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brayangcastro

industrial-mcp

trigger_motor_action

Start or stop an industrial motor safely. Dry-run checks preconditions first; execute by setting dry_run=False, providing operator ID and reason, and server write permission. Executed actions are audit-logged.

Instructions

Request a start or stop of an industrial motor.

Defaults to a DRY RUN: returns what would happen, evaluates safety preconditions, and lists warnings — without sending any command to the field. To actually execute:

  1. Pass dry_run=False.

  2. Pass operator_id (recorded in the audit log).

  3. Pass reason (short free-text justification).

  4. The server itself must be started with INDUSTRIAL_MCP_ALLOW_WRITES=true; otherwise the call is rejected even when the LLM sets the flags.

Every executed command is appended to an audit log; dry-run calls are not logged.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
actionYes
reasonNo
dry_runNo
motor_idYes
operator_idNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Behavior5/5

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

Given there are no annotations, the description carries full responsibility for disclosing behavior. It extensively covers the dry-run default, safety precondition evaluation, no field command on dry-run, audit logging for executed commands, and the server flag that gates writes. This is exceptional transparency for a mutation tool.

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?

The description is well-structured with a numbered list and every sentence provides distinct value. It is concise yet comprehensive, covering behavior, prerequisites, and security considerations without redundancy.

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?

For an industrial control tool with no annotations, the description thoroughly covers execution requirements, safety behavior, and logging. The presence of an output schema reduces the need to explain return values. It is complete for its intended use.

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

Parameters4/5

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

Schema description coverage is 0%, so the description must compensate. It explains the meaning and purpose of dry_run, operator_id, and reason effectively within the execution steps. It does not explicitly define motor_id and action, but those are straightforward (motor_id from name, action from enum). This good partial compensation earns a 4.

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 'Request a start or stop of an industrial motor' with a specific action and resource. This distinguishes it from sibling tools like list_motors and get_active_alerts, which are read-only.

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?

The description gives explicit step-by-step instructions for executing a real action, including passing dry_run=False, operator_id, and reason, plus the server-side environment requirement. It does not explicitly mention when not to use the tool or compare it to alternatives, but the context is clear and practical.

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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