ue_list_editor_selection

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It clearly describes the return payload (JSON with success, selected_actors array containing name/class/location, and count) and illustrates it with an example call. It does not explicitly state whether the operation is read-only or has editor prerequisites, but the described behavior is otherwise transparent.

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 front-loaded with purpose, followed by a concise use case, a compact JSON return example, and an illustrative call. Every element adds value; the KB reference and example support accurate invocation without bloating the text.

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 query tool, the description provides everything an agent needs: purpose, use case, return format, and example. The presence of an output schema and the detailed JSON in the description make the return contract clear. No significant missing information for correct invocation.

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 schema coverage is trivially 100%. The description appropriately shows an example call ue_list_editor_selection() and makes clear no arguments are needed. The baseline of 4 for zero-parameter tools is appropriate.

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 clear action ('Return') on a specific resource ('what is currently selected in the Unreal Editor viewport'). It is not tautological and gives a concrete usage example to reinforce purpose. However, it doesn't explicitly contrast against sibling tools like find_actors_by_class or get_actors_in_level, so it stops short of the highest differentiation.

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?

The description gives a context ('Useful for context-aware scripting: what is the AI looking at right now?') that tells an agent when to call this tool. It does not state alternatives, exclusions, or conditions under which another tool would be more appropriate. The guidance is implied rather than explicit, warranting a mid-score.

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