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

Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false. The description adds substantial behavioral context beyond annotations: it explains the error handling policy ('never raises a protocol error — it returns a JSON error object'), confirms idempotency and safety to retry, and mentions the 'honest fit quality' and a caveat in the output. These details give the agent a realistic expectation of the tool's behavior.

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 concise and well-structured. It starts with the primary action, then mentions licensing, gives an example, provides usage guidelines, explains error handling, and concludes with the tool's safety properties. Every sentence earns its place; there is no redundant or vague language. The information is front-loaded and easy to scan.

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's complexity (linear forecasting with fit quality), the description fully covers input, output, error behavior, usage context, and the trade-off against siblings. The existence of an output schema is complemented by a sample output in the description. Sibling tools are listed and one is explicitly referenced. The description leaves no significant gaps for an agent to guess about.

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%, but the description adds meaningful semantics beyond the schema: it provides a concrete typical input example showing how the parameters are used, and it reveals clamping behavior for periods_ahead ('values outside 1-12 are clamped') which is not documented in the schema. The example also implicitly demonstrates the required array structure for values, reinforcing the schema requirements.

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 opens with a specific verb-resource pair ('Forecast future periods with a linear trend') and distinguishes itself from siblings by explicitly naming growth_rates as the alternative for growth measurement. It also clarifies that it is not for seasonal or cyclical data, leaving no ambiguity about the tool's scope.

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 direct when-to-use guidance ('Use when a series is roughly linear and fit quality matters') and clear when-not-to-use instructions ('Not for seasonal or cyclical data, and not for measuring growth already observed (growth_rates)'). It also includes a typical input example and explains error handling behavior, making it easy for an agent to decide whether to invoke this tool.

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