np_randn

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

No annotations are provided, so the description must carry full behavioral disclosure. It mentions the distribution (standard normal) but omits essential details like distribution parameters (mean=0, std=1), reproducibility, or random state handling. This is minimal for a random sampling tool.

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 concise sentence. However, it could be slightly more informative by specifying distribution parameters without losing 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 the presence of an output schema (likely returning a numpy array), the description is mostly adequate. However, it lacks context about random state, seeding, or how multiple samples are drawn. For a simple function, this is acceptable but not complete.

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 coverage is 100% with the shape parameter fully described. The description adds no additional meaning beyond 'standard normal' context, so baseline 3 is appropriate.

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 returns samples from the 'standard normal' distribution, using a specific verb ('Return') and resource ('sample(s) from standard normal'). This distinguishes it from sibling tools like np_rand (uniform) and np_randint (integers).

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 implies usage when standard normal samples are needed but provides no explicit guidance on when to use this tool versus alternatives (e.g., np_rand for uniform, np_randint for integers). No when-to-use or when-not-to-use context is given.

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