stats_stats_correlation

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

Despite no annotations, the description discloses return values (r, r_squared, interpretation). However, it does not mention input requirements (numeric, same length), handling of missing values, or any assumptions like normality, which would be needed for full transparency.

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 sentence that efficiently conveys core functionality and output. It is front-loaded with the key information. However, it could be slightly expanded to include constraints without losing conciseness.

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?

The tool is simple and the description covers purpose and outputs. However, missing parameter constraints and usage context reduce completeness. An output schema exists but is not referenced.

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%, and the description adds no meaning beyond the parameter names. It does not specify that x and y must be numeric arrays of equal length, nor any type constraints, despite the schema allowing any type.

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?

Description explicitly states 'Pearson correlation between x and y' with output including r, r_squared, and interpretation. This clearly distinguishes it from sibling tools like stats_stats_describe and stats_stats_frequency.

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?

No guidance on when to use this tool versus alternatives, nor any prerequisites or assumptions (e.g., data must be continuous, linear relationship). The description does not mention when not to use Pearson correlation.

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