power_analysis

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 what the tool calculates but doesn't describe behavioral traits such as error handling, computational limits, assumptions (e.g., normality for t-test), or output format. For a statistical tool with no annotation coverage, this leaves significant gaps in understanding how it behaves.

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 highly concise and front-loaded, with a single sentence that directly states the tool's function and domain. Every word earns its place, and there's no unnecessary information or redundancy, making it efficient for quick understanding.

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 power calculation tool with 4 parameters (2 required), 0% schema description coverage, no annotations, and no output schema, the description is incomplete. It doesn't explain parameter details, behavioral assumptions, or what the output looks like, leaving the agent with insufficient context to use the tool effectively.

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%, meaning parameters are undocumented in the schema. The description mentions 'sample size and effect size,' which maps to two of the four parameters (n, effect_size), but doesn't cover the optional alpha and alternative parameters. It adds some meaning for the required parameters but doesn't fully compensate for the coverage gap, resulting in a baseline score.

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 statistical power (probability of detecting effect) for t-test given sample size and effect size.' It specifies the statistical method (t-test), the calculation (power), and the required inputs (sample size, effect size). However, it doesn't explicitly differentiate from sibling tools, many of which are unrelated statistical functions, so it doesn't reach the highest 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 provides minimal guidance on when to use this tool. It mentions the domain (statistics) and category (inference), which implies usage in statistical hypothesis testing contexts, but it doesn't specify when to choose this over alternatives (e.g., other power analysis tools or statistical tests in the sibling list). No explicit when-not-to-use or prerequisite information is given.

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