brand_compare_trend

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

With no annotations, the description carries the burden and does add real value: it discloses anchor-keyword rescaling across batches, that values are relative indexes rather than counts, and that dryRun/call-budget/export exist. It still omits what the response looks like, cost/rate implications beyond maxCalls, and what happens when brands exceed the anchor batch.

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 core comparison and the relative-index caveat are front-loaded in the first two sentences. The Korean restatement is a full duplicate that adds length without new information, keeping it from a 5.

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 14-parameter, no-annotation, no-output-schema tool, the description covers the conceptual model well but leaves gaps around return structure, error/limit behavior, and shopping-mode prerequisites that an agent would want before invoking it.

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 100%, so the baseline is 3; the description reinforces the meaning of dryRun, call budget, export and the relative-index semantics but adds little parameter-level detail beyond the schema.

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?

States a specific verb and resource: comparing relative search/shopping-click trends across multiple book brands on a common anchor-rescaled scale. This clearly separates it from the single-keyword datalab_* explorers, though it never names a sibling explicitly.

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?

Usage is only implied through the source enum and the dryRun/call-budget mention; there is no explicit statement of when to prefer this over datalab_shopping_keywords or brand_mentions, nor prerequisites such as needing find_category for shopping mode (that detail lives only in the schema).

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