binomial_pmf

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It only states what the tool calculates without mentioning input constraints (e.g., valid ranges for k, n, p), error handling, or output format. This is inadequate for a tool with three parameters and no output schema.

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. It is front-loaded and wastes no words, though it could be more informative 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 complexity of a statistical tool with three parameters, no annotations, and no output schema, the description is insufficient. It does not cover parameter semantics, behavioral traits, or usage guidelines, making it incomplete for effective agent operation.

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%, and the description does not explain the parameters k, n, and p beyond what the schema provides (their types). It fails to add meaning such as k being the number of successes, n the number of trials, and p the probability of success, which is crucial for correct usage.

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 calculates the binomial PMF, which is a specific probability function, but it does not differentiate from sibling tools like binomial_cdf or binomial_sample. It mentions the domain and category, which adds some context but doesn't clearly distinguish its unique purpose among related tools.

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 like binomial_cdf or binomial_sample. The description lacks any context about appropriate scenarios, prerequisites, or exclusions, leaving the agent without usage direction.

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