post_dataset

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It states this is a registration (write) operation and mentions a return value ('登録結果'), but lacks critical details: authentication requirements, whether registration is idempotent or reversible, rate limits, error conditions, or what the return result contains. For a mutation tool with zero annotation coverage, this is a significant gap.

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 appropriately sized and front-loaded with the core purpose first. The Args/Returns sections are structured clearly, though the Japanese text might require translation for some agents. Every sentence earns its place, with no redundant information.

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 complexity (4 parameters, mutation operation) and the presence of an output schema (which handles return values), the description is moderately complete. It covers the basic purpose and parameters but lacks behavioral context (e.g., side effects, permissions) and detailed usage guidelines. With no annotations and incomplete parameter documentation, it's adequate but has clear gaps.

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 0%, so the description must compensate. It lists all 4 parameters with brief explanations (e.g., '統計表ID' for stats_data_id, '取得条件' for conditions), adding basic semantic meaning beyond the schema's type definitions. However, it doesn't provide format examples, constraints, or usage details (e.g., what cdCatXX, cdTime, cdArea mean in conditions), leaving parameters partially documented.

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's purpose as 'データセットを登録する' (register a dataset), which is a specific verb+resource combination. It distinguishes itself from sibling tools that are primarily get/search operations (e.g., get_dataset, get_stats_data, search_stats_by_keyword), though it doesn't explicitly name alternatives for dataset registration.

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 no guidance on when to use this tool versus alternatives. It doesn't mention prerequisites, constraints, or relationships with sibling tools (e.g., whether you need to use get_stats_data first to obtain stats_data_id). Usage is implied only by the tool's name and purpose.

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