stats

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

With no annotations provided, the description carries the full burden but only states that it returns a breakdown. The verb '返す' implies a read-only aggregation, but there is no mention of side effects, authentication, data scope, or edge cases such as empty results.

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?

A single, front-loaded sentence with no filler. Every element (breakdown, grouping dimensions) earns its place and nothing is redundant.

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 no-parameter stats tool, the description tells the agent what the tool returns and how data is grouped, which is the core information needed. It lacks an explicit output shape, but with no output schema the description still communicates the essential return semantics clearly enough for correct invocation.

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?

There are zero parameters, so the input schema already fully covers parameters (vacuously). The description adds useful context about the output dimensions, which is more than required for parameter semantics; baseline 4 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 states a specific verb ('返す' returns) and resource (件数の内訳, a breakdown of counts) with explicit grouping dimensions (year, disposition type, article). This clearly distinguishes it from siblings that retrieve individual cases or case lists.

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 the tool is for aggregate statistics rather than case retrieval, so an agent can infer when to use it. However, it does not explicitly name conditions, exclusions, or alternatives compared to siblings like search_cases or list_recent.

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