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sandeepbazar

io.github.sandeepbazar/ocm-mcp-server

by sandeepbazar

get_placement_decision

Read-only

Retrieve the list of clusters selected by a Placement by reading its PlacementDecisions. Provide the Placement name and namespace to get the decision.

Instructions

Which clusters a Placement actually selected (reads its PlacementDecisions).

Args: placement: Placement name. namespace: the namespace the Placement lives in.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
namespaceYes
placementYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior4/5

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

Annotations already declare readOnlyHint=true and destructiveHint=false, and the description's word 'reads' aligns with these. The description adds context by specifying that it reads the PlacementDecisions subresource, which clarifies the exact data source. This is a useful behavioral disclosure beyond the annotations, with no contradictions.

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 is extremely concise: one purpose sentence plus a compact parameter list. Every word earns its place, and the core purpose is front-loaded. There is no redundancy or extraneous information.

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?

For a tool with only two required string parameters, no nested objects, an output schema, and read-only annotations, the description provides adequate context. It covers the tool's purpose and both parameters, while the output schema handles return values. It could mention error behavior (e.g., placement not found), but this is a minor gap for such a simple read operation.

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?

The input schema provides no descriptions (0% coverage), so the description's Args section is critical. It explains both parameters: 'placement: Placement name' and 'namespace: the namespace the Placement lives in.' These are clear and sufficient, fully compensating for the schema's lack of detail.

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 opens with 'Which clusters a Placement actually selected (reads its PlacementDecisions)', which clearly states the tool's function using a specific verb ('reads') and a specific resource ('PlacementDecisions'). This distinguishes it from sibling tools like list_placements, which list placements rather than their selected clusters.

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 strongly implies when to use this tool: when you need to know the clusters a specific Placement selected. It does not explicitly name alternatives or state when not to use, but the context is clear from the purpose statement alone, especially given the sibling tool list.

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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