uniform_sample

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

No annotations are provided, so the description carries the full burden of behavioral disclosure. It mentions generating random samples but does not specify output format (e.g., array of numbers), randomness characteristics (e.g., pseudorandom, reproducibility with seed), or any side effects. For a tool with parameters like 'seed' for reproducibility, this lack of detail is a significant gap in transparency.

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 concise and front-loaded, consisting of a single sentence that directly states the tool's purpose. There is no wasted verbiage, and it efficiently communicates the core function. However, it could be improved by including key usage details without sacrificing brevity.

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's complexity (4 parameters, no annotations, no output schema), the description is incomplete. It does not explain the parameters, output format, or behavioral traits like randomness control. For a statistical sampling tool, this lack of detail makes it inadequate for users to understand how to invoke it correctly or interpret results.

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?

The schema description coverage is 0%, meaning none of the parameters (n, a, b, seed) are documented in the schema. The description does not add any meaning beyond the schema; it does not explain what 'n' (sample size), 'a' and 'b' (distribution bounds), or 'seed' (random seed) represent. This leaves all parameters undocumented, failing to compensate for the low schema coverage.

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 states the tool 'Generate random samples from the uniform distribution', which provides a clear verb ('Generate') and resource ('random samples'), but it is vague about the specific mechanism or context. The domain and category tags ('Domain: probability, Category: general') add some context but do not differentiate it from sibling tools like 'random_array', 'random_float', or 'random_int', which also generate random values. It lacks specificity to distinguish it clearly from these alternatives.

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 provides no guidance on when to use this tool versus alternatives. It does not mention any prerequisites, constraints, or comparative contexts with sibling tools such as 'random_array', 'random_float', or 'random_int'. Without such information, users must infer usage based on the tool name and parameters alone, which is insufficient for effective tool selection.

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