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ruvltra_scale_workers

Scale the number of active workers to a target between 1 and 32, enabling dynamic capacity adjustment for parallel code generation and review workloads.

Instructions

Scale worker pool size within configured min/max.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
targetYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
statusYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.2.8

TDQS

A3.7/5.0
Behavior2/5

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

There are no annotations, so the description carries the full burden of explaining side effects. It only mentions that scaling respects configured min/max bounds; it does not disclose whether the operation mutates running work, whether changes are immediate or reversible, or any permission implications. This is thin for a state-changing operation.

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 a single sentence with no filler. It front-loads the action, names the resource, and embeds the key constraint efficiently.

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 one-parameter scaling tool with an output schema present, the description is minimally viable: it states the operation and the constraint. However, without annotations or any behavioral/side-effect context, the overall picture is incomplete for an agent deciding whether this call is safe or how it affects ongoing work.

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 succeeds in linking the lone 'target' parameter to the intended worker pool size and adds the meaningful constraint that the value must fall within configured min/max. An agent can infer that 'target' is the desired new pool size even though the parameter name is not explicitly restated.

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 uses a specific verb ('Scale') with a clear resource ('worker pool size') and a visible constraint ('within configured min/max'). No sibling tool overlaps with this operation, so an agent can distinguish this from the code-generation and status tools without needing to inspect further.

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

Usage Guidelines3/5

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

The purpose itself implies when to use it: an agent should call this when it needs to resize the worker pool. However, the description provides no explicit when-to-use guidance, no exclusions, and no mention of alternatives, leaving the decision mostly to inference.

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