get_workload_costs

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

No annotations are provided, so the description carries the full burden. It discloses the input parameters, default behavior (current kubeconfig context, limit 50), and the output structure (each workload includes cost, waste, CPU/memory usage, efficiency grade, pod labels). It lacks mention of read-only or side effects, but for a read tool this is sufficient. It could be strengthened by stating it queries the cluster and is safe to run.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is well-structured with sections for Args, explanation of return data, and examples. It is slightly verbose but every sentence adds value. The examples are particularly helpful. A minor reduction for not being more concise, but still effective.

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

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

Given the tool has 5 parameters with no schema descriptions, no annotations, and no output schema, the description is very complete. It covers all parameters with defaults and allowed values, explains the return data in detail, and provides concrete usage examples. It also mentions the kubeconfig context for environment. This leaves little ambiguity for an AI agent.

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

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

The input schema has zero description coverage for all 5 parameters. The description compensates fully by providing clear definitions, default values, allowed values (for sort_by: cost, waste, efficiency; for kind: Deployment, StatefulSet, DaemonSet, Job), and usage context (e.g., namespace as filter, context as kubeconfig). This adds significant meaning beyond the raw schema.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool provides a detailed Kubernetes workload cost breakdown with efficiency grades. It specifies filtering by namespace and kind, sorting options, and the data included (cost, waste, CPU/memory, efficiency grade). This distinguishes it from sibling tools like get_kubernetes_costs, which likely give aggregate cluster costs.

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

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

The description explains when to use the tool via examples like 'Show me all workload costs in the production namespace' and which workloads to filter by. It does not explicitly state when not to use it or list alternatives, but the context of sibling names (e.g., get_kubernetes_costs) implies this is for granular workload detail. A clear usage guidance is provided.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.