get_party_walls

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

No annotations are provided, so the description carries the full behavioral burden. It implies a read-only lookup and describes the return tuple, but says nothing about whether a dataset must be loaded, whether the scan is expensive on large datasets, or how empty results are handled. The return-value explanation is largely redundant given an output schema exists.

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 definition is short and front-loads the core purpose in one sentence. The 'Returns' block is verbose and duplicates what the output schema already provides, costing some efficiency.

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 no parameters and a separate output schema, the description covers the essentials of what the tool does. However, it omits the key operational precondition — that the tool acts on the loaded dataset — which an agent needs in order to invoke it correctly in sequence with load_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?

The tool takes zero parameters, so there is nothing to document and the baseline of 4 applies. Schema coverage is 100% with an empty object schema, so no parameter semantics are missing.

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 and resource ('Findet alle angrenzenden Wände (Party Walls) zwischen Gebäuden im Dataset'), which is unambiguous and matches the tool name. It does not explicitly contrast itself with siblings like analyse_dataset or get_all_buildings, but the resource is distinctive enough to disambiguate.

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 guidance on when to use this tool versus alternatives, nor any stated prerequisite such as needing a dataset loaded first. The only contextual hint is the phrase 'im Dataset', which implies it operates on the currently active dataset but never says so explicitly.

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