sampler_random_int

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

With no annotations, the description carries the full burden of behavioral disclosure. It states the generation of random integers and return format, but does not explain the distribution shape (uniform), the effect of the seed parameter, or behavior when n is null. The description provides basic transparency but lacks depth.

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—a single sentence plus a return note. It is front-loaded with the category marker '[sampler]' and directly states the core function. No unnecessary words; every element earns its place.

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 low complexity of the tool and the presence of an output schema (which handles return structure), the description covers the main operation. However, it omits the seed parameter's purpose and the behavior when n is null, leaving gaps in completeness for a simple tool.

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%, so the description must compensate. It explains low/high as inclusive range and n as count, but does not clarify that n can be null (defaulting to a single value?) nor does it explain the seed parameter at all. This leaves significant ambiguity for the agent.

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 'Generate', specifies the resource 'random integers', and defines the range as inclusive with [low, high]. It effectively distinguishes from sibling tools like sampler_random_choice and sampler_random_float by highlighting the specific task of integer generation within a range.

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 lacks any guidance on when to use this tool versus alternatives. There is no mention of context, exclusions, or references to sibling tools like sampler_random_choice or sampler_random_float, which would help an agent decide which sampler to invoke.

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