np_mean

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

No annotations are provided, placing the burden on the description. The description covers basic behavior (computes mean along axis) but omits details like handling of NaN, output type, or data type promotion. Adequate but not thorough.

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?

Single sentence, no redundant words. It efficiently communicates the core purpose without waste, achieving perfect conciseness.

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 that an output schema exists (mentioned in context), the description is sufficient to understand input and behavior. It covers the essential axis semantics and normal operation, though it could mention edge cases.

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 covers both parameters with descriptions (100% coverage). The description adds minimal extra meaning by clarifying that axis=None means 'mean of all'. Baseline is 3, and description provides slight improvement.

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 verb 'Compute' and the resource 'arithmetic mean', explicitly mentions the axis parameter, and distinguishes the tool from related siblings like np_sum by specifying 'mean'.

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 explicit guidance on when to use or avoid this tool compared to alternatives. The context is implicit but lacks direct statements about when-not-to-use or sibling differentiation.

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