bordeaux.guru — Bordeaux en primeur & terroir

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.

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.

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

With no annotations, the description carries the full behavioral disclosure burden. It openly states that the written note is restricted to Collector/Cellar members while others receive only the opening line, and it lists the returned fields. This is meaningful beyond the schema and helps set expectations about access-controlled output.

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 three focused sentences with no filler. It front-loads the core distinction, then lists the return fields, then notes the access tier difference. Every sentence 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?

The description enumerates the return values and explains the membership limitation, which is important since there is no output schema. It is sufficient for a low-complexity two-parameter tool, though it does not discuss edge cases like missing tasting notes or a wine with no bottle tasting record. Overall, it provides enough context for correct use.

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%, so the parameter descriptions already document 'wine' and 'vintage' thoroughly. The tool description adds context about output fields but does not add new semantics about how the parameters behave. A baseline 3 is appropriate because the schema handles the parameter meaning.

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 a specific verb and resource: it returns bottle tasting notes from bordeaux.guru, meaning wines tasted from the bottle at events. It also distinguishes this from en primeur barrel samples, which sets it apart from sibling tools like get_tasting_note. The scope is unambiguous.

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?

It gives a clear usage context: use this for bottle tastings at events, as opposed to en primeur barrel samples. It doesn't explicitly name the alternative sibling or state 'use get_tasting_note instead', but the distinction is clear enough for an agent to infer the correct tool.

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 burden; it discloses the returned content (profile fields and tasted wines) but doesn't state output format, error behavior, or non-mutating guarantees. 'Get' implies reading, and the content list helps, but security/behavioral details remain unspoken.

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 compact sentence with a front-loaded verb and resource, followed by a tightly listed set of included data. No filler or redundant restatement of tool name.

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, straightforward read tool, description is reasonably complete: it names the external source and lists returned data. Minor gap of not clarifying response format, but schema covers input and the profile scope is clear.

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% with an illustrative example ('chateau-lynch-bages' or 'Lynch-Bages'). Description adds no additional parameter detail, so baseline 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?

Description specifies verb and resource: 'Get a château profile from bordeaux.guru', plus enumerates contents (appellation, classification, terroir, vineyard size, typical blend, tasted wines). This clearly distinguishes it from exclusive sibling tools like get_terroir, get_tasting_note, or get_vintage_profile.

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?

Description implies usage for broad château overview through the word 'profile' and its comprehensive content list, but it doesn't explicitly state when to use this over narrower siblings or mention any when-not-to-use conditions.

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 explaining behavior. It discloses the data source and storage context (Open-Meteo/ERA5 via bordeaux.guru) and the three return modes: summary, monthly, and daily. It stops short of describing edge cases, season boundaries, units, or error behavior, but the key behavior is clearly presented.

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 dense but front-loaded: it states the core purpose first, then lists relevant metrics and return modes. The parenthetical source explanation is useful, and no sentence is wasted, though the combined clause is somewhat packed.

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?

The description covers the main use case, parameter context, and return granularity, which is adequate for a basic climate retrieval. However, with no output schema, an agent still lacks detail about exact season date ranges, units of measurement, valid appellation values, and behavior for unavailable data.

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%, so the schema already documents appellation, year, and granularity. The description's mention of summary, monthly, and daily granularities largely mirrors the schema, adding little independent meaning beyond context.

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 a specific resource: growing-season climate for one Bordeaux appellation and year, and lists the data types it returns. It does not, however, explicitly contrast itself with sibling tools like compare_climate, so sibling differentiation is only implicit.

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 'for a Bordeaux appellation and year' implies the intended call for a single appellation/year climate query. The description gives no explicit guidance on when not to use it or when to prefer a sibling such as compare_climate.

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 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.

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

With no annotations provided, the description carries the full behavioral disclosure burden. It warns that data is sparse and available only for documented vintages, and it indicates that the source is official château brochures, setting expectations about coverage and provenance without revealing internal state.

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, front-loads the core resource and data phases, and caps usage expectations with the sparse-data caveat. No sentence is wasted; everything included supports invocation decisions.

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 simple optional-parameter getter with no output schema, the description conveys the fundamental return intent (phenology windows) and important coverage limitations. It lacks explicit output formatting details, but the schema plus this description give an agent enough to reason about the call.

