list_datasets

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

No annotations are supplied, so the description carries the full disclosure burden. 'Listet ... auf' plus the Returns block effectively communicates that this is a non-mutating enumeration and reveals which dataset formats exist (GML or JSON), but it does not state ordering, whether the directory is fixed, or what an empty result means.

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?

Two short sentences, front-loaded with the action and resource, no padding. The 'Returns: list[str]' block is largely redundant given that a structured output schema exists, costing it a perfect score.

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 zero-parameter, read-only listing whose return type is covered by the output schema, the description covers what is needed to call it. Minor gaps remain around ordering, the location of the 'data directory', and behavior on an empty directory.

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 tool takes zero parameters, so there is nothing for the description to clarify; the baseline for a no-argument tool is 4.

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?

States a specific verb and resource — listing all available GML/JSON datasets in the data directory — which an agent can read unambiguously. It does not, however, distinguish itself from the many dataset-oriented siblings (load_dataset, filter_dataset, analyse_dataset, create_dataset), so the agent must infer which one to pick.

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 when-to-use guidance, no mention of prerequisites, and no reference to an alternative tool. With siblings like load_dataset and get_dataset_history in the same family, the description should say something like 'use to discover datasets before loading one', but says nothing.

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