get_light

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

Discloses that the tool has no side effects, explains parameter semantics (system name or label fragment, case-insensitive), and clarifies the scope (layout light vs. locomotive headlight). Without annotations, this is detailed enough.

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 concise and well-structured, using separate paragraphs for parameter details and usage guidance. Slightly verbose but still effective.

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?

Given the simplicity of the tool (one parameter, no output schema), the description covers purpose, parameter semantics, side effects, and distinguishes from a related tool. Adequately complete.

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 only provides type and title for 'name'; description adds concrete examples (IL1, Depot Lighting), mentions acceptability of fragments, and case-insensitivity. This significantly enhances understanding.

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 clearly states 'Get the current ON/OFF state of one layout light,' providing a specific verb and resource. It also distinguishes from locomotive headlight queries, aiding in tool selection.

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?

Explicitly tells when not to use this tool and provides an alternative: 'for "is the Autorail's headlight on", use get_locomotive_functions instead.'

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