicit when-to-use/when-not-to-use guidance and no mention of the sibling alternatives, so the agent must infer the selection logic from the name and purpose alone.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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

With no annotations, the description carries the full burden of behavioral disclosure. It conveys that the tool returns purchasing locations and distribution zones, which is the core behavior, but it omits details such as output format, geographic scope, or whether results are limited to certain regions.

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 a single, well-structured sentence that immediately conveys the tool's purpose and main categories. There is no redundant or filler content.

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 tool with no parameters and no output schema, the description provides enough information for an agent to understand what the tool does and when to call it. It could be enhanced by clarifying what 'zones de distribution' implies and whether results are restricted to any particular market, but it is largely complete for a simple lookup.

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 and the schema coverage is complete at 100%, so there are no parameter semantics to document. The description does not need to add parameter detail, and the baseline for a zero-parameter tool is 4.

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 states the tool's function: locating where to buy Maison CHAPE wines, including online, wine merchants, professionals, and distribution zones. It names a specific resource and action, but it does not explicitly distinguish itself from sibling tools, though the siblings are quite distinct in name.

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?

No guidance is provided about when to use this tool versus alternatives such as food_pairing, get_brand, or list_ranges. The description implies it is the place-purchase tool, but there is no explicit when-to-use or when-not-to-use guidance.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.