get_correlations

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

The readOnlyHint annotation already marks this as safe, and the description adds that results are filtered to significant correlations, sorted by |r|, and capped at 200. This goes beyond the annotation, though 'significant' is vague and could refer to min_abs_correlation or statistical significance.

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 three concise sentences that each add value: what it does, what it returns, and when to use it. 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?

Given the full schema coverage and readOnly annotation, the description covers the tool's behavior well. Minor gaps: the meaning of 'significant' and the availability of Spearman are not mentioned, but these are in the schema/params. Overall adequate.

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?

The schema describes all parameters with 100% coverage, so the baseline is 3. The description doesn't add specific parameter guidance beyond mentioning the top_n cap, which the schema already states. It doesn't clarify method options or column restrictions.

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 pairwise Pearson correlations between numeric columns, specifies the output sorted by |r| descending, and distinguishes it from sibling tools like get_distribution or aggregate. It also notes filtering and a cap, making the purpose unambiguous.

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 provides an explicit use case: 'Use this to discover relationships in the data without manual exploration.' However, it doesn't mention when not to use it or alternatives, so it's clear but lacks exclusions.

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