Look up ingredient lists and allergens for branded food products. Shows every ingredient with the data source origin. Essential for dietary restrictions, allergies, and MAHA transparency.
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | ||
| query | Yes | Product name or brand to look up ingredients for | |
| allergen | No | Filter for specific allergen: gluten, dairy, nuts, soy, eggs, shellfish |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the burden. It discloses that the tool shows every ingredient with data source origin, which adds behavioral context. However, it does not mention any destructive actions, auth requirements, or rate limits. Given no annotations, this is adequate but not exceptional.
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?
Two sentences, front-loaded with the core action, no fluff. Every sentence adds value.
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?
The description covers the main functionality and output (ingredients, allergens, data source). However, it does not explain the return format, pagination, or behavior when no results are found. Given no output schema, more context would help, but it is minimally adequate.
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 67% (descriptions for query and allergen, not for limit). The description does not add semantic detail beyond the schema; it does not explain the limit parameter or provide examples. With moderate schema coverage, the description should compensate but does not, so score is baseline 3.
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 verb 'Look up' and the resource 'ingredient lists and allergens for branded food products'. It distinguishes from siblings like food_nutrition and food_diet_filter by focusing on ingredient details and allergen filtering.
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 mentions use cases ('dietary restrictions, allergies, MAHA transparency') but does not explicitly state when not to use this tool or mention alternatives. It provides usage context but lacks exclusion criteria.
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 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.