advanced_statistical_analysis

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

No annotations are provided, so the description carries full burden. It discloses that non-numeric columns are ignored, the return format (markdown), and the correlation strength labels. However, it does not mention handling of missing data or any potential side effects, but for a read-only statistical tool, this is largely sufficient.

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 paragraph that efficiently conveys the purpose, behavior, and usage guidelines without redundant information. Every sentence adds value.

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 simplicity (two parameters, no nested objects), the description covers the return format, handling of non-numeric data, and explicit cross-references to sibling tools. It is fully adequate for an AI agent to understand and invoke correctly.

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 100%, but description adds value beyond the schema: it clarifies that 'data' is a table of records, and for 'analysisType' it explains the two modes and their outputs. This enhances understanding beyond the enum labels.

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 it computes per-column descriptive statistics or Pearson correlation for numeric column pairs over a table of records, specifying the return format and distinguishing between the two analysis types. It also differentiates from sibling tools.

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?

Explicitly provides when to use this tool versus alternatives: use analyze_dataset for single series, hypothesis_testing for p-values, and advanced_regression_analysis for predictive models. This is direct and helpful for agent selection.

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