Given a list of UPC barcodes (from a receipt or shopping list), return full nutrition breakdown, ingredient analysis, allergen warnings, recall checks, and total meal nutrition for everything purchased. The receipt-to-nutrition bridge nobody else has built.
| Name | Required | Description | Default |
|---|---|---|---|
| upcs | Yes | List of UPC barcodes from a receipt |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description must fully disclose behavioral traits. It lists outputs (nutrition, ingredients, allergens, recalls, total meal) but does not mention performance, authorization requirements, or error handling.
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 with essential information in the first. The second sentence is marketing fluff but not harmful. Could be slightly tighter.
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, the description thoroughly explains the return values (nutrition, ingredient analysis, allergen warnings, recall checks, total meal nutrition), making it complete for a lookup tool with one parameter.
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 baseline is 3. The description adds context that the UPCs come from a receipt but does not specify format constraints or behavior for invalid codes.
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 takes UPC barcodes from a receipt/shopping list and returns comprehensive nutrition, ingredient, allergen, recall, and total meal analysis. It distinguishes itself as a 'receipt-to-nutrition bridge' that no other sibling tool offers.
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 when you have a list of UPC barcodes from a receipt, but does not explicitly state when to avoid it or compare with sibling tools like food_nutrition or food_recall_check, which might handle subsets.
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.