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 already covers both parameters completely with descriptions, so no additional semantic burden falls on the tool description. The phrase 'for a Bordeaux vintage' hints at the vintage parameter but adds no detail beyond the schema's own documentation.

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 vine phenology windows (budbreak, flowering, veraison, harvest) for a Bordeaux vintage. It effectively distinguishes itself from broader tools like get_vintage_profile or get_climate_season by anchoring the data source and scope, though it does not name a sibling explicitly.

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 'Sparse, documented vintages only' caveat implies when the tool will be useful, but there is no explicit guidance on when to choose this over get_vintage_profile or get_climate_season. The optional appellation and vintage parameters give some context, but no alternatives are named.

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 provided, the description carries the full burden of behavior disclosure. It does well by revealing membership-gated output behavior: Collector/Cellar members receive the number, expected price, and comparison count, while everyone gets the release price. This is a meaningful behavioral trait not inferable from the schema. It could go further by noting whether lookup failures return errors or fallback, but the key access limitation is clearly disclosed.

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 compact at two sentences, with the core value proposition front-loaded in the first sentence. Each sentence earns its place: one defines the metric, the other explains what different user tiers can see. Slightly awkward phrasing ('How dear...') and the vague 'the number' prevent 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 tool with no output schema and no annotations, the description should more thoroughly explain return values. It conveys the main output categories but leaves terms like 'the number' and 'the expected price' underspecified, and does not state units, currency, or what happens when data is unavailable. Given the simplicity of the two parameters, the definition is adequate but not fully 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 coverage is 100%: both wine and vintage have descriptions with examples. The description adds conceptual context (en primeur release price vs. vintage price curve) but does not materially clarify parameter formats or constraints beyond the schema. Baseline 3 is appropriate because the schema does the heavy lifting.

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 tool's purpose: it communicates how expensive a Bordeaux wine is relative to its score by comparing en primeur release price to the vintage's price curve. This is a specific, meaningful function that differs from generic price lookup tools like get_wine_prices. The phrase 'How dear...' is slightly indirect, but the meaning is clear and actionable.

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 usage context: if you need to evaluate price fairness relative to wine score, this is the tool. It also distinguishes member vs. non-member visibility. However, it does not explicitly state when to prefer this over get_wine_prices or other siblings, nor does it provide exclusions or decision criteria beyond the inherent purpose.

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?

Since no annotations are provided, the description carries the full burden, and it does meaningful work: it identifies the source, the returned tasting-note sections, and the tier behavior ('Free tiers receive a teaser'). It does not detail error cases or authentication, but for a read-only retrieval tool this is reasonably transparent.

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 dense, front-loaded sentence with no filler words. It states the purpose, the resource, the output scope, and the tier limitation without repetition.

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 simple two-parameter read tool with no output schema, the description is unusually complete: it names the output fields and warns about teaser limitations for free tiers. The main missing context is sibling-selection guidance, which is already penalized under usage guidelines.

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%, so the schema already documents both parameters. The description adds context by framing them as 'one wine and vintage,' but it does not meaningfully enrich the schema-provided meaning.

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 begins with a specific verb and resource: 'Get the en primeur tasting note for one wine and vintage from bordeaux.guru.' It also enumerates the returned content, which makes it clearly distinct from sibling tools like get_chateau, get_vintage_profile, or search_wines.

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 does not state when to choose this tool over alternatives or when not to use it. It implies that it is for a single wine/vintage tasting note, but it does not mention exclusions, prerequisites, or routing to siblings such as search_wines for unknown wine names.

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 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.

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

With no annotations, the description carries the behavioral burden, and it usefully discloses an important edge case: older vintages may lack a profile and the response will explicitly say so. This helps set agent expectations about missing data, though it does not specify response shape or error 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?

Two sentences, no filler, with the core function and content list front-loaded and the edge case placed second. Every sentence 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?

For a single-parameter read tool with no output schema, the description covers what the tool returns, notes the missing-data behavior, and implicitly distinguishes it from the climate and tasting siblings. It could add a sentence about invalid years or response limitations, but the essential context is present.

