Generate a table of statistical power across sample sizes to show trade-offs between power and N instead of a single value.
Tabulate power against sample size -- useful for showing a user the trade-off instead of a single number. Capped at 500 rows.
| Name | Required | Description | Default |
|---|---|---|---|
| n_to | Yes | ||
| test | Yes | ||
| alpha | Yes | ||
| tails | No | ||
| n_from | Yes | ||
| n_step | No | ||
| predictors | No | ||
| effect_size | Yes | ||
| allocation_ratio | No |
Changes observed during successful MCP inspections.
v1.1.0Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden, and it does disclose one important trait: results are 'capped at 500 rows', telling the agent output can be truncated. It says nothing about error behavior when the requested n range exceeds the cap, valid values for test, or whether the tabulation errors or silently truncates — gaps that matter for a computation tool.
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?
Two short sentences with no filler, and the core purpose plus the key comparison are front-loaded. The structure is slightly fragmented (mid-sentence line break) but efficient.
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?
A 9-parameter statistical computation with no annotations, no output schema, and zero parameter documentation needs far more from the description than purpose and a row cap. An agent cannot tell what 'test' strings are valid, what effect_size means per test, or how n_step interacts with the 500-row limit.
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 coverage is 0% across 9 parameters, including non-obvious ones like allocation_ratio, predictors, tails, and n_step. The description never mentions any of them; the 500-row cap only indirectly hints that the n_from/n_to/n_step range controls output volume. It does not compensate for the coverage gap.
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?
States a specific verb and resource ('Tabulate power against sample size') and clarifies the output shape ('instead of a single number'), which implicitly separates it from the sibling power_analysis. It could name power_analysis explicitly, but the function is unambiguous.
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?
'Useful for showing a user the trade-off instead of a single number' gives a clear selection criterion relative to a single-value power calculation. There is no explicit naming of alternatives or when-not-to-use conditions, keeping it short of a 5.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.