k8s_scale_deployment

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

With no annotations provided, the description carries full burden but only states the basic action. It doesn't disclose that this is a destructive/mutative operation (changing replica count affects running pods), permission requirements, potential side effects (e.g., pod creation/deletion), rate limits, or what happens on failure. For a mutation tool with zero annotation coverage, this is a significant gap in behavioral context.

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 a single, efficient sentence with zero wasted words. It's front-loaded with the core action and resource, making it immediately understandable. Every word earns its place by contributing essential information about what the tool does.

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?

For a mutation tool with no annotations and no output schema, the description is incomplete. It doesn't cover behavioral aspects (destructive nature, permissions), error conditions, return values, or usage context. While concise, it lacks the depth needed for safe and effective use by an AI agent in a Kubernetes environment with many sibling tools.

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 description coverage is 100%, so all parameters are documented in the schema. The description adds no additional parameter semantics beyond implying the 'replicas' parameter sets the target count. It doesn't explain parameter relationships, constraints (e.g., replicas must be non-negative integer), or provide examples. Baseline 3 is appropriate when schema does the heavy lifting.

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 action ('Scale') and resource ('a deployment') with a specific outcome ('to a specific number of replicas'). It distinguishes itself from siblings like k8s_get_deployment (read) or k8s_update_deployment_image (different mutation), but doesn't explicitly differentiate from k8s_scale_statefulset which performs a similar scaling operation on a different resource type.

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?

No guidance is provided on when to use this tool versus alternatives. It doesn't mention prerequisites (e.g., needing deployment to exist), when not to use it (e.g., for scaling other resources), or direct alternatives like k8s_scale_statefulset for statefulsets. The agent must infer usage from the name and context alone.

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