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sandeepbazar

io.github.sandeepbazar/ocm-mcp-server

by sandeepbazar

propose_cluster_action

Propose a cluster lifecycle action (cordon, add-on, label updates) without applying it. Server-side dry-run validates the request, then stores it pending for human approval.

Instructions

Propose an OCM cluster lifecycle action. Does NOT apply anything.

Args: cluster: target managed cluster name. action: one of 'cordon' (taint out of scheduling), 'uncordon' (undo cordon), 'set_label' (params: {"key","value"}; empty value removes the label), 'accept' (set hubAcceptsClient=true and approve pending join CSRs), 'enable_addon' (params: {"addon","install_namespace"?}; create a ManagedClusterAddOn), 'disable_addon' (params: {"addon"}; delete it). summary: one or two sentences the human approver will read. params_json: JSON object of action parameters (set_label and the addon actions need it; cordon/uncordon/accept do not).

The action is validated with a server-side dry-run, then stored pending. The human operator must run ocm-mcp approve <id> to mint the approval token.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
actionYes
clusterYes
summaryYes
params_jsonNo{}

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior5/5

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

Discloses server-side dry-run validation, pending storage, and the mandatory human approval step via `ocm-mcp approve`. It also details per-action side effects such as cordon tainting, CSRs approval, and addon deletion, which go well beyond the provided annotations.

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?

The description starts with a concise purpose statement, uses a compact bulleted Args list, and every sentence contributes essential information. No filler or repetition is present.

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?

Covers action selection, parameter requirements, validation behavior, pending storage, and the approval handoff. With an output schema present, omitting return-value details is acceptable, making the description complete for this tool.

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?

Despite 0% schema description coverage, the Args section fully documents all four parameters, enumerates valid action values, and specifies which actions require params_json fields. This completely compensates for the schema's lack of descriptions.

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 first sentence clearly states a specific verb ('Propose') and resource ('OCM cluster lifecycle action'), and immediately clarifies that it does NOT apply anything, distinguishing it from the sibling apply_cluster_action and other cluster-action tools.

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?

The description provides detailed per-action semantics and explains the propose/approve flow, including the need for a human operator to run `ocm-mcp approve`. It does not explicitly name alternative tools, but the 'Does NOT apply anything' and 'stored pending' behavior make the usage context clear.

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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