sampler_random_float

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

The description discloses that it generates random floats in [low, high) and returns a result object with a 'values' key. However, it does not specify the distribution (uniform), precision control via decimals, or the effect of seed. Since no annotations are provided, the description carries the full burden, but it offers only basic behavioral info.

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 very concise—one sentence with a category tag. It is front-loaded with '[sampler]' for context. While efficient, it sacrifices detail that could be included without becoming verbose.

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 has 5 parameters, no required, and an output schema exists, the description is insufficient. It does not explain the output format beyond '{values}', nor does it cover the 'decimals' and 'seed' parameters. The lack of schema coverage makes the description inadequate for a tool with this parameter count.

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 must compensate but only mentions low, high, and n. The 'decimals' and 'seed' parameters are entirely undocumented. The description adds minimal meaning beyond the parameter names for low, high, and n.

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 it generates n random floats in [low, high) and returns {values}. It distinguishes from sibling tools like sampler_random_int and sampler_random_choice by specifying floats and interval range. However, it could be more explicit about the output format beyond '{values}'.

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 sampler_random_int for integers or sampler_random_choice for discrete selection. No mention of seeding for reproducibility or exclusions.

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