advanced_regression_analysis

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

No annotations are provided, so the description carries full burden for behavioral disclosure. The description mentions the output format but does not discuss potential side effects, data handling, or resource implications. Additional context about being a read-only analysis would improve transparency.

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 concise sentences: first covers purpose and output, second provides usage guidelines and alternatives. No redundant information; every sentence adds value.

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?

The description explains the return format and core functionality, but does not mention optional parameters (useTestSplit, includeMetrics, includeCoefficients, etc.) that can alter the output. Given the schema covers these, the description is largely complete but could be slightly more comprehensive.

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 coverage is 100% with descriptions for all parameters. The tool description provides high-level context but adds no specific details about individual parameters beyond what the schema already provides. Baseline score of 3 is appropriate.

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 fits regression models (linear, polynomial, logistic, multivariate) and returns a markdown report with coefficients, metrics, and interpretation. It also distinguishes itself from siblings: advanced_statistical_analysis for association without a model, and ml_model_evaluation for scoring existing predictions.

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?

Explicitly states when to use: 'when you have a designated outcome to predict' and provides specific alternatives for other scenarios. This clear guidance helps the agent select the correct tool.

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