random_sample

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

With no annotations, the description must disclose behavioral traits. It indicates a generation action but does not explain randomness, reproducibility, or side effects. The 'random_state' parameter implies seeding, but no details are given.

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 short sentence, which is too minimal for a tool with 5 parameters and an output schema. While it is concise, it lacks necessary structure and detail, making it under-specified.

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 lack of parameter descriptions in the schema, the description should compensate but does not. It fails to explain input behavior, distribution options, or the role of parameters like 'size' and 'dimensions'. The output schema exists, but the description still feels incomplete.

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%, and the description adds no explanation for any of the 5 parameters. It only mentions 'from NumPy distributions', which hints at the 'distribution' parameter but does not elaborate on valid values or the 'params' object.

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 generates random samples from NumPy distributions, indicating a specific verb and resource. It doesn't differentiate from sibling tools like 'distribution' or 'bootstrap', but the purpose is still unambiguous.

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, such as 'distribution' for distribution functions or 'bootstrap' for resampling. The description lacks any context on appropriate usage scenarios.

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