run_correlation

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

With no annotations, the description carries the full burden of behavioral disclosure. It mentions computing correlation and available methods, but omits critical details like file read operations, handling of missing data, numeric type requirements, or any side effects. The output schema exists, so return value structure is covered, but behavioral traits are insufficiently described.

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 two concise sentences. Each sentence serves a purpose: the first defines the tool's function, and the second provides usage guidance. No unnecessary words or redundancy.

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 (4 parameters, file reading), the description is minimally adequate. It covers the core function and use case, but lacks details on parameter semantics, file requirements, and potential errors. The presence of an output schema mitigates the need for return value descriptions, but overall completeness is average.

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%, and the description adds no meaning to the parameters. It does not explain that 'data_file_path' refers to the file containing the columns, nor does it describe the 'method' parameter options or default. The schema itself provides basic info, but the description fails to compensate for the lack of schema descriptions.

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 computes statistical correlation (pearson, spearman) between two numeric columns. The verb 'computes' and resource 'statistical correlation' are specific, and it distinguishes from sibling plotting tools by emphasizing mathematical verification.

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 advises using this tool to mathematically verify relationships before plotting scatterplots, providing a clear context. However, it does not explicitly mention when not to use it or differentiate from sibling tools like 'rank_target_correlations' or correlation heatmaps, which might lead to ambiguity.

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