top_resources

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

Annotations already declare readOnlyHint=true, and the description adds useful behavioral context: it returns live metrics, joins with Kubernetes context, and explains that kind=workloads aggregates Pods from Deployments/StatefulSets/DaemonSets/Jobs. It does not contradict annotations, and while it omits rate limits or auth, the read-only nature is well-covered.

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 and front-loaded with the most critical usage guidance. Sentences are compact and information-dense; every clause earns its place, covering when to use, what it returns, and how the kind parameter behaves, without redundancy.

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 no output schema, the description adequately explains the return shape (ranked metrics with pod status, readiness, restarts, owner workload, requests, limits). It also covers all kind variants and parameter behavior, making it complete for an agent to select and invoke the tool successfully.

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 covers 100% of parameters with descriptions, establishing a strong baseline. The description adds further semantic value by explaining how 'kind=workloads' aggregates Pods to specific workload types, which is not fully captured in the parameter description. This goes beyond the schema's basic field definitions.

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 states a specific verb and resource: 'Returns live metrics ranked like kubectl top pods|nodes | sort' and explicitly differentiates from siblings by positioning it as a pre-log investigation tool. It clearly defines the three kind variants, making its purpose unambiguous.

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 opens with explicit use cases ('Use when investigating high CPU, memory pressure, OOMKills, slow services, noisy pods, or uneven node load') and provides an alternative guidance ('Use before reading logs when the symptom mentions CPU, memory, GC, OOM, latency, or load'). This tells the agent when to choose this tool over log-focused siblings.

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