Bottles Pour Picks users are adding to their cellars most over the last 30 days (falls back to catalog popularity when live activity data is unavailable). The method used is labeled in the response.
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | Max results (default 10) |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, so the safety profile is covered. The description adds valuable context about the fallback mechanism and that the method is labeled in the response, which goes beyond the 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 two sentences, front-loaded with the core purpose, and includes the fallback note efficiently. No wasted 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?
For a simple read-only tool with one parameter and no output schema, the description is complete. It explains the data source, time window, fallback, and response labeling. The only minor gap is not describing the response format, but with no output schema and simple data, this is acceptable.
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% for the single 'limit' parameter, which is well-documented with min/max and default. The description does not add parameter-specific details, but the schema already provides full semantics, 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?
The description clearly states the tool returns trending bottles based on user cellar additions over the last 30 days, with a fallback to catalog popularity. This specific verb+resource combination distinguishes it from siblings like get_recommendations or search_bottles.
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 to use it (for trending/activity-based picks) and mentions the fallback behavior, but does not explicitly state when not to use it or name alternative tools. The context is clear enough for an agent to select it appropriately.
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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Most tools have distinct purposes: search, get details, compare, find similar, find cheaper, recommend, suggest for tonight, and trending. However, 'find_similar' and 'find_cheaper_alternative' overlap somewhat in that both return similar bottles, though one explicitly filters by price. 'pour_tonight_suggestion' and 'get_recommendations' also both provide recommendations, but differ in input (mood/occasion vs. taste/budget).
Tool names follow a consistent verb_noun pattern: compare_bottles, find_cheaper_alternative, find_similar, get_bottle, get_recommendations, pour_tonight_suggestion, search_bottles, trending_bottles. The only minor deviation is 'pour_tonight_suggestion' which is a bit more descriptive than the others, but it still fits the pattern.
8 tools is well within the ideal 3-15 range. Each tool covers a distinct user need: searching, retrieving details, comparing, finding alternatives, getting recommendations, and seeing trends. No tool feels redundant or unnecessary.
The tool surface covers the core discovery and recommendation workflows well: search, get details, compare, find similar/cheaper, get personalized recommendations, and see trends. Minor gaps include no explicit tool for filtering by age or rating, and no way to get a list of all bottles in a category, but these are workable via search_bottles.