Fit a descriptive series model

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

With no annotations, the description carries the full burden for behavioral disclosure. It reveals that the model is 'bounded' and that athleteId is required, but it does not state whether the operation mutates data, requires specific permissions, returns coefficients or predictions, or has rate limits. This leaves significant behavioral ambiguity for a tool that 'fits' a model.

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 carry a surprising amount of signal: model family, boundedness, domain exclusion, and a prerequisite. There is no filler, and the most important differentiators are front-loaded.

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 13 parameters, 4 required fields, no output schema, and zero schema descriptions, this description is far from sufficient. An agent would not know how to populate dataset, metrics, model, date filters, or resolution correctly, making the tool effectively uncallable without external knowledge.

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 must compensate for the 13 parameters. It only mentions athleteId and its source, leaving model, metrics, dataset, dates, filters, resolution, and cursor semantics unexplained. This is a major gap in practical usability.

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 states a clear action ('Fit') and a specific resource ('bounded OLS, Theil–Sen, quadratic, or cubic descriptive model'). It also explicitly disclaims being a sport-performance model, which helps differentiate it from related analysis tools. The prerequisite reference to list_athletes further narrows its identity.

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 clear context: this is for descriptive modeling, not sport-performance modeling, and it must be preceded by list_athletes. It does not name alternative tools or state explicit when-not-to-use conditions beyond the performance-model exclusion, but it is enough to route an agent toward appropriate use.

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