Search Japanese Food Database

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations already indicate readOnlyHint=true and idempotentHint=true, so the tool is safe and idempotent. The description adds valuable behavioral context: each result includes a confidence level, source URL, and the note that values are AI-compiled from sources (manufacturer labels, nutrition PDFs, MEXT database, or estimates) and not individually verified. This goes beyond annotations to set appropriate expectations.

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 English description is front-loaded with the core purpose and returns structure. It is concise but includes important caveats. The bilingual repetition (English then Japanese) adds length but is justified for a Japanese food database tool. Every sentence adds value, and the structure is logical.

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 what is returned: macros, allergens, cited source, confidence level, and source URL for each item. It also describes the database size (460 items), source types, and the AI-compiled nature. This provides an agent with a complete picture of the tool's behavior and output.

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 the schema already documents all parameters. The description adds some context by mentioning English/Japanese keywords and categories, but these are already covered in the schema descriptions. Thus, the description adds minimal new semantic meaning beyond what the schema provides.

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 it searches a 460-item Japanese food database by keyword, returning macros, allergens, and source. The title 'Search Japanese Food Database' reinforces the purpose. However, it does not explicitly differentiate from sibling tools like 'konbini_item' or 'restaurant_meal', which may have similar search functionality.

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 searching for food items by English or Japanese keywords. It also cautions that values are AI-compiled and not human-verified, setting expectations. However, it does not provide explicit guidance on when to use this tool versus alternatives like 'konbini_item' or 'analyze_meal', nor does it specify when not to use it.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.