Compare nutrition facts between 2-5 food products side by side. Great for "which is healthier" questions. Shows per-serving and per-100g when available.
| Name | Required | Description | Default |
|---|---|---|---|
| nutrients | No | Specific nutrients to compare (default: all major) | |
| product_ids | Yes | Product IDs to compare (2-5) |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description must disclose behavioral traits. It states 'Shows per-serving and per-100g when available,' revealing output format behavior. It does not cover error handling or authentication, but the core behavioral context is well-addressed.
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 concise sentences, front-loaded with the core purpose, and every word adds value. No redundancy or fluff.
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 no output schema and no annotations, the description covers purpose, usage guidance, parameter defaults, and output format (per-serving and per-100g). It could mention cross-category comparison or units, but overall it is sufficiently complete for a comparison 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 coverage is 100%, so both parameters are documented. The description adds value by clarifying the default behavior for the 'nutrients' parameter ('Specific nutrients to compare (default: all major)'), exceeding what the schema alone provides.
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 uses a specific verb ('Compare') and resource ('nutrition facts between 2-5 food products'), clearly distinguishing from siblings like food_nutrition (single product) and food_price_compare (price focus). It also provides a common use case ('which is healthier').
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 phrase 'Great for "which is healthier" questions' provides clear context for when to use the tool. However, it does not explicitly state when not to use it or mention alternatives, though the context of sibling tools implies differentiation.
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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Each tool targets a specific aspect of food data: prices, nutrition, recalls, dietary filters, supply chain, etc. Overlaps are minimal and clearly differentiated by scope, such as full nutrition vs. ingredient lists.
All tools consistently use the 'food_' prefix followed by a descriptive snake_case term. While the stems vary between nouns and verbs, the pattern is uniform and predictable.
With 18 tools, the server comprehensively covers the grocery domain including prices, nutrition, recalls, dietary needs, supply chain, and more. Each tool serves a distinct purpose without being overwhelming.
The tool set is remarkably complete, covering search, detailed product info, price comparisons across supply chain, dietary constraints, household meal planning, recalls, receipts, and data source transparency. No critical gaps apparent.