Lumino

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It mentions 'progressive analysis and token budget management' and that it 'prioritizes failed/error pods, correlates events,' which gives some behavioral context. However, it doesn't address important aspects like whether this is a read-only operation, potential performance impact, authentication requirements, or rate limits. The description adds value but leaves significant behavioral questions unanswered.

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 efficiently structured with a clear opening statement, usage context, parameter explanations, and return value description. Every sentence serves a purpose, and information is well-organized with labeled sections ('Args:', 'Returns:'). No wasted words or 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's complexity (adaptive investigation with 5 parameters), no annotations, and an output schema exists (so return values don't need explanation), the description does well. It covers purpose, usage context, all parameters with semantics, and mentions the return structure. However, for a tool with no annotations and significant behavioral implications, it could better address safety, permissions, or operational constraints.

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?

With 0% schema description coverage, the description compensates well by explaining all 5 parameters in the 'Args' section with meaningful descriptions and default values. It adds substantial semantic context beyond the bare schema, though it doesn't provide examples or format details for parameters like 'focus_areas' (which accepts an array or null).

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 performs 'adaptive namespace investigation with progressive analysis and token budget management' and specifies it's 'best for medium namespaces (5-30 pods)' with prioritization of 'failed/error pods' and event correlation. This provides a specific verb+resource+scope, though it doesn't explicitly distinguish from all sibling tools like 'conservative_namespace_overview' or 'smart_get_namespace_events'.

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 clear context about when to use this tool ('best for medium namespaces (5-30 pods)') and what it prioritizes ('failed/error pods, correlates events'). However, it doesn't explicitly state when NOT to use it or name specific alternative tools from the sibling list for different scenarios, which prevents a perfect score.

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