correlation

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 the main action (return Pearson correlation) but does not disclose whether the operation is read-only or destructive, error handling for missing or non-numeric columns, or any side effects. For a tool with no annotations, this is insufficient 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 consists of one clear sentence followed by a minimal, structured parameter list. Every part is essential: the verb, the specific correlation type, and the parameter explanations. No unnecessary words or repetition. The most important information is front-loaded.

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 (a statistical function) and the presence of an output schema (not shown but indicated), the description covers the core functionality and parameter semantics adequately. It does not describe error conditions or assumptions (e.g., columns must be numeric), but for a correlation tool, the provided information is sufficient for correct invocation in most cases.

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?

The input schema properties have no descriptions (0% coverage). The description adds meaning by explaining each parameter: 'name: the dataset name', 'col_a: first numeric column', 'col_b: second numeric column'. This clarifies the purpose of each parameter beyond the schema titles, though it could be more precise (e.g., dataset name format).

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 explicitly states 'Return the Pearson correlation between two numeric columns' which is a specific verb (Return) and resource (Pearson correlation). This clearly distinguishes it from sibling tools like load_csv, list_datasets, summary, filter_rows, and top_rows, which perform different operations.

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 does not provide explicit guidance on when to use this tool versus alternatives. While the mention of 'Pearson correlation' implies it is for measuring linear relationships between two numeric columns, no when-not-to-use instructions or alternative tool mentions are given. The context of sibling tools partially clarifies its distinct role.

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