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% and there is only one parameter, 'year', so the schema already carries the semantic load. The description does not add format, range, or interpretation details beyond what the schema provides, making baseline 3 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 names a specific verb and resource ('get the bordeaux.guru vintage profile for a year') and enumerates the delivered contents: quality assessment, harvest summary, key events. This clearly distinguishes it from sibling tools like get_bottle_tasting or get_climate_season, which address different data domains.

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 when the tool is appropriate by naming the vintage profile as its scope, but it never explicitly states when to prefer it over an alternative or when not to use it. The caveat about older vintages does hint at a fallback to bottle tastings, but the alternative tool is not named.

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 data accessibility ('Public data — available on every tier'), defines the exact data scope, and explicitly flags a member-only feature that is absent. This is strong behavioral context for a simple read-only lookup.

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?

Three short sentences, front-loaded with the core purpose, followed by essential pricing details and a useful exclusion. No 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 single-parameter public lookup with no output schema, the description supplies the key missing context: what is returned, the currency, the pricing basis, and access availability. It does not specify the exact return shape, but the tool is simple enough that this is a minor gap.

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 for the only parameter is 100%, with examples and field description, so the baseline applies. The description adds value by framing the wine as 'one Bordeaux wine' and explaining price semantics, but it does not need to restate the parameter itself.

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?

States a specific resource ('En primeur release prices of one Bordeaux wine across vintages'), units (EUR), and pricing stage (ex-négociant, excluding VAT). The scope 'one Bordeaux wine' also differentiates it from broader wine-data siblings like get_vintage_profile and compare_climate.

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?

Gives clear context: this is a public-data lookup available on every tier, so an agent can infer there are no auth barriers. It also states what is not included (price position against the vintage curve), preventing misuse. It does not explicitly name sibling alternatives, but the description makes the use case obvious.

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 burden of behavioral disclosure. It adds useful context about the origin of the data and gives concrete examples, but it does not describe behavior such as whether matching is fuzzy, case-insensitive, or limited, nor does it mention what the output looks like. For a read-only lookup these gaps are minor but still worth noting.

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 long with every clause earning its place: purpose, scope, data source, examples, and search dimensions. It is front-loaded with the main action and resource, making it easy 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 simple read-only lookup with two optional parameters, the description plus schema covers the common use cases well. It does not explicitly describe the return shape or list fields, which would be more valuable given no output schema is present, but the verb 'Look up' and the known resource type make the output fairly predictable.

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 100% description coverage for its two parameters, so the schema already explains 'name' and 'appellation' with defaults and clear semantic hints. The description adds only a high-level summary ('Search by name and/or appellation'), which does not go beyond the structured field descriptions.

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 a specific verb ('Look up'), a resource type ('named vineyard sites / lieux-dits'), a geographic scope ('in Bordeaux appellations'), and the data source ('cadastral open data mapped by bordeaux.guru'). This makes it easy to distinguish from sibling tools like get_terroir, get_parcel_varieties, or search_wines without needing to inspect schemas.

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 provides clear context on how to use the tool: 'Search by name and/or appellation.' It implies that omitting one of the parameters is valid, and the schema further clarifies the omit-to-list behavior. It does not explicitly name alternative tools or say when not to use it, but the lookup scope is distinct enough from its siblings.

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 must carry the behavioral burden. It discloses that this is a search operation and explicitly enumerates the returned fields: wine, château, appellation, vintage, score, en primeur release price, tasting pointer, and URL. It omits mention of result limits or ordering, but for a simple search tool 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?

Two sentences with no filler: the first states the action and scope, the second lists the return payload. It is front-loaded and every clause contributes useful information.

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 search tool with no output schema, the description adequately covers the return values and source scope. It could add details about result cardinality, sorting, or pagination, but the essential information an agent needs to invoke the tool correctly is present.

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%, so all four parameters are already documented in the input schema. The description restates the search dimensions but adds no new semantic detail beyond what the schema provides, so the baseline score of 3 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 names a specific verb ('Search') and a specific resource ('Bordeaux wines from bordeaux.guru en primeur tastings'), with clear search dimensions ('by name, château or appellation'). This distinguishes it from the targeted get_* sibling tools and makes the tool's purpose unambiguous.

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 provides clear context: use this tool to search wines by name, château, or appellation. It does not explicitly state when not to use it or name a preferred alternative, but the search intent is evident from the phrasing and the surrounding sibling tools.

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