get_label_costs

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

No annotations provided, so burden is on description. It discloses that untagged workloads are grouped under '__untagged__' and a warning is returned if tagging coverage is low. However, it does not mention if it is read-only, pagination, or other behavioral traits.

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?

Description is well-structured with bullet points for values and examples, front-loaded with the core purpose, and every sentence adds value. No unnecessary repetition.

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?

Covers main use case, untagged behavior, and warning about coverage. No output schema, but response format is hinted. For a simple aggregation tool, it is sufficiently complete, though could mention how costs are presented (e.g., per label value).

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?

Schema coverage is 0%, and description adds meaning for label_key by listing common values and providing examples. However, the context parameter is not described at all, leaving half of the parameters undocumented.

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 aggregates Kubernetes costs by any pod label across all namespaces, with explicit use cases (chargeback) and common label_key examples. It distinguishes from siblings like get_kubernetes_costs by focusing on label-based aggregation.

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?

Provides explicit guidance that the tool is great for chargeback and lists common label_key values and examples. However, it doesn't explicitly state when not to use it or contrast with alternative tools like get_kubernetes_namespace_breakdown.

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