bootstrap_confidence_interval

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

No annotations are provided, so the description carries the full burden. It mentions the method ('resampling') and statistics ('mean or median'), but lacks critical behavioral details: computational intensity (e.g., 'n_bootstrap' default 1000 may be heavy), assumptions (e.g., data independence), output format, or error handling. For a statistical tool with no annotation coverage, this is a significant gap.

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 front-loads the core purpose. It avoids redundancy and wastes no words, though it could be more structured (e.g., separating purpose from context). The domain/category note is concise but could be integrated better.

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 no annotations, 0% schema coverage, and no output schema, the description is incomplete. It omits parameter explanations, behavioral traits (e.g., performance, assumptions), and output details. For a 4-parameter statistical tool with resampling complexity, this leaves the agent under-informed.

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. It only vaguely references 'statistic (mean or median)' and 'resampling', but does not explain the four parameters (data, statistic_func, n_bootstrap, confidence_level) or their semantics (e.g., what 'statistic_func' accepts beyond defaults). The description adds minimal value beyond the bare schema.

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 bootstrap confidence interval for a statistic (mean or median) using resampling.' It specifies the verb ('calculate'), resource ('bootstrap confidence interval'), and method ('resampling'), and distinguishes it from siblings like 'confidence_interval_mean' by mentioning bootstrap resampling. However, it doesn't explicitly differentiate from other statistical inference tools beyond naming the domain/category.

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 mentions the domain ('statistics') and category ('inference'), but does not specify scenarios, prerequisites, or compare it to sibling tools like 'confidence_interval_mean' or 'permutation_test'. Without explicit usage context, the agent must infer applicability.

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