correlation_matrix

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

The description declares read-only behavior ('Read-only'), which is the only annotation available, but no annotations are provided. It adds clarity about the tool's non-destructive nature beyond the schema. However, it does not disclose details about handling non-numeric columns or missing values, which could be important behavioral traits.

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?

Two sentences, concise and front-loaded with the core purpose. The read-only note is efficiently appended. No wasted words.

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 3 parameters, it is a moderately complex tool. The description covers the main purpose and the key input (numeric columns), and the output schema exists, so return values are handled. Missing details on edge cases (e.g., non-numeric columns) but acceptable.

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%, but the description mentions 'numeric columns' which gives some meaning to the 'columns' parameter. However, it does not add detail for file_path or sheet_name beyond their schema titles. The description partially compensates for 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 a specific verb ('Compute'), resource (Pearson correlation matrix), and scope (numeric columns in a workbook sheet). It distinguishes itself from siblings like summary_statistics or rolling_average, though not explicitly naming alternatives.

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 correlation analysis but does not explicitly state when to use this over other statistical tools. There is no mention of when not to use it or how it differs from alternatives like summary_statistics.

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