Correlation

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

Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, covering the safety profile. The description adds no behavioral context beyond the two methods, which is already visible in the schema's method enum. It does not contradict annotations.

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, short sentence that front-loads the core action ('Compute Pearson or Spearman correlation') and includes the target resource. There is no redundant detail or filler, making it appropriately concise.

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 has an output schema, so return values are not required in the description. Given the simple nature of the operation, the description sufficiently covers purpose and method selection, while annotations and schema handle safety and parameter details. The description is complete for invoking the tool.

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 provides full descriptions for all three parameters (x, y, method), including examples, defaults, and constraints, achieving 100% schema coverage. The description's mention of 'two paired variables' adds no meaning beyond what the schema already states.

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 uses a specific verb ('Compute') and resource ('correlation'), and explicitly names the two methods (Pearson or Spearman). This clearly distinguishes it from sibling tools like stats_t_test or stats_chi_square, which serve different statistical purposes.

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 usage for measuring correlation between two paired variables and mentions both available methods, but it does not explicitly say when to use this tool over alternatives like t-tests or chi-square tests. No when-not guidance or alternative tool references are provided.

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