Find 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. |
Changes observed during successful MCP inspections.
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.