number_of_buildings

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

With no annotations provided, the description carries the full behavioral burden. It discloses the return type (int) and that the count is scoped to the 'current dataset', which is useful context. It does not mention behavior when no dataset is loaded or any error conditions, leaving a modest gap for what is a trivially safe read operation.

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 with zero waste. The purpose is front-loaded and the return type follows immediately.

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 tool, the definition covers what is needed. Since an output schema exists, the explicit Returns block is mildly redundant, but the scoping phrase 'im aktuellen Dataset' adds genuinely useful context about which dataset is counted.

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 compensate for. The baseline for a no-parameter tool is a 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 combination: returns the count of buildings in the current dataset. The purpose is unambiguous. It does not, however, differentiate itself from the sibling get_all_buildings, which an agent could otherwise use to achieve the same count.

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 such as get_all_buildings, nor any stated precondition (e.g. that a dataset must already be loaded). Usage must be inferred entirely from the name and description.

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