Dreissigacker Wine Concierge

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

Annotations already declare readOnlyHint, idempotentHint, destructiveHint. The description adds that it returns structured data, but does not disclose any additional behavioral traits beyond what annotations cover.

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 three short sentences, front-loaded with the main action, and no unnecessary words.

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 has no output schema, the description adequately outlines the return (contact data, importer info, next steps). It could mention more about fallbacks for missing markets, but it's sufficient for a simple query tool.

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 input schema already describes both parameters. The tool description does not add further meaning beyond the schema, earning the baseline score.

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 verb (Get) and resource (B2B contact information for Weingut Dreissigacker), and distinguishes from siblings which are about food pairing and wine recommendation.

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 use for trade inquiries (B2B) but does not explicitly contrast with siblings or provide when-not-to-use guidance. However, the context of being the only B2B contact tool among the siblings makes it clear.

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?

Annotations already declare readOnlyHint=true, so the tool is safe. The description adds that the tool returns a recommendation with reasoning, which provides useful behavioral context. No contradictions or missing critical behavior details for a simple query tool.

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 short sentences in English and one in German. Front-loaded with the core action. No unnecessary words. Efficient and well-structured.

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 no output schema, the description mentions the return includes a wine recommendation and reasoning, which is adequate for a simple read-only tool. Could specify more about error handling or unknown dishes, but current completeness is sufficient.

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 fully describes the parameters. The description only restates the purpose without adding new semantic meaning to the parameters. 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 clearly states the tool finds a Dreissigacker wine for a specific dish and returns a recommendation with reasoning. It includes both English and German, making the purpose unambiguous. It distinguishes itself from siblings like 'dreissigacker_recommend_wine' by focusing on food pairing.

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 explicit guidance on when or when not to use this tool versus alternatives. The description only implies usage for pairing wine with a dish. No exclusions or alternative tools are mentioned.

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?

Annotations already indicate readOnlyHint=true and destructiveHint=false, so the description's job is to add context beyond safety. It adds that the tool 'returns matching wines from the portfolio across 4 tiers,' which provides some behavioral insight, but it does not disclose other traits like expected number of results or behavior with no matches.

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 at three short sentences, with the purpose front-loaded in English and a German translation appended. It avoids fluff, though the bilingual repetition could be seen as slightly redundant.

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 required parameters and no output schema, the description covers the key aspects: recommendation criteria, the 4-tier scope, and language support (via the parameter schema). It provides a solid understanding of what the tool does and its input, but it lacks details on output structure or edge 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 coverage is 100%, so the baseline is 3. The description simply reiterates the parameter categories (style, food, occasion, budget) without adding new meaning beyond what the schema already provides in each property's description.

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 purpose: recommend wines based on four explicit criteria (style, food pairing, occasion, budget) and mentions the 4-tier portfolio (Entry to Grand Cru). This specificity differentiates it from siblings like dreissigacker_food_pairing, which likely focuses on pairing only.

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 by listing the selection criteria, but it does not provide explicit guidance on when to use this tool versus alternatives like dreissigacker_food_pairing. No 'when-not-to-use' or prerequisite information is given.

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