confidence_interval_mean

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 calculates a confidence interval but does not explain how it behaves—e.g., what statistical method is used (e.g., t-distribution for small samples), assumptions (e.g., normal distribution), output format, or error handling. This lack of detail is significant for a statistical tool with no annotation coverage.

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 with the core purpose in the first clause. The domain and category tags are efficiently appended. There is no wasted text, though it could be slightly more informative without losing 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 (statistical inference with 2 parameters), lack of annotations, no output schema, and 0% schema description coverage, the description is incomplete. It does not cover behavioral traits, parameter details, or output expectations, making it inadequate for effective use without additional context or trial-and-error.

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 for undocumented parameters. It mentions 'sample data' and 'confidence level' implicitly but does not explain parameter semantics—e.g., that 'data' is a numeric array of sample values or typical ranges for 'confidence_level'. Without this, users might not understand how to provide inputs correctly.

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 tool's purpose: 'Calculate confidence interval for population mean from sample data.' It specifies the verb ('calculate'), resource ('confidence interval'), and domain context ('population mean from sample data'). However, it does not explicitly differentiate from its sibling 'confidence_interval_proportion' or other statistical inference tools, which prevents a perfect score.

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 includes domain and category tags ('Domain: statistics, Category: inference'), which imply usage context but do not provide explicit guidance on when to use this tool versus alternatives like 'confidence_interval_proportion' or other statistical methods. No when-not-to-use instructions or prerequisites are mentioned, leaving gaps in practical application advice.

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