brand_mentions

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

No annotations are provided, so the description carries the full load and does disclose key traits: results are an approximate sample rather than a census, candidate terms are unverified and require judgment, call count is budgeted, and dryRun/export are supported. It does not say where export files are written or what happens when the call budget is exceeded and confirm is unset, but the main behavioral caveats are surfaced.

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 English sentence is front-loaded and dense with the right facts (collection scope, aggregation, candidate caveat, option flags). The full Korean restatement doubles the length without adding information, and the emoji is decorative, so it is slightly padded but still efficient overall.

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?

With 11 parameters and no output schema, the description does enough by describing the return shape at a high level: per-month counts and a co-mentioned candidate list with examples. What is missing is the cost/latency profile of a run and the side-effect detail for export files, but nothing critical for invoking it correctly is absent.

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 82%, so the schema already explains brands, aliases, baselineQuery, maxCalls, dryRun, exportAs, and maxPostsPerBrand. The description only restates that dryRun, call budget and xlsx/csv export exist, without adding syntax, defaults, or trade-offs beyond the schema text. That meets the baseline but does not exceed it.

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 names a concrete pipeline: collect blog/cafe/web posts mentioning book brands, aggregate them by month with approximate sampling, and surface co-mentioned candidate terms with examples. This clearly separates the tool from the raw search_blog/search_cafearticle siblings, whose purpose is retrieval rather than aggregation. It never names a sibling explicitly, so the differentiation is inferred rather than stated.

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 says to judge candidate terms yourself and mentions dryRun/export, which implies exploratory and export workflows. However, it gives no explicit 'use this when…' rule, no guidance on how it should be chosen over search_blog or brand_compare_trend, and no prerequisites for the call budget.

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