CardDeals Live Gift Card Deals

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

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, and destructiveHint false. The description adds meaningful behavioral context beyond those: it returns no purchase action and only provider or CardDeals checkout links. This clarifies expected output behavior and non-transactionality without contradicting the 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 description is compact and front-loaded. The first sentence states the core use case, followed by supported inputs, an exclusion, and return behavior. Every sentence adds necessary information with no 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?

For a single-parameter tool with full schema coverage, strong annotations, and an output schema, the description covers all essential aspects: when to use, what input forms are accepted, what not to use it for, and what the response will contain. Nothing critical 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 baseline is 3. The description adds value by giving a concrete example ('brand:starbucks') and clarifying accepted forms (brand slug, product ID, or exact brand name) beyond the schema's terse parameter description.

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 a specific verb ('get'), resource ('live deals for one gift-card brand'), and scope ('one exact gift-card brand'). It distinguishes itself from siblings by explicitly saying it is not for broad category discovery, so an agent can tell it apart from search-like 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?

It gives an explicit when-to-use condition ('when the user asks for complete current provider details for one exact gift-card brand') and an explicit when-not-to-use condition ('Do not use it for broad category discovery'). However, it does not name the alternative tool to use for broad discovery, so the routing guidance is slightly less direct than it could be.

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?

Annotations already cover the read-only, non-destructive, idempotent safety profile. The description adds meaningful behavior beyond those annotations: exact-match semantics, batching across up to 10 IDs, and the explicit failure behavior that unknown IDs appear in not_found rather than being guessed.

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 tightly-written sentences deliver the trigger condition, ID format, exclusion, alternative, and failure behavior with no filler. The most decision-relevant information is front-loaded.

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 read-only lookup with an output schema, the description covers when to use it, what the input looks like, the limit, the sibling to prefer for fuzzy cases, and how unknown IDs are handled. Nothing critical is missing for an agent to invoke it correctly.

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 ids parameter is already documented. The description adds valuable semantics beyond the schema by specifying the expected ID format ('brand:starbucks') and clarifying that exact brand slugs are acceptable, which directly helps the agent construct valid input.

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 states a clear action ('look up'), a specific resource ('CardDeals product IDs or brand slugs'), and the batching scope ('up to 10 ... to compare in one call'). It also differentiates itself from search_gift_card_deals, and the title signals the multi-item contrast with get_gift_card_deals.

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 explicitly says when to use the tool ('when the user supplies up to 10 exact... IDs or slugs') and when not to use it ('Do not use it for fuzzy discovery'), naming the alternative (search_gift_card_deals). This leaves little room for an agent to route incorrectly.

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?

Annotations already declare read-only, idempotent, non-destructive, and open-world behavior, so the bar is lower. The description adds geographic scope (U.S.), temporal scope (current), and return content (structured provider offers and destination links), which is useful beyond the 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?

Two dense sentences with no filler. The use case is front-loaded, the return value is stated, and the exclusion/routing guidance is included without unnecessary detail.

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 rich annotations, zero required parameters, and an output schema, the description covers scope, return content, and sibling differentiation. Nothing essential for selecting and invoking the tool 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?

The description does not elaborate on individual parameters, but schema coverage is 60% with useful descriptions for query, category, and min_discount_percent, while sort and limit are self-documenting via enums/defaults/min-max. The schema bears most of the parameter burden, so the baseline 3 is appropriate.

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 opens with a specific use case: discovering or comparing current U.S. gift-card discounts across brands or categories. It names the resource and action clearly and distinguishes itself from get_gift_card_deals for exact single-brand lookups.

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?

It states exactly when to use the tool (discover/compare discounts across brands or category) and when not to (exact single brand when get_gift_card_deals can return the full provider set). This provides a clear selection rule and names the alternative.

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