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. While it mentions the output range (-1 to 1), it doesn't describe error handling (e.g., what happens with invalid inputs like arrays of different lengths), computational characteristics, or any side effects. For a statistical calculation tool with zero annotation coverage, this is a significant gap.

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 concise and front-loaded with the core functionality. The single sentence efficiently communicates the calculation, output range, and domain/category. No unnecessary words or redundant information are present.

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 lack of annotations, 0% schema description coverage, and no output schema, the description is insufficient. It doesn't explain parameter semantics, error conditions, or return format. For a tool performing a mathematical calculation with two required parameters, more contextual information is needed for effective use.

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 schema description coverage is 0%, meaning neither parameter (xs, ys) has descriptions in the schema. The description mentions 'two datasets' but doesn't explain what xs and ys represent, their expected format (numeric strings?), or any constraints (e.g., equal length required). This leaves parameters largely undocumented.

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 the tool's purpose: 'Calculate the Pearson correlation coefficient between two datasets (ranges from -1 to 1)'. It specifies the statistical method (Pearson correlation), the input (two datasets), and the output range. However, it doesn't explicitly differentiate from sibling tools like 'covariance' or 'autocorrelation', which are related statistical measures.

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 provides minimal usage guidance. It mentions the domain (statistics) and category (general), but gives no explicit instructions on when to use this tool versus alternatives like 'covariance' (a sibling tool) or other correlation methods. There's no mention of prerequisites, data requirements, or typical use cases.

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