get_health_nutritionMachine-readable nutrition per beer for health, fitness, and diet-tracking platforms: per-can and per-100ml calories, carbs, protein, sugar, ABV, allergens, and dietary flags.
| Name | Required | Description | Default |
|---|---|---|---|
| slug | No | Optional beer slug; omit for the full lineup |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden. It adds value by specifying per-can and per-100ml values, allergens, and dietary flags. However, it omits details like data source freshness, mutation behavior, or any quotas, making it adequate but not rich.
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?
A single sentence that front-loads the purpose and efficiently lists output fields. 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 retrieval tool with one optional parameter, no output schema, and no annotations, the description sufficiently explains the output format (per-can, per-100ml values, specific fields) and use case (health platforms). It is 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?
Schema coverage is 100% with a single optional slug parameter (enum). The description does not add extra meaning beyond the schema's description of 'omit for full lineup'. 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 it returns machine-readable nutrition data per beer, listing specific fields (calories, carbs, protein, sugar, ABV, allergens, dietary flags) and context (health, fitness, diet-tracking). It distinguishes from siblings like get_beer (general info) and list_beers (listing).
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 for health and diet-tracking platforms, but does not explicitly state when to avoid using it or mention alternatives beyond implied sibling differentiation. No exclusions provided.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.
Each tool has a clearly distinct purpose covering different aspects: beers, brand, process, pricing, nutrition, recipes, subscriptions, wallet, wholesale, buy link, and waitlist. No two tools overlap significantly.
Most tools use the 'get_' prefix for information retrieval, with 'list_beers' and 'join_waitlist' as slight deviations. However, the pattern is consistent (verb_noun with underscores) and clear.
13 tools is well-scoped for a brewery server, covering all essential aspects without being overwhelming. Each tool serves a specific need, and none feel superfluous.
The tool set covers the full lifecycle: brand info, product details, pricing, ordering, subscriptions, waitlist, wallet, and even recipes and nutrition. No obvious gaps for the stated domain.