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

Annotations already indicate readOnlyHint, idempotentHint, and destructiveHint false, so the description need not restate those. It adds value by specifying the decomposition model (additive) and that it returns per-timepoint components and summary amplitude. No contradictions with annotations. It does not discuss edge cases or data requirements, but for a read-only analysis tool, this is adequate.

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 efficiently convey the model, use case, and output. No redundant or filler content. The description is front-loaded with the key equation and then the application, making it easy to parse quickly.

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 complexity of a time series decomposition tool with no output schema, the description covers purpose and return type but omits details about the summary amplitude structure and the role of the undocumented 'time', 'entity', 'indicator' parameters. An AI might need to infer their usage from naming conventions. For a routine analysis tool, this is minimally adequate but not comprehensive.

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 40% with only 'full' and 'period' having descriptions; three parameters ('time', 'entity', 'indicator') lack any description in either schema or tool description. The description mentions 'monthly or quarterly' which indirectly relates to 'period' but adds no specific syntax for 'time', 'entity', or 'indicator'. The 'full' parameter is well-described in schema, but the description does not compensate for the undocumented required parameters, reducing its additive value.

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 performs additive decomposition (trend, seasonal, residual). It specifies the verb 'decompose' implicitly and the resource is a time series. The phrase 'strip seasonal cycle... reveal underlying trend' clarifies the purpose, and it is distinct from siblings like 'correlate' or 'decompose_drivers' which serve different analytical functions.

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 guidance on when to use: 'for monthly or quarterly data' with examples (retail sales, unemployment). It implies use for seasonal data but does not explicitly state when not to use or mention alternative tools among siblings. However, the context is sufficiently clear for an AI to select this tool for seasonal decomposition.

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