Get personalized recommendations

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

The annotations already provide readOnlyHint=true, idempotentHint=true, destructiveHint=false, so the safety profile is covered. The description adds context about inputs but not much about behavior beyond that: no mention of what the tool returns, how recommendations are ranked, or any limits or caveats. It does not contradict annotations, but it also doesn't add significant behavioral precision beyond the obvious read-only nature.

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 a single, front-loaded sentence that effectively communicates the tools purpose and key inputs without redundant words. Each example is purposeful and helps clarify intents, making it an efficient, well-structured definition.

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 moderate parameter count, rich schema, and annotations, the description is largely sufficient for an agent to call it correctly. It captures the input types and purpose in one sentence. However, it misses minor behavioral details like the default limit (5) or that results are returned as a list of bottles—though these are implied by the schema and the phrase 'bottle picks'. It is still fairly complete for what it is.

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 each parameter. The description adds little beyond the schema—it restates taste_preferences with examples and occasion with examples, and repeats that budget is in USD (already in schema's description). While the examples are helpful, they don't substantially move beyond what the schema already says, so a 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 states a specific verb ('personalized bottle picks') and the resource (bottles selected from taste preferences, budget, occasion). This is clearly distinct from sibling tools like search_bottles (which implies searching) or trending_bottles (which implies popularity), and the inputs are provided upfront. It adds specificity beyond the title by naming exactly what kind of 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 strongly implies usage: when the agent needs personalized suggestions based on taste, budget, and occasion, this is the tool to call. But it does not explicitly mention when not to use it or recommend alternatives like search_bottles or pour_tonight_suggestion. The use cases are implied through the parameter examples, but there is no explicit 'use when...' or 'if you need... instead'. This is adequate but lacks strong discrimination.

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