データ概要の確認

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

Annotations already declare the tool as read-only, idempotent, and non-destructive, so the safety profile is well covered. The description adds valuable behavioral context by revealing that the tool performs datetime column estimation and returns dimension/metric candidates, which conveys that it does heuristic analysis rather than just raw listing. No contraindication with annotations.

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 compact sentences, front-loading the core purpose and then providing practical workflow guidance. Every sentence earns its place—no fluff, no repetition of schema details.

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 the tool's moderate complexity (3 optional params, no output schema, one sibling), the description covers the essential context: what it does, what it returns, and its place in the workflow. It could theoretically mention error handling or return format specifics, but the schema already handles param constraints, and the description sufficiently orients the agent for a read-only inspection tool.

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 description coverage is 100%, with each parameter (rows, file_path, datetime_column) already well-documented including format constraints and mutual exclusivity. The description does not add substantive parameter-level detail beyond what the schema provides, so the baseline of 3 applies.

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 uses a specific verb ('確認する' - inspect) and clearly identifies the resource (data contents). It enumerates the concrete outputs (columns, datetime column estimation, date range, dimension/metric candidates), which precisely scopes the tool's function. It also distinguishes itself from the sibling metric_shift_localize by explicitly positioning this as the pre-check step.

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 states exactly when to use the tool: before analysis and before calling metric_shift_localize. It explicitly recommends verifying column names and date range here prior to localization, giving clear workflow context. While it doesn't mention when not to use it, the recommendation is unambiguous.

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