Get Dataset Metadata

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

The annotations already declare readOnlyHint=true, destructiveHint=false, and openWorldHint=false, so the safety profile is covered. The description adds a contents inventory (label/type/format, creation time), which helps set expectations, but it discloses nothing about failure behavior for missing paths or dataset size limits. With annotations carrying the safety burden, a 3 is appropriate.

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?

It is a single compact sentence that lands the resource first and then lists return contents; every clause earns its place with zero filler.

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?

An output schema exists, so return structure need not be explained, and the one required parameter is fully documented. The only missing piece for a complete definition is guidance on when to prefer this over summarize_dataset.

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% and the single path parameter is fully documented in the schema with a relative/absolute example. The description adds nothing about the path argument, so the baseline 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?

It gives a specific verb+resource ("获取数据集元数据") and enumerates exactly what metadata is returned (row count, per-variable label/type/format, file label, creation time). This distinguishes it in substance from list_datasets and read_dataset, but no sibling is named explicitly.

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?

There is no statement of when to use this tool versus read_dataset or summarize_dataset, and no prerequisites, such as whether the dataset must exist or whether a path prefix is required. Usage must be inferred entirely from the contents list.

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