list_stored_data

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 that the tool returns a list with specific fields like data_id, file_path, record_count, and provides a hint for accessing data. It does not mention any destructive behavior or special requirements, which is appropriate for a read-only listing tool.

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 includes a full return example and a hint, which are helpful but slightly redundant (the hint is repeated in the example). It could be more concise without losing clarity, but it is well-structured and front-loaded.

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 no parameters and an existing output schema, the description is complete. It explains the purpose, usage context, and provides an example return value, fully equipping an AI agent to use the tool correctly.

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 zero parameters and 100% schema coverage, but the description adds value by explaining the tool's purpose in the context of get_statistics_data and showing the return structure. Since there are no params, the description is sufficient.

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 verb '조회합니다' (retrieve) and the resource '저장된 원본 데이터 파일 목록' (list of stored original data files). It distinguishes itself from sibling tools like read_stored_data by indicating that this tool lists files and the other accesses specific data.

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 explains the workflow: large data from get_statistics_data is automatically saved to a file, this tool lists those files, and read_stored_data accesses them. This provides clear context on when to use this tool, though it does not explicitly state when not to use it or mention alternatives.

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