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by RareCloudio

update_cluster_pool

Edit autoscaling bounds, machine type, or root volume size of a Kubernetes worker node pool. Updates only specified fields.

Instructions

Edit an existing worker node pool of a managed Kubernetes cluster — any of its autoscaling bounds (minimum/maximum), machineType, or per-node volumeSizeGb (10-1000 GiB). Requires scope services:write. Send only the fields you want to change; omitted fields are left as-is. Plain write — not gated. service_id from list_services; pool is the pool name from list_cluster_pools.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
poolYesNode-pool name from list_cluster_pools.
maximumNoNew maximum worker count (>= minimum, <= 16).
minimumNoNew minimum worker count.
service_idYesManaged-Kubernetes service ID from list_services.
machineTypeNoNew worker machine type / flavor (resolved server-side).
volumeSizeGbNoNew per-node root-volume size in GiB.
Behavior3/5

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

No annotations are provided, so the description carries full burden. It discloses required scope (services:write), partial update behavior, and constraints (volumeSizeGb range). However, it does not mention potential side effects like rolling updates or downtime, nor the impact on running nodes. Adequate but not comprehensive.

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 that are front-loaded with the purpose, then authorization, then prerequisites. Every sentence adds value with no redundancy.

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?

The description covers input, prerequisites, and partial update behavior. However, since there is no output schema, the description should ideally mention what is returned (e.g., updated pool or success status). This omission leaves a gap for a mutation tool.

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?

The input schema has 100% description coverage, so the baseline is 3. The description adds little new semantic meaning beyond reinforcing the source of service_id and pool. The note about omitted fields being left as-is is more about usage than parameter semantics.

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 verb 'Edit' and the resource 'existing worker node pool of a managed Kubernetes cluster', listing specific modifiable attributes (autoscaling bounds, machineType, volumeSizeGb). It is distinct from sibling tools like add_cluster_pool, delete_cluster_pool, rename_cluster_pool, etc.

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?

Provides clear context for when to use (editing a pool) and includes prerequisites: 'service_id from list_services; pool is the pool name from list_cluster_pools.' Also advises partial update behavior. Does not explicitly state when not to use or mention alternatives, but the context is sufficient.

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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