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Glama

scale_replicas_up

Destructive

Increase a deployment's replica count to handle higher inference load. Preview the scaling with dry_run=True and revert to the prior count if needed.

Instructions

[WRITE][risk=medium] Raise a deployment's replica count (reversible → prior).

Pass dry_run=True to preview: it reads the deployment's current count, so the preview reports the real from→to rather than only the requested target — and it fails here, not mid-write, if the deployment does not exist.

Args: application: Serve application name. deployment: Deployment name. num_replicas: New (higher) replica count. dry_run: If True, preview without scaling. target: Inference target name from config; omit for the default.

Input Schema

NameRequiredDescriptionDefault
targetNo
dry_runNo
deploymentYes
applicationYes
num_replicasYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.6.0

TDQS

A4.3/5.0
Behavior4/5

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

Annotations already declare readOnly=false, idempotent=false, destructive=true; the description adds meaningful context beyond that — medium risk, reversibility to the prior state, and the fact that dry_run reads the live current count and fails early if the deployment is absent. The 'reversible' framing usefully softens the destructiveHint, though no auth/permission or undo mechanics are detailed.

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?

Well front-loaded: the [WRITE][risk] tag, the core action, the dry_run rationale, then Arg docs. The dry_run sentence is longer than strictly necessary, but it conveys a non-obvious failure-mode benefit rather than padding.

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

Completeness4/5

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

For a mutating tool with no output schema, the description covers direction, reversibility, dry-run semantics, and every parameter, leaving little an agent needs before calling it. Missing only permission/auth prerequisites and any rate/limit behavior.

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 coverage is 0%, so the description must carry the burden, and it documents all five parameters including the non-obvious ones: num_replicas is constrained to a higher value, and target is 'from config; omit for the default'. Entries for application/deployment are brief but sufficient alongside their required status.

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?

States a specific verb and resource ('Raise a deployment's replica count'), immediately distinguishing it from the sibling scale_replicas_down and scale_to_zero. The [WRITE][risk=medium] tag further classifies the operation, so an agent identifies it without opening the schema.

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?

Gives clear operational guidance for dry_run=True, including why the preview is more accurate than a naive target-only check. It does not explicitly name the direction-based alternatives (scale_replicas_down / scale_to_zero) as exclusions, but the 'raise ... (higher) replica count' framing implies the boundary.

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