January Sales

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

Annotations already declare readOnly, idempotent, non-destructive and closed-world, so safety is covered. The description adds genuine behavioral context beyond that: it discloses the exact shape of the result (current sales with start/end dates and a link, plus the latest December and January sales history) and the underlying data source, which the annotations cannot 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?

The first two sentences are front-loaded and useful, but the trailing clause 'From January Sales' listings of UK shops' sales all year round (Black Friday, Boxing Day, January and the seasons between)' is rambling and its provenance adds little decision value. The sentence about 'its sales from the latest December and January' is also awkwardly phrased.

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 an output schema present, the description doesn't need to explain return values, yet it still gives enough scope (UK shops, today's sales plus recent December/January history, source coverage) for correct invocation. The one omission is sibling differentiation, which is the only real gap.

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?

Only one parameter exists and schema description coverage is 100% ('The shop's name, e.g. Next, John Lewis, Currys'). The description adds no further constraint on the shop argument, so the baseline 3 applies.

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 ('Checks whether a UK shop has a sale on today') and scopes it to UK shops with a clear temporal focus. It also names the return payload, distinguishing it from a generic sales-listing tool. It does not explicitly contrast itself with the sibling list_category_sales, so it stops short of 5.

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 never says when to use this tool versus alternatives; the sibling list_category_sales is never referenced. Usage is only inferable from the phrasing ('whether a UK shop has a sale on'), leaving the agent to guess that this is the per-shop lookup path rather than the category-browsing path.

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

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

The annotations already state readOnlyHint, idempotentHint, openWorldHint false, and destructiveHint false. Beyond those, the description adds real context: a 'today' time window, one sale per shop, a cap of five results, and the fields returned (start and end dates plus a link). It does not cover empty results, pagination, or timezone handling.

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?

Everything sits in one front-loaded sentence with the resource and scope stated first, followed by the return shape and data source. The trailing 'From January Sales' listings...' phrase is slightly awkward but does carry provenance information.

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?

An output schema exists, so the description does not need to walk through return values, yet it still summarizes the result shape. The annotations cover the safety and world profile, and the one-parameter schema is fully documented. For a simple read lookup, nothing material is missing.

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 schema already documents the single category parameter, including the same examples ('Electricals', 'Fashion', 'Furniture') that the description repeats. The description adds no extra meaning, such as matching behavior or case sensitivity, so the baseline of 3 applies.

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 gives a specific verb and resource: it finds UK shops with a sale today in one product category, and it names example categories. The category-scoped framing implies how it differs from check_shop_sales (shop-scoped), but it never names that sibling, so it stops short of full differentiation.

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 implies when the tool is useful (you want shops with a sale in a category), but it gives no explicit when-not-use guidance and never mentions check_shop_sales as the shop-level alternative. A caller must infer the choice from the tool names.

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