sampler_random_split

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

With no annotations, the description must fully disclose behavior. It mentions return structure but omits details like determinism (seed role), error handling for empty lists, or whether the split applies shuffling. The presence of a seed parameter is not explained.

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 extremely concise, using a single sentence to convey the core purpose, defaults, and output. Every word is meaningful, and the tool name is front-loaded in brackets.

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?

Despite having an output schema, the description lacks context about the algorithm (e.g., shuffling, deterministic vs. random), how to interpret the 'splits' and 'sizes', and edge cases. For a tool with 3 parameters, this is 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 coverage is 0%, so description must compensate. It explains the default ratio and return format but does not clarify the structure of 'ratios', the effect of 'seed', or the expected type of 'items'. This adds some value but is insufficient.

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 action ('Split list into partitions') and the resource (list of items). It specifies default ratios and return format, distinguishing it from sibling tools like sampler_shuffle or sampler_random_choice.

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 this tool versus alternatives. The mention of 'train/test' implies a common use case but does not provide context for other scenarios or exclusions.

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