select

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

With no annotations, the description carries the burden of behavioral disclosure. It states the primary behavior (creating a selection) and the return value (atom count), but does not mention edge cases such as overwriting an existing selection, behavior on invalid selection expressions, or prerequisite loaded structures. This leaves some gaps.

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 a single sentence, front-loaded with the action, and contains no filler. Every word contributes to understanding the tool's purpose and result.

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?

The tool is simple with only two parameters, and an output schema exists (though not shown). The description states the return value (atom count), so it does not need to elaborate on return format. It could mention side effects like overwriting, but overall it is reasonably complete for the tool's complexity.

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 coverage is 100% and both parameters are well described in the schema. The description adds no extra parameter semantics beyond restating the purpose, so the baseline of 3 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 uses a specific verb ('Create'), identifies the resource (named selection), and states the return result (how many atoms matched). This clearly distinguishes it from sibling tools like get_selection_info, which likely reads existing selections.

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 implies the use case: when you want to create a named selection and get its atom count. It doesn't explicitly name alternatives or exclusions, but the context is clear enough that an AI agent can infer when to invoke it versus querying selection info.

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