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@rarecloudio/mcp-server

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by RareCloudio

add_cluster_pool

Add a named worker node pool to a managed Kubernetes cluster. Provision autoscaling nodes with specified machine type and volume size, after user approval of costs.

Instructions

Add a named worker node pool to a managed Kubernetes cluster. Requires scope services:write. SPENDS MONEY: a new pool provisions billable worker nodes. Pass confirm:true only after the user has approved the pool and its cost. name is the pool name (lowercase letters/digits/hyphens, start with a letter, max 15, unique in the cluster); minimum/maximum are the autoscaling bounds; machineType (worker flavor, resolved server-side) and volumeSizeGb (per-node root volume, 10-1000 GiB, default 30) are optional. service_id comes from list_services; inspect existing pools with list_cluster_pools.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
nameYesNode-pool name (lowercase letters/digits/hyphens, start with a letter, max 15; unique in the cluster).
confirmYesSet to true to execute. This operation spends from your account balance and/or is irreversible; only pass true after the user has explicitly approved. Omit or false → the tool refuses and makes no API call.
maximumYesMaximum worker count (>= minimum, <= 16).
minimumYesMinimum worker count (autoscaling lower bound).
service_idYesManaged-Kubernetes service ID from list_services.
machineTypeNoWorker machine type / flavor (resolved server-side). Defaults to the cluster default when omitted.
volumeSizeGbNoPer-node root-volume size in GiB (default 30).
Behavior4/5

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

No annotations exist, so description carries full burden. It discloses the monetary impact, required scope, and the confirm contract. Could add error conditions or success response but sufficient for safe invocation.

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?

Description is front-loaded with purpose and warnings, then parameter details. Every sentence adds value, though slightly verbose; could be tightened without losing clarity.

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?

Covers scope, cost, confirmation flow, parameter details, and references sibling tools. Missing final outcome description (e.g., returns pool ID) but no output schema makes it acceptable.

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 covers 100% of params, baseline 3. Description adds naming constraints, autoscaling bounds, machine type resolution, volume size range and default, and source for service_id, enriching beyond schema.

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 tool adds a named worker node pool to a managed Kubernetes cluster, distinguishing it from siblings like list_cluster_pools, update_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: requires services:write scope, costs money, confirm flag only after user approval, and references list_services for service_id. Lacks explicit when-not-to-use but effectively guides usage.

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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