Skip to main content
Glama
GeiserX

spinnaker-mcp

by GeiserX

get_scaling_activities

Read-onlyIdempotent

Audit recent auto-scaling events and diagnose capacity issues for a Spinnaker server group by retrieving scale-up and scale-down activity records with timestamps, descriptions, and status.

Instructions

Get scaling activities for a server group in a Spinnaker cluster. Use this to audit recent auto-scaling events (scale-up, scale-down) and diagnose capacity issues. Returns JSON array of scaling activity records with timestamps, descriptions, and status.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
accountYesSpinnaker account name
providerNoCloud provider (e.g. aws, gce, kubernetes)
applicationYesApplication name as registered in Spinnaker
cluster_nameYesCluster name as shown in the Spinnaker UI
server_group_nameYesServer group name (e.g., myapp-v001)

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed2 schema fields changedv0.3.4
    • changedInput schema / properties / cluster_name / description
      Previous value: -"Cluster name"New value: +"Cluster name as shown in the Spinnaker UI"
    • changedInput schema / properties / server_group_name / description
      Previous value: -"Server group name"New value: +"Server group name (e.g., myapp-v001)"
  2. First observedv0.1.0

TDQS

A4/5.0
Behavior4/5

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

Annotations already declare the safety profile (readOnlyHint=true, idempotentHint=true, destructiveHint=false), so the bar is lower. The description usefully adds the return shape (JSON array of records with timestamps, descriptions, status), which is not derivable from the annotations or schema.

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?

Three sentences, zero waste, front-loaded with the core purpose before usage and return details. Every sentence earns its place.

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?

With no output schema, the description correctly compensates by summarizing the return payload, and annotations cover the read-only safety semantics. It is nearly complete, though it says nothing about pagination or result volume for what could be a large activity list.

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

Parameters3/5

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

Schema description coverage is 100%, so all five parameters are already documented in the schema and the description adds no syntax, format, or constraint detail beyond it. Baseline 3 applies.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description names a specific verb (Get) and resource (scaling activities) and scopes it to a server group within a Spinnaker cluster. No sibling tool overlaps with this resource, so the purpose is unambiguous, though it does not explicitly contrast itself with any alternative.

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?

"Use this to audit recent auto-scaling events (scale-up, scale-down) and diagnose capacity issues" gives clear intent-based guidance for when to reach for this tool. It lacks explicit exclusions or named alternatives, but the use case is well-defined.

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