Correlation (descriptive)

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

Annotations already declare readOnlyHint=true, destructiveHint=false, openWorldHint=false, so the safety bar is low; the description still adds real value by disclosing that no p-value is returned and that a user confirmation step precedes execution. It does not say whether confirmation_token is mandatory or what happens if it is omitted.

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?

Three short sentences, front-loaded with what the tool computes, then the alternative, then the prerequisite. No filler.

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?

For a 10-param, no-output-schema analytical tool the description covers purpose and the confirm-first workflow but leaves the input source (arrays vs file) and duplicate column-name parameters unexplained, which is exactly the ambiguity an agent will hit when constructing a call.

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?

With 10 parameters and only 30% schema coverage, the description carries the burden but does not: it never explains the x/y arrays versus file versus x_name/y_name versus x_column/y_column alternatives, nor what pearson vs spearman do. Only the method choice ('r or rho') and CI are loosely implied.

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 definition names a specific computation (strength/direction between two numeric columns) and enumerates the outputs: r or rho, CI, least-squares line. Its explicit exclusion of a p-value separates it cleanly from the significance-testing siblings.

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?

It gives a clear when-not ('WITHOUT a p-value') and routes significance testing to a hypothesis_test with test "correlation", plus notes the user must confirm the columns first. The one weakness is that no sibling tool is literally named hypothesis_test (the list has run_test/choose_test), so the redirect is slightly ambiguous.

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