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set_signal

Set a signal mast aspect on your DCC layout via JMRI. Send a valid aspect name and receive confirmation of the actual observed aspect, including hardware feedback.

Instructions

Set a signal mast's aspect, and report the aspect actually observed.

Args: name: Signal mast name (JMRI system name or user-friendly label) or an unambiguous fragment. Case-insensitive. aspect: The aspect to request, e.g. "Hp0" (stop), "Hp1" (proceed), "Hp2" (proceed reduced speed) for a German DB-HV-1969 mast — valid names depend on this mast's configured signal system and are NOT validated locally (JMRI doesn't expose a valid-aspect list); JMRI validates server-side and this tool reports rejection as an error. If unsure, call get_signal/list_signals first to see the current aspect as a naming-style example, or ask the user rather than guessing.

Writes to JMRI, and on masts driven by external hardware (DCC accessory decoder, microcontroller) changes the real physical signal. An unknown aspect is reported as an "error". A valid aspect is re-read after the command; if the observed aspect still doesn't match, "confirmed" is false — report that honestly, not as success, since unresponsive hardware can cause this even when JMRI itself accepted the change.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
nameYes
aspectYes
Behavior5/5

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

With no annotations, the description fully discloses that it writes to JMRI, affects physical hardware, explains error handling, and the 'confirmed' field indicating possible hardware unresponsiveness.

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

Conciseness4/5

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

The description is well-structured with an Args section and explanatory paragraphs. While slightly verbose, each sentence adds critical value, so it earns its length.

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

Completeness5/5

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

Despite no output schema, the description details return behavior (observed aspect, error, confirmed flag). All parameter semantics are covered, and usage context is complete for safe invocation.

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

Parameters5/5

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

Schema coverage is 0%, so description compensates thoroughly: explains name as system name, label, or fragment (case-insensitive), and aspect with examples, dependency on signal system, and validation behavior.

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 action (set) and resource (signal mast's aspect), and includes the outcome of reporting the observed aspect. It distinguishes itself from read-only siblings like get_signal and list_signals.

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

Usage Guidelines5/5

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

Explicitly advises when to use and when not to guess, recommends calling get_signal/list_signals first if unsure, and warns that aspects depend on the configured signal system and are not validated locally.

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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