statistics_analysis

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

With no annotations, the description carries the full burden of behavioral disclosure. It adds meaningful details: standard deviation and variance use the sample estimator (n-1), and the percentile operation returns a specific set of percentiles. This goes beyond the schema's enum values and is highly useful.

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 two sentences with zero wasted words. It front-loads the main purpose, then provides two specific, high-value behavioral details. Every sentence earns its place.

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 has an output schema and moderate complexity (10 operations, 2 params), the description is near-complete. It covers the main purpose and key behavioral nuances but lacks explicit sibling differentiation and leaves some operations (e.g., 'summary') unspecified. Overall, it is adequately complete.

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 does so for two key parameters by clarifying the estimator for std_dev/variance and specifying the percentile set. While not every operation is explained (e.g., skewness, summary), the enum names are mostly self-explanatory and the added details cover the most ambiguous cases.

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 performs statistical analysis on data sets, which is a specific verb+resource. However, it doesn't explicitly distinguish this from the sibling tools 'stats_summary' and 'percentile', which likely overlap.

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?

Usage is implied by the list of operations (mean, median, std_dev, etc.) and the note about sample estimators. There is no explicit guidance on when to use this tool versus alternatives like stats_summary or percentile, but the intended use case is reasonably clear.

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