Cubbie Club Offers

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

Since no annotations are provided, the description must carry the full burden of behavioral context. It successfully discloses what is returned—namely, the discount and the specific unlock page—which is crucial when there is no output schema to define the response format.

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 highly concise and efficient, using two clear sentences. The first sentence defines the action, and the second defines the return value, ensuring maximum information density with zero wasted words.

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 the simplicity of the tool (only one input parameter and no defined output schema), the description is remarkably complete. It defines both the required input (brand name) and the key pieces of expected output data (discount and unlock page).

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% for the single parameter, meaning the schema provides most of the necessary detail. The description adds semantic context by clarifying that the input must be a 'baby or kids brand,' though this is largely redundant given the clear string type in 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?

The description clearly states the tool's purpose: retrieving the current Cubbie Club discount for a specific brand. It distinguishes its scope by specifying retrieval 'by name,' which narrows its purpose compared to broader sibling tools like 'search_offers.'

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 implied by the singular parameter 'brand' and the explicit context of the discount being retrieved 'by name.' However, it does not explicitly instruct the agent on when to use this tool versus, for example, searching for brand offers based on other criteria.

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?

No annotations are provided, so the description carries the disclosure burden. It makes clear this is a read-only enumeration of live discounts and tells the agent what each entry contains, but says nothing about result size, pagination, ordering, or whether an empty result is possible.

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?

One sentence, zero waste, with the scope filter and return payload both front-loaded. Nothing could be cut without losing meaning.

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 no-param list tool with no output schema and no annotations, the description covers what is returned per item (discount, link) and the inclusion criterion. Only pagination/result-size behavior is missing, which is a minor gap at this complexity level.

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?

Zero parameters, so there is nothing to disambiguate; baseline 4 applies. The description correctly implies no filtering arguments are needed.

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?

Specific verb ('List') plus a precisely scoped resource ('baby & kids brands that currently have a live discount on Cubbie Club'), and it names the per-item payload (discount and link). It implicitly contrasts with get_brand_offer (single brand) via 'all', but does not explicitly differentiate from siblings.

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 'currently have a live discount' filter implies the useful context (live-offer discovery), but there is no explicit when-not or pointer to search_offers/get_brand_offer as alternatives. Usage is inferable rather than stated.

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?

With no annotations, the description carries the full burden and does a good job: it discloses that results include each discount plus a link to unlock it, and critically that promo codes are NOT returned here and require a signup at the link. This pre-empts the most likely agent failure mode. It stops short of stating result limits, pagination, or whether results are ranked, which leaves some behavioral gaps.

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?

Three tight sentences with zero waste. The searchable scope is front-loaded, the return shape follows, and the promo-code caveat comes last where it belongs. Every sentence adds information the agent needs.

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 single-parameter, no-output-schema search tool, the description covers purpose, query semantics, and return content including the key limitation that codes require signup. The remaining gap is result-set behavior (how many offers, pagination, ranking), which matters somewhat for a search tool but is not disqualifying.

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, and the description earns an extra point by widening the parameter's meaning beyond the schema: the schema says 'Brand name or keyword' while the description explicitly adds 'product type' with concrete examples like 'stroller' and 'diapers'. That tells the agent a generic product noun is a valid query, which the schema alone does not convey.

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 uses a specific verb and resource ("Search current baby & kids brand discount offers on Cubbie Club") and enumerates the searchable dimensions (brand name, keyword, product type). It does not explicitly distinguish itself from the siblings get_brand_offer and list_brands, so an agent still has to infer that this is the keyword-discovery entry point rather than a direct lookup.

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 usage context is implied by the query examples ('stroller', 'diapers', 'muffins', 'swim'), which show the tool is for broad discovery. However, there is no explicit when-to-use or when-not-to-use guidance, and neither sibling is named as the alternative — nothing tells the agent to prefer list_brands for browsing or get_brand_offer once a brand is known.

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