analyse_dataset

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

No annotations are provided, so the description carries the full burden of behavioral disclosure. It implies a read-only inspection, but does not state that it has no side effects, whether a dataset must be loaded first, or any cost/performance traits. The 'Returns' block largely restates what an output schema would already cover.

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 short and front-loads the core action. The explicit 'Returns' section is somewhat redundant since an output schema exists, but it costs only one line and does not obscure the main point.

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 inspection tool with an output schema, the description covers what the tool does and what comes back. The main omission is the precondition/side-effect profile, which matters more because no annotations exist to cover it.

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 (empty object schema), so there is nothing to document or compensate for. Baseline 4 applies; the description introduces no parameter-related ambiguity.

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 states a specific verb+resource ('Analysiert das Dataset') and enumerates the returned content (GML-Version, CRS, LoD, Anzahl Gebäude), which distinguishes it reasonably well from siblings like number_of_buildings. However, it does not explicitly contrast itself with overlapping siblings such as list_datasets or number_of_buildings.

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 alternatives, nor prerequisites such as whether a dataset must first be loaded via load_dataset. The only implied usage is that it inspects a dataset, which the reader must infer.

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