Get Kroger-specific product details: real-time pricing, aisle/shelf location, stock level, fulfillment options (curbside/delivery/in-store), ratings, SNAP eligibility, organic/GMO claims, allergens, ingredients, nutrition, country of origin, and product images.
| Name | Required | Description | Default |
|---|---|---|---|
| upc | No | UPC barcode to look up | |
| limit | No | ||
| query | No | Search Kroger products by name | |
| kroger_id | No | Kroger product ID |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description must convey behavioral traits. It lists return attributes but does not disclose whether authentication is needed, rate limits, side effects (none expected), or error behavior. The tool is read-only by implication ('Get'), but no explicit statement about safety or constraints.
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 a single sentence that front-loads the main purpose and then lists specific features. It is efficient with no wasted words, though the list of attributes is long. Slightly more structured formatting (e.g., bullet points) could improve readability, but it remains clear and concise.
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 compensates by listing many return attributes, providing reasonable context for what the tool returns. However, it lacks guidance on how to use parameters (e.g., priority between upc, query, kroger_id) and does not mention pagination, error handling, or result limits. Completeness is adequate for a simple lookup tool but could be richer.
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 75% (3 of 4 parameters described). The description adds value by listing the detailed return fields, which helps understand what each parameter influences. However, it does not add direct meaning or usage constraints beyond what the schema already provides for individual parameters. 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?
Description clearly states 'Get Kroger-specific product details' and lists numerous specific attributes (pricing, aisle/shelf location, stock level, etc.). This distinguishes it from sibling tools like food_search (generic search) and food_retail_prices (pricing-only). The verb-resource combination is specific and unambiguous.
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 retrieving Kroger product details but does not explicitly state when to use it vs. alternatives (e.g., food_search, food_retail_prices). No guidance on prerequisites or exclusions. The term 'Kroger-specific' provides some context, but explicit when-not-to-use or alternative tool references would improve clarity.
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.