create_dataset

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

No annotations are provided, so the description carries full behavioral burden. It usefully discloses the non-overwrite guarantee and that the dataset is loaded, but omits permissions, whether it becomes the active dataset, and concrete error behavior beyond 'Fehlermeldung'.

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?

Front-loads the purpose, then a practical note, then Args/Returns. Because the schema carries no descriptions, the Args block earns its place rather than repeating structured data; the padding is minimal.

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?

With an output schema present, return values need not be detailed, and the description covers the create-and-load behavior plus the non-overwrite constraint. For a creation tool with zero annotations it could say more about failure modes or whether it replaces the current 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 0%, so the description must compensate and largely does: it explains the '.json' (CityJSON) / '.gml' (CityGML) extension convention with an example ('test.city.json'), the optional title, and the epsg default meaning (25832 = ETRS89 / UTM 32N). It does not mark which parameter is required, a minor gap.

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 ('Erstellt ein neues, leeres Dataset als GML- oder JSON-Datei und lädt es'), including the accepted formats and the load side effect. It also distinguishes itself from siblings by naming the follow-on tools create_building() and save_dataset().

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?

Gives clear usage context ('Praktisch zum Testen') and an explicit guardrail ('Bestehende Dateien werden nicht überschrieben'). It does not name an alternative such as load_dataset for opening existing files, so it stops short of full when/when-not routing.

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