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 discloses the tool's behavior well for a parameterless read-only catalog: it returns a listing of all published datasets ('一覧'), and it defines the content scope (numbers, primary sources, methodology). The word '公開している' implies public data with no side effects. It does not describe output formatting or pagination, but for a 0-parameter reference tool these gaps are minor.

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 tight sentences: the first names what the tool is, the second explains the catalog's contents and when to use it. Every clause earns its place, and the core purpose is front-loaded. No filler, no redundancy with the empty schema.

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?

For a low-complexity tool (0 params, no output schema, no annotations), the description is nearly complete: an agent knows what it will get (a list of all datasets), what information that list contains (numbers, sources, methodology), and when to invoke it (before citing). The only missing piece is a concrete description of the return format, but '一覧' (list) already hints at it, and nothing critical is absent.

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 has 0 properties, so per the rubric the baseline is 4. There are no parameters to document, and the description still adds value by clarifying what the tool returns (the full dataset listing) without requiring any arguments. Nothing more is needed.

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 states a specific resource and action: '暮らしグラムが公開している全データセットの一覧' (a list of all datasets published by Kurashi Gram). The word '全' (all) makes clear this is the meta-index covering every data tool, which inherently distinguishes it from the individual sibling data tools (answers, cheapest, gov_money, etc.). It also specifies what the catalog contains: which numbers, from which primary sources, and how they are produced.

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 provides an explicit usage trigger: '引用する前にここを見る' (look here before citing). This tells the agent when to consult this tool. However, it does not name alternatives or state when not to use it, so it stops short of a full when/when-not specification.

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