curve_fit

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

No annotations are present, so the description carries full burden. It only states the fitting method ('least squares') but fails to disclose convergence criteria, error handling, or whether covariance is returned. The output schema exists but is not referenced.

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 a single, efficient sentence that clearly communicates the core action. However, it is overly terse and could be expanded to include parameter hints without losing conciseness.

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 tool has 7 parameters, an output schema, and multiple sibling tools, the description is severely incomplete. It provides no information on model syntax, optional parameters, or how results are returned, leaving the agent underinformed.

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%, and the description does not explain any of the 7 parameters. The term 'nonlinear model' gives no clue about the 'model' string format (e.g., function expression), nor does it clarify parameters like p0, sigma, or absolute_sigma.

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 verb 'fit', the resource 'nonlinear model', and the method 'least squares'. It distinguishes from sibling tools like 'regression' (likely linear) and 'lstsq' (generic least squares) by specifying nonlinear modeling.

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?

No guidance is provided on when to use this tool versus alternatives such as 'regression' for linear fits or 'lstsq' for linear least squares. The agent cannot determine the appropriate context without explicit cues.

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