normal_probability

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

With no annotations, the description carries the full transparency burden. It mentions a return dictionary, but does not disclose constraints like std_dev > 0 or error behavior, and only lists prob_type options without elaboration.

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 efficiently structured with a one-sentence summary followed by an Args/Returns list. It contains no redundant or overly verbose content.

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?

For this simple calculation tool, the description provides the basic function and return format. It is minimally complete but lacks usage context and explanations of cumulative vs survival probability semantics, which could leave an agent uncertain.

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 description compensates for 0% schema coverage by explaining each parameter briefly, including the allowed prob_type values. However, it lacks constraints such as requiring positive std_dev and does not clarify how defaults (mean=0, std_dev=1) relate to standard normal distributions.

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 'Calculate normal distribution probabilities' clearly states the tool's function with a specific verb and resource. It distinguishes from sibling tools by naming the normal distribution, though it doesn't explicitly address 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?

No guidance is provided about when to use this tool versus the Poisson or binomial probability calculators. There is no mention of assumptions, data type, or use cases.

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