linear_regression_analysis

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

With no annotations, the description must disclose behavioral details, but it only vaguely states that it returns 'Dictionary with regression results.' It omits what results are included (e.g., coefficients, R-squared, p-values), any assumptions (e.g., equal-length arrays), and potential side effects or limitations. This is insufficient for a statistical analysis tool.

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 very concise and well-structured with clear sections for Args and Returns. Every sentence is purposeful, though the Returns section is vague. It earns a high score for brevity and readability, but not a perfect 5 due to the lack of detail in the return specification.

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 statistical tool, the description is incomplete. It does not explain what the output dictionary contains, what statistical assumptions are made, or how to interpret the results. The lack of an output schema makes this gap more critical. Essential context for using linear regression is missing, so the tool is only minimally usable as described.

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 input schema provides only titles ('X Data', 'Y Data') with no descriptions, so the description must compensate. It does define each parameter as 'Independent variable data' and 'Dependent variable data,' adding basic meaning. However, it does not specify array length requirements, type constraints beyond numbers, or how they relate. This partially compensates for the 0% schema coverage but leaves room for improvement.

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's function with a specific verb and resource: 'Perform simple linear regression analysis.' This distinguishes it from sibling tools like correlation_analysis or hypothesis_test_ttest, 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?

The description offers no guidance on when to use this tool over alternatives. It does not mention any context, prerequisites, or situations where other statistical tools would be more appropriate. This absence of usage direction leaves the agent to infer suitability.

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