search_resources_by_labels

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

With no annotations provided, the description carries full burden for behavioral disclosure. It mentions the tool searches and returns results with 'analysis, and recommendations', but lacks critical details: whether it's read-only or mutating, permission requirements, rate limits, pagination behavior, error handling, or what 'analysis' entails. For a complex search tool with 9 parameters, this is insufficient.

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 a clear purpose statement followed by 'Args:' and 'Returns:' sections. Each sentence earns its place by explaining parameters or outputs. It could be slightly more concise by combining some parameter explanations, but overall it's efficient and front-loaded.

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's complexity (9 parameters, no annotations, but has output schema), the description is partially complete. It excels in parameter semantics but lacks behavioral context (e.g., safety, performance). The output schema existence means the description doesn't need to detail return values, but it should cover more operational aspects for a search tool in a Kubernetes context.

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 0%, so the description must fully compensate. It provides detailed semantics for all 9 parameters: examples for 'resource_types' and 'label_selectors', defaults for optional parameters, and explanations of each parameter's role (e.g., 'include_metadata_only: Return only metadata'). This adds significant value beyond the bare 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's purpose with a specific verb ('Search') and resource ('Kubernetes resources by labels'), specifying it works 'across multiple resource types and namespaces'. This distinguishes it from siblings like 'list_pods_in_namespace' or 'get_kubernetes_resource' that focus on single resource types or specific resources.

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 provides no guidance on when to use this tool versus alternatives. It doesn't mention prerequisites, compare to sibling tools (e.g., 'list_namespaces' for namespace listing or 'semantic_log_search' for log-based searches), or specify scenarios where this search is preferred over other investigation methods.

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