Restaurant Chain Meal Lookup

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

The description discloses key behaviors: data is AI-compiled from official sources with estimates flagged, no human verification, modifications recorded but not applied, and some chains returning no items. These go beyond the readOnlyHint and idempotentHint annotations, providing important caveats about data quality and current limitations.

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 front-loaded with the main action and provides essential details in a logical order. Including both English and Japanese adds length but serves the bilingual audience. Every sentence adds value, though the Japanese translation could be considered redundant for monolingual users.

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?

The description covers data provenance, accuracy caveats, default size behavior, and chain availability (some coming soon). Missing output format details are compensated by the tool's purpose (lookup); the description gives enough context for a user to decide to use it, even without an output schema.

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?

All parameters have schema descriptions (100% coverage), so baseline is 3. The tool description adds specific context: 'Default size is 並 (regular)' and 'modifications field is recorded but not yet applied to nutrition totals'. This extra information clarifies default behavior and modification handling 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 'Look up a restaurant chain meal with size variants' and details the AI-compiled data sources from official nutrition PDFs, allergen tables, and product labels. This immediately distinguishes it from sibling tools like search_food (general food lookup) and konbini_item (convenience store items). The purpose is specific and well-defined.

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 does not explicitly guide when to use this tool over alternatives. While it provides context about data sources and limitations, it lacks direct comparisons or exclusions. The user must infer from sibling names that this tool is for restaurant chain meals only.

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