Wine recommendations, food pairings & B2B contact for Weingut Dreissigacker, Rheinhessen.
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Average 3.7/5 across 3 of 3 tools scored.
Each tool serves a clearly distinct purpose: B2B contact info, food pairing, and wine recommendation. There is no overlap in functionality, making it easy for an agent to select the right tool.
All tools share the 'dreissigacker_' prefix and use snake_case, but 'recommend_wine' uses a verb-noun structure while the others use noun-noun. This is a minor deviation and the pattern is still predictable.
With 3 tools, the server is well-scoped for a concierge service, covering the primary user intents without unnecessary bloat. This fits within the ideal 3-15 range.
The core workflows are covered: getting trade contact, food pairing, and recommendations. A notable gap is a dedicated tool to list all wines or access detailed wine profiles, but the recommendation tool likely surfaces this information.
dreissigacker_b2b_contactAGet B2B contact information for Weingut Dreissigacker. Returns structured contact data, importer info, and next steps for trade inquiries. B2B-Kontaktdaten für Gastronomie, Handel und Importeure.
| Name | Required | Description | Default |
|---|---|---|---|
| market | No | Target market or country, e.g. 'USA', 'UK', 'Japan', 'Deutschland' | |
| interest | No | Specific interest, e.g. 'wine list curation', 'import partnership', 'event wines' |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already cover read-only, idempotent, and non-destructive behavior. The description adds what the tool returns (contact data, importer info, next steps), complementing the structured metadata without contradiction.
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 concise and front-loaded with the main action, followed by a brief summary of return content. The German sentence is redundant but not wasteful, keeping the overall length appropriate.
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 tool's simplicity (2 optional parameters, no output schema), the description adequately summarizes return values and context. It lacks detailed return structure but is sufficient for expected use cases.
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 descriptions cover 100% of the two parameters, so the description does not need to add parameter semantics. It provides no extra detail beyond the schema, matching the baseline for high 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 ('Get') and identifies a clear resource ('B2B contact information for Weingut Dreissigacker'). It also distinguishes from sibling tools by focusing on contact data rather than food pairing or wine recommendations.
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 for when to use this tool—when B2B contact information, importer info, or next steps for trade inquiries are needed. It does not explicitly mention alternatives but the purpose is unambiguous enough to guide selection.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
dreissigacker_food_pairingAFind the perfect Dreissigacker wine for a specific dish. Returns a wine recommendation with reasoning why it pairs well. Findet den perfekten Dreissigacker-Wein zu einem bestimmten Gericht.
| Name | Required | Description | Default |
|---|---|---|---|
| dish | Yes | The dish to pair wine with, e.g. 'Vietnamese spring rolls', 'Trüffel-Risotto', 'grilled lobster' | |
| language | No | Response language: 'de' for German, 'en' for English. Auto-detected if omitted. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare the tool read-only and idempotent. The description adds that the output includes reasoning for the pairing, which is useful behavioral context beyond the annotations. No contradictions exist.
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 but repeats the same information in English and German. While this serves a bilingual audience, it adds redundancy. The front-loaded English sentence conveys the core purpose immediately.
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 tool with two well-documented parameters and rich annotations, the description provides sufficient context. It specifies the output type (recommendation with reasoning), which partially compensates for the lack of an 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?
Schema description coverage is 100%, so the schema fully documents the 'dish' and 'language' parameters. The description does not add any additional parameter-level detail, matching the baseline for high 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 clearly states the tool finds the perfect Dreissigacker wine for a specific dish and returns a recommendation with reasoning. This distinguishes it from the sibling 'recommend_wine' tool, which likely provides general wine recommendations.
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 for dish-specific pairing but does not explicitly state when to use it versus alternatives like 'recommend_wine'. No exclusions or alternative guidance is provided, so the context remains implied rather than explicit.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
dreissigacker_recommend_wineBRecommend Dreissigacker wines based on style, food pairing, occasion, or budget. Returns matching wines from the portfolio across 4 tiers (Entry to Grand Cru). Empfiehlt Dreissigacker-Weine basierend auf Stil, Speise, Anlass oder Budget.
| Name | Required | Description | Default |
|---|---|---|---|
| food | No | Food to pair with, e.g. 'grilled fish', 'Vietnamese spring rolls' | |
| style | No | Wine style preference, e.g. 'fresh Riesling', 'full-bodied white', 'biodynamic' | |
| budget | No | Budget per bottle, e.g. '30€', '50', 'under 25 EUR' | |
| language | No | Response language: 'de' for German, 'en' for English. Auto-detected if omitted. | |
| occasion | No | Occasion, e.g. 'fine dining', 'casual dinner', 'wine list for Michelin restaurant' |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true, destructiveHint=false, and idempotentHint=true, so the safety profile is covered. The description adds useful context about the output scope ('Returns matching wines from the portfolio across 4 tiers (Entry to Grand Cru)'), but it does not disclose any additional behavioral details such as result limits or sorting logic. The addition of the German translation is redundant but doesn't contradict annotations.
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 concise, with two sentences (the English and German versions essentially repeat each other). It is front-loaded with the main purpose and criteria, and the tier information adds value. The German duplication is minor redundancy but keeps it brief overall.
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 recommendation tool with 5 optional parameters and no output schema, the description adequately covers the tool's purpose, criteria, and output scope ('matching wines from the portfolio across 4 tiers'). It does not explain ranking or result limitations, but given the read-only nature and simple use case, the description is sufficiently 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?
The input schema provides descriptions for all 5 parameters, covering 100% of the semantics. The description groups these into four categories (style, food pairing, occasion, budget) but does not add any syntax, format, or constraint details beyond what the schema already provides. This meets the baseline for high 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 the specific verb 'Recommend' with a clear resource ('Dreissigacker wines') and enumerates the matching criteria (style, food pairing, occasion, budget). It is clear in what it does, but it does not explicitly differentiate itself from the sibling tool 'dreissigacker_food_pairing', which could overlap in food pairing recommendations.
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?
There is no guidance on when to use this tool versus alternatives. Given the existence of 'dreissigacker_food_pairing' as a sibling, the description misses an opportunity to clarify that this tool is for general recommendations across multiple criteria, while the sibling may be more specialized. No exclusions or alternative scenarios are mentioned.
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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