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

With no annotations, the description must carry the full burden of behavioral disclosure. It reveals that the tool returns survey prices for the latest month and monthly history, and specifies the data source and number of cities. However, it omits details about error handling for invalid codes, output structure, or any side effects. This partial disclosure earns a mid-range score.

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 two sentences, front-loading the core purpose and then adding necessary details (source, item count, time range, and code examples). Every piece of information is relevant, and there is no redundancy or filler.

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 there is no output schema, the description partially explains what will be returned: survey prices for ~550 items with latest month and monthly history. However, it does not specify the exact output structure (e.g., whether it returns a list, a summary, or time-series format), nor does it address edge cases like invalid codes or unsupported cities. This leaves some ambiguity for an agent.

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?

The input schema provides only '5桁の自治体コード' for the 'code' parameter. The description enhances this by explaining the code is a 5-digit national local government code and provides concrete examples (Sapporo 01100, Tokyo wards 13100, Fukuoka 40130). This adds meaningful context beyond the schema, clarifying the format and usage.

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 provides cost-of-living data for 81 Japanese cities, sourced from a specific government survey, covering ~550 items with latest month and monthly history. It explicitly describes the resource (city-level price data) and the scope, making it distinct from sibling tools like cost_item or cost_index which likely focus on individual items or indices.

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 provide any guidance on when to use this tool versus alternatives. It merely states what the tool does, leaving the agent to infer selection criteria. There is no mention of preferred use cases, exclusions, or conditions that would route to a different sibling tool.

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