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

Annotations already declare readOnlyHint=true and idempotentHint=true, so the safety profile is covered. The description adds meaningful behavioral context beyond annotations: it returns a page URL that should be cited, enumerates the aggregate dimensions returned, and defines the omit-area listing behavior. There is no contradiction with annotations.

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 definition is dense but efficient: it packs the dataset scope, output categories, and area-omission behavior into one sentence, then adds a concise citation instruction. The long list of dataset families is necessary context rather than filler.

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?

Even without an output schema, the description tells an agent what it will receive: totals, share ratings, rank, breakdown by type, low performers, and a page URL to cite. It also covers the key parameter behavior (omit area to list areas). For a read-only, idempotent tool with two simple parameters, this is complete.

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 100%, so the baseline is 3. The description adds value beyond the schema by explaining that omitting area lists available areas, and by mapping dataset ids to real-world categories such as food hygiene ratings by local authority, US restaurant inspections, and building permits.

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 returns aggregate statistics for rating datasets and enumerates the exact output categories: totals, share rated well/poorly, rank among areas, breakdown by type, and low performers. It also calls out a distinct secondary behavior—omitting area lists available areas—which distinguishes it from raw-data query or dataset-search tools.

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 establishes a clear context for use: aggregate statistics over rating datasets, with concrete dataset families listed. It also provides a direct instruction for listing areas ('Omit area to list the areas available'). It does not explicitly contrast with siblings like query_dataset or search_datasets, so it falls short of a full when-not-to-use explanation.

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