np_corrcoef

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

No annotations are provided, so the description must carry the full burden. It fails to disclose important behavioral traits such as handling of missing values (NaN), output shape (correlation matrix), or performance considerations. This is insufficient for a statistical function.

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 gets straight to the point. However, it is slightly under-specified for a tool with two parameters and no annotations. It earns a 4 for its brevity but loses a point for not being more informative.

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 moderate complexity (2 parameters, 1 required) and the presence of an output schema (which should describe return values), the description is minimally acceptable. However, it lacks usage guidelines and behavioral transparency, making it incomplete for an agent to correctly select and invoke 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?

Schema description coverage is 100% (both 'array' and 'rowvar' have descriptions). The tool description adds no extra meaning beyond what the schema already provides, so the baseline score of 3 is appropriate.

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 Pearson product-moment correlation coefficients', which clearly indicates the function's purpose (computing correlation coefficients). It uses a specific verb and resource, distinguishing it from sibling tools like np_correlate which computes cross-correlation.

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 is provided on when to use this tool versus alternatives (e.g., np_correlate for cross-correlation, or np_cov for covariance). The description lacks context about appropriate use cases, prerequisites (e.g., numeric data), or when not to use it.

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