Find food products that work for ALL members of a household with different dietary needs. Example: person A needs low-fat (post-gallbladder), person B needs high-protein (GLP-1). Finds products satisfying ALL constraints simultaneously.
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | ||
| members | Yes | Household members with individual dietary constraints | |
| category | No | Product category filter | |
| max_price | No | Maximum price per item |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries full burden. It discloses that the tool performs intersection filtering ('ALL constraints simultaneously'), which is useful. However, it does not mention behavior when no products satisfy all constraints, potential performance issues, or data freshness.
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 purpose, followed by a concrete example. No extraneous words, highly efficient.
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 could hint at return format (e.g., 'returns list of products with details'). For a tool with 4 parameters including a nested array, it is reasonably complete but lacks explanation of output structure.
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 have descriptions). The description adds no additional parameter semantics beyond what the schema already provides, but the example helps contextualize how constraints are combined. Baseline score 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?
The description clearly states the tool finds food products satisfying all dietary constraints of multiple household members simultaneously. The example with specific dietary needs (low-fat, high-protein) illustrates the purpose distinctively from sibling tools like 'food_diet_filter' and 'food_meal_recommend'.
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 use when needing to find products for a household with differing dietary needs, and the example provides concrete context. However, it does not explicitly state when not to use this tool or mention alternatives like 'food_diet_filter' for single-person filtering.
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.