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scale_tkc_cluster

Scale the worker node count of a TanzuKubernetesCluster. Specify cluster name, namespace, and desired worker count; node provisioning or removal happens asynchronously.

Instructions

[WRITE] Scale the worker node count of an existing TanzuKubernetesCluster (TKC).

Asynchronous: patches the cluster spec and returns immediately with status "scaling" — node provisioning or removal continues in the background; poll get_tkc_cluster to watch progress. Scales workers only (control plane is unchanged); use upgrade_tkc_cluster to change the K8s version instead. Not destructive, but reducing worker_count drains the removed nodes. Audited to ~/.vmware/audit.db (SQLite) and ~/.vmware-vks/audit.log (JSON Lines).

Args: name: TKC cluster name (discover via list_tkc_clusters). namespace: vSphere Namespace containing the cluster. worker_count: Desired total worker node count, integer >= 1 (values below 1 are rejected with an error). pool_name: Node pool (machineDeployment) to scale. Omit to scale the first existing pool. Other pools are always preserved. target: Name of a vCenter entry in ~/.vmware-vks/config.yaml. Omit to use the default target defined in that file.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
nameYes
targetNo
namespaceYes
pool_nameNo
worker_countYes
Behavior5/5

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

The description discloses asynchronous behavior (patches and returns immediately with 'scaling' status), background node provisioning/removal, and auditing details. This adds context beyond annotations, which only indicate non-destructive write.

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?

The description is well-structured with clear sections but slightly verbose. Every sentence adds value, though some redundancy could be trimmed.

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

Completeness5/5

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

The tool has 5 parameters, no output schema, and annotations. The description covers async behavior, parameter details, auditing, and sibling differentiation, making it fully actionable.

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

Parameters5/5

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

With 0% schema description coverage, the description fully compensates by explaining each parameter: name (discover via list_tkc_clusters), namespace, worker_count (>=1, error below), pool_name (omit for first pool, others preserved), target (config entry).

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 'scale' and resource 'worker node count of an existing TKC'. It distinguishes from siblings like upgrade_tkc_cluster (K8s version) and create_tkc_cluster, and specifies it scales workers only.

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

Usage Guidelines5/5

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

It explicitly states when to use (scale workers) and when not to (use upgrade_tkc_cluster for version changes). It also recommends polling get_tkc_cluster to watch progress, providing clear alternative tools.

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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