Compare price levels for a food product across the supply chain: farm gate (USDA AMS), wholesale/terminal market, BLS national retail average, and Kroger real-time retail. Shows markup at each level. Great for understanding food economics.
| Name | Required | Description | Default |
|---|---|---|---|
| product | Yes | Product name: eggs, ground beef, lettuce, milk, chicken, bread, etc. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden. It states the tool compares prices across levels and shows markup, implying a read-only operation. However, it does not mention data freshness, rate limits, or whether the comparison includes historical data. The behavioral profile is partially clear but lacks important details.
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. The first delivers the core purpose and scope. The second adds a clear value proposition ('Great for understanding food economics'). No filler words. Very compact and front-loaded.
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 agent must infer return format from the description. The description explains what the tool does (compare levels, show markup) but not the structure of results (e.g., per-level price, markup percentage, timestamp). For a tool with no annotations and no output schema, this is a moderate gap. It covers the 'what' but not the 'how' of returns.
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 description coverage is 100%: the single 'product' parameter has a description listing examples. The tool description reiterates 'eggs, ground beef, lettuce, milk, etc.' which adds marginal value. With full schema coverage, the baseline is 3, and the description does not provide additional semantics (e.g., case sensitivity, accepted formats).
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 specific verbs and nouns: 'Compare price levels for a food product across the supply chain'. It lists concrete levels (farm gate, wholesale, retail, Kroger) and notes it shows markup. This clearly distinguishes from sibling tools like 'food_commodity_prices' (likely broader) and 'food_retail_prices' (likely less granular).
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 ends with 'Great for understanding food economics', which implies a use case but does not explicitly state when to use this tool versus alternatives. With 17 sibling tools, more guidance (e.g., 'Use for supply chain markup analysis; for raw commodity prices, see food_commodity_prices') would help the agent. Current guidance is vague.
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.