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select_machines

Preview which machines a selector picks before naming them, so you can verify you're targeting the right group and avoid unintended machines.

Instructions

Preview which machines a selector picks, before naming them.

Worth running first on anything product-based: 17 machines make Concrete on the reference save, but 15 of them are a construction feed inside the steel site and only one is the player's "concrete setup".

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
saveNo
splitNokeep only the largest spatial cluster
worldNo
expandNopull in everything belted to the result
selectYesselector terms, ANDed. product:<item> | recipe:<name> | building:<class or name> | near:<x,y@radius_m or label@radius_m> | base:<n> | line:<n> | slab:<n> | proposal:<n> | label:<name> | all. Terms are ANDed; comma-separated values inside one term are ORed; prefix a term with '-' to exclude it
Behavior4/5

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

Without annotations, the description carries the burden. It discloses a read-only preview nature and reveals a non-obvious behavioral trait: a selector can match unintended machines (17 matching, only 1 the player's setup). It does not detail output format or side effects beyond the preview implication, which would push it to a 5.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Two short paragraphs with a clear first-sentence purpose and a practical example that reinforces usage. Every sentence earns its place, and the structure fronts the key information effectively.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness3/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a moderate-complexity tool with no output schema, the description covers purpose and usage intent but lacks explicit return-value expectations or prerequisites. The illustrative example partially compensates, but an agent might still be uncertain about the exact output format.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The description adds no parameter-level details, but the input schema provides useful descriptions for select, split, and expand. The optional save and world parameters are simple and self-explanatory, and the main selector syntax is fully documented in the schema, so the description need not duplicate.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool's function: 'Preview which machines a selector picks, before naming them.' It uses a specific verb ('Preview') and resource ('machines'), and explicitly ties it to the naming workflow, distinguishing it from sibling tools like name_factory.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Provides strong situational guidance: 'Worth running first on anything product-based' and illustrates the risk with a concrete example. However, it does not explicitly name alternative tools or state when not to use it, leaving the exclusion boundary implicit.

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

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