correlation

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

No annotations are provided, so the description must fully disclose behavior. It fails to mention critical prerequisites such as the requirement that the two arrays must be of equal length for a valid correlation, and it does not describe the return format or edge cases (e.g., constant arrays).

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 concise sentence that is front-loaded with the main action. It does not contain any waste, though it could include additional useful information 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?

For a simple tool with 2 parameters and no output schema, the description lacks necessary completeness. It does not mention the expected output (a single value between -1 and 1), the condition that arrays must be same length, or any assumptions like linearity. The information provided is adequate for a basic understanding but insufficient for correct invocation in all 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?

Schema description coverage is 0%, so the description should compensate by explaining the parameters. It describes 'paired numeric observations' but does not explicitly state that firstValues and secondValues are the two sequences or that they must be paired and of the same length. This adds minimal value beyond the schema structure.

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 specifies the verb 'calculates' and the exact statistical measure 'Pearson correlation coefficient', distinguishing it from sibling tools like variance, standard_deviation, and linear_regression.

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 implies when to use (when needing correlation between paired observations) but provides no explicit guidance on when not to use or how it compares to alternative correlation measures or siblings like linear_regression.

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