get_brand_trends

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

Annotations already cover read-only, idempotent, and non-destructive behavior, so the description needed less safety disclosure. It adds useful behavioral context by noting each trend is time-stamped, includes fit/fail assessments, and should not be treated as a prescriptive directive — all beyond what the annotations convey.

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 focused sentences deliver the core purpose up front, followed by concrete trend examples and an important usage caveat. Every sentence earns its place, with no redundant filler or restatement of the tool name.

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?

For a zero-parameter, read-only informational tool, the description is complete. It explains what the trend data contains, gives representative examples, clarifies that trends are time-stamped, and tells the agent how to interpret the results — including the calibration caveat.

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?

The tool has zero parameters and 100% schema coverage, so there is no parameter ambiguity requiring explanation. The description still enriches the input semantics by indicating that the tool is self-contained and returns a curated trend set rather than needing filters or configuration.

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 specific verb ('Get') and resource ('current brand and visual-design trends') and goes further by listing concrete examples like bento grids, monospace type, and generative patterns. The 'current/2026/time-stamped' framing distinguishes it from static brand reference tools such as get_brand_system or get_brand_principles.

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 clearly communicates the intended use — checking current trend fit and calibration — and adds a strong usage caution: 'treat as a calibration signal, not a prescription.' It does not explicitly name sibling alternatives or exclusions, but the context is specific enough for an agent to select it appropriately.

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