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 burden of behavioral disclosure. It adds useful context by mentioning 'bounded' and 'descriptive,' and clarifies scope with 'Not a sport-performance model.' However, it does not disclose output format, read-only nature, or any side effects, leaving significant gaps.

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 two sentences, front-loaded with the core action and model types, followed by a concise scope disclaimer. Every word earns its place with zero waste.

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 tool has 12 parameters, no output schema, and no schema descriptions. The minimal description cannot adequately guide an agent on how to use the tool correctly; it lacks details on required inputs, interpretation of results, and how it differs from similar tools like analyze_series or aggregate_data.

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 undocumented parameters. It only clarifies the model parameter by listing the specific model types (OLS, Theil–Sen, quadratic, cubic), but leaves dataset, metrics, filters, dates, and other 8+ parameters entirely unexplained.

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 fits a bounded OLS, Theil–Sen, quadratic, or cubic descriptive model. It uses a specific verb ('Fit') and resource, and the explicit 'Not a sport-performance model' distinguishes it from sports-related siblings like power_curve_trend and get_ftp_history.

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 gives an implicit usage context by naming the model types and explicitly excluding sport-performance modeling, which hints at when not to use it. However, it does not name alternatives or provide explicit 'when to use' guidance, relying on the tool name and sibling context.

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