Get detailed nutrition facts for a specific food product. Returns calories, protein, fat, carbs, vitamins, minerals, ingredients, allergens. If product exists in Kroger, also returns real-time price, aisle location, stock level, ratings, SNAP eligibility.
| Name | Required | Description | Default |
|---|---|---|---|
| upc | No | UPC/barcode to look up directly | |
| kroger_id | No | Kroger product ID for direct Kroger lookup | |
| product_id | No | Product ID from food_search results (products table) |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Given no annotations, the description discloses the conditional behavior (Kroger data if product exists) and the read-only nature. It does not mention authentication needs or rate limits, but the core behavioral traits are clear.
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, front-loaded with the core purpose, and every sentence adds value. No redundant or 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?
No output schema exists, so the description reasonably explains return values (calories, protein, etc.) and conditional Kroger fields. It lacks guidance on input selection logic (which param to use) but covers the key information for an AI agent.
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%, and the description reuses parameter names from the schema without adding extra meaning. The schema itself already describes each param, so the description adds no new semantic value beyond what's in the schema.
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 retrieves nutrition facts for a specific food product, listing specific nutrients and Kroger-specific data. It distinguishes from sibling tools by focusing on nutrition facts plus optional retail data.
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 nutrition lookup but provides no explicit guidance on when to use this tool versus alternatives like food_kroger_product or food_search. No when-not or alternative suggestions.
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.