Curve-fit experimental data

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

Without annotations, the description carries full burden. It discloses the fitting behavior, return values, and constraints (e.g., positive values for power). It could mention error handling for invalid inputs, but overall 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?

Two sentences with the main action and return values front-loaded. Every sentence adds crucial information without redundancy.

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 tool with no output schema, the description covers return values (coefficients, R^2, formula string), usage constraints, and model types. It is complete and self-sufficient for an AI agent.

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 covers all three parameters fully (100% coverage). Description adds significant meaning: explains model choices (power vs. linear), the requirement of positive values for power, and the paired nature of x and y.

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 explicitly states the tool fits a least-squares model to paired experimental data and returns coefficients, R^2, and a formula string. It clearly distinguishes between 'linear' and 'power' models, providing specific context for each.

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 provides explicit guidance on when to use 'power' (dimensionless correlations, log-log fit, positive values) versus 'linear' (direct linear fit). It also explains the mathematical transformation for power model.

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