binomial_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 states the tool generates random samples but does not describe the output format (e.g., list of integers), randomness characteristics (e.g., pseudorandom vs true random), performance implications, or error handling. For a statistical sampling tool with zero annotation coverage, this leaves significant gaps in understanding its behavior.

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, efficient sentence that directly states the tool's purpose. The domain and category tags are appended concisely. There is no unnecessary verbiage or structural fluff, making it easy to parse quickly.

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 complexity of a statistical sampling tool with 4 parameters, no annotations, no output schema, and 0% schema description coverage, the description is insufficient. It does not explain the output (e.g., sample format), parameter semantics, or behavioral traits like randomness seeding or error conditions. The description alone does not provide enough context for reliable tool invocation.

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 input schema has 4 parameters with 0% description coverage, meaning none of the parameters are documented in the schema. The description does not mention any parameters, their meanings (e.g., 'n' as number of trials, 'p' as success probability), valid ranges, or default behaviors. This fails to compensate for the lack of schema documentation, leaving all parameters semantically undefined.

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 binomial distribution', which clearly indicates the verb ('Generate random samples') and resource ('binomial distribution'). However, it does not differentiate from sibling tools like 'binomial_cdf' or 'binomial_pmf', which also operate on the binomial distribution but for different purposes (cumulative distribution function vs probability mass function). The domain/category tags add context but don't enhance the core purpose statement.

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 sibling tools like 'binomial_cdf' or 'binomial_pmf', nor does it explain when random sampling is appropriate compared to calculating probabilities or distributions. There is no indication of prerequisites, typical use cases, or limitations.

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