correlation_analysis

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

With no annotations, the description must carry the full burden of behavioral disclosure. It only states that it returns a dictionary with coefficients and analysis, but omits specifics like correlation method, assumptions, missing data handling, and side effects. Though it doesn't contradict annotations (none provided), it is minimally transparent.

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 concise, with a clear purpose statement and structured Args/Returns sections. However, the brevity borders on under-specification, as it omits crucial details like correlation type and return format.

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 simple two-parameter tool, the description gives a basic outline but lacks completeness. It doesn't explain what 'analysis' includes or what the dictionary keys are, and with no output schema, the return value remains ambiguous. It's adequate but with clear gaps.

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 needs to compensate. 'First dataset' and 'Second dataset' are tautological and add no meaning beyond the schema's array-of-number type. It doesn't specify required lengths, alignment, or data assumptions, leaving the parameters inadequately explained.

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 it performs correlation analysis between two datasets, identifying the specific operation and resource. However, it doesn't differentiate from sibling tools like linear_regression_analysis or hypothesis_test_ttest, which also work with datasets, so it falls short of a 5.

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?

There is no guidance on when to use this tool versus alternatives, no prerequisites, and no exclusions. The description simply states what it does without indicating context or comparative use cases.

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