datasets_apps_charts_search

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

With no annotations, the description carries the behavioral transparency burden. It discloses the data source, document structure (one per chart × snapshot × rank), default date behavior, and enums. Missing operational details like rate limits or exact return format, but the core behavior is well explained.

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 front-loaded: the first sentence states the purpose, followed by data model, defaults, and usage examples. Every sentence earns its place—no redundant fluff or repetition of schema content.

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 12 parameters, no annotations, and no output schema, the description covers the key aspects: data scope, source, defaults, enums, and a time-series use case. It does not explicitly describe return fields or pagination limits, but the schema covers parameter constraints, making this sufficient for a search tool.

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?

Despite 100% schema coverage, the description adds value beyond the schema by explaining enum semantics (store, chart type, platform, sort), the default behavior of `date`, and the app_id/sort combination for rank history. This enriches the bare schema descriptions with practical meaning.

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 'Search the app-charts dataset' and specifies the exact resource: daily top-chart snapshots from iOS App Store and Google Play. It distinguishes this from sibling tools like datasets_apps_search by focusing on chart snapshots and rank data.

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 concrete usage patterns: 'With no `date` the latest snapshot is returned' and 'pair `app_id` with `sort=date_desc` for an app's rank over time'. This gives clear context for common cases, though it does not explicitly mention when not to use this tool versus alternatives.

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