get_correlation_matrix

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

No annotations are present, so the description carries full burden. It discloses the data source (per-iteration samples from ModelRisk) and that both Pearson and Spearman methods are computed. However, it does not explicitly state side effects, read-only nature, or permissions required, which are important for a read operation.

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 with no extraneous words. The first sentence states the core functionality, and the second provides optional usage guidance. Ideal length and structure.

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 presence of an output schema, return values need not be explained. The description provides context about ModelRisk simulation and the optional name list. It is complete enough for an agent to understand the tool's purpose and usage, though it lacks details about the output format (e.g., matrix structure), which is covered by the output schema.

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 must compensate. The description explains that the 'name_list' parameter restricts variables, adding meaning beyond the schema's type definition. The required 'workbook_name' is not explained but is contextually obvious.

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 Pearson and Spearman rank correlation between simulation inputs and outputs, citing the source (per-iteration samples from ModelRisk). The verb 'compute' and resource 'correlation matrix' are specific, and it distinguishes from siblings by naming both correlation types.

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 guidance on restricting variables via a name list, implying default inclusion of all variables. However, it does not explicitly state when to use this tool versus its sibling 'compute_correlation_matrix' or other correlation tools, nor does it mention 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.