Whale Coupons

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

Annotations cover the safety profile (readOnly, idempotent, non-destructive, closed-world), and the description adds substantial context beyond that: result cap of 3 stores, the found=false and match="product" edge cases, a 'never invent a code' guarantee, daily update cadence, and the coverage gap for large general marketplaces.

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?

Front-loaded with the triggering condition, then inputs, returns, edge cases, coverage and exclusions. Despite its length, each sentence carries distinct information an agent needs to select and call the tool correctly.

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?

Output schema exists, yet the description still summarizes return contents and failure modes. Combined with full parameter and annotation coverage, an agent has everything needed to call it and interpret results.

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?

With one parameter at 100% schema description coverage, the schema already documents accepted formats and examples. The description restates the same accepted inputs (store name, domain, URL) without adding syntax or validation detail beyond the schema, so 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 ('find coupon codes') plus the accepted inputs (store name, domain, product URL). It distinguishes itself from sibling search_stores by scoping to coupons for a specific store rather than a product type.

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?

Explicitly gives when-to-use (user wants a coupon/promo/discount code or shares a store URL), names the alternative (search_stores for a product type), and lists exclusions (price comparison, reviews, order tracking, buying on the user's behalf). Nothing is left to inference.

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 readOnlyHint, idempotentHint, destructiveHint=false and openWorldHint=false, so safety is covered; the description adds the meaningful prerequisite that the slug must come from a prior result, which prevents speculative lookups. It also enumerates returned fields, though that partly overlaps the existing output schema.

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 tight sentences, zero waste, with the routing constraint front-loaded and the fallback tool named last. Every clause earns its place.

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 lookup with an output schema and full annotation coverage, the description supplies the precondition, the alternative route and a sense of the payload, leaving nothing an agent needs to call 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% and the single parameter already documents 'store slug exactly as returned in a previous result', including a format example. The description reinforces the provenance constraint but adds no syntax or format detail beyond the schema, 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 ('get one store's coupon details') and explicitly scopes it against the sibling: 'For a store name, domain or URL use find_coupons instead.' An agent can distinguish it from find_coupons and search_stores without opening any schema.

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?

Gives an explicit precondition ('only when you already have a Whale Coupons store slug from an earlier result') and names the alternative tool plus the exact condition that selects it (name, domain or URL). Both when-to-use and when-not-to-use are 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?

Annotations already declare readOnlyHint, idempotentHint, destructiveHint=false and openWorldHint=false, so the safety profile is covered. The description adds that results include a per-category store count and page link, but this overlaps with the existing output schema. No additional behavioral context (caching, ordering, freshness) is provided beyond what structured fields already 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?

Two tight sentences with zero filler; the trigger condition is front-loaded and the exclusion/alternative note follows. Every clause carries 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?

With an output schema present and full annotation coverage, the description need not explain the return payload in detail. It supplies the trigger, the exclusion rule, and the alternative tools, which is everything an agent needs to select and invoke 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?

The tool takes zero parameters, so there is nothing to document and the baseline of 4 applies. The description correctly implies no input is required to call it.

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 specific verb and resource (list store categories) and clarifies the triggering user intent (what kinds of stores/products Whale Coupons has codes for). It also distinguishes itself from siblings by naming search_stores and list_largest_discounts, so an agent can tell them apart without opening any schema.

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 trigger ('when the user asks what kinds of stores or products...') and an explicit when-not-to-use ('Not needed before search_stores or list_largest_discounts'). The reasoning is supplied: those siblings' category parameter already enumerates valid names, which fully routes the agent.

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 readOnly/idempotent/non-destructive/openWorld=false, so safety is covered. The description adds genuinely non-derivable behavior: results are sorted largest-to-smallest, fixed-amount discounts ($30 off) are deliberately excluded because they can't be ranked by percentage, and it enumerates the returned fields. It doesn't discuss pagination or empty-result behavior, so not a 5.

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?

Four sentences, front-loaded with the trigger condition, then output semantics, then the exclusion caveat. No filler or restatement of the title; every sentence carries new 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?

With an output schema present, return-value explanation is optional, and the description still summarizes the payload. Combined with annotations covering safety and a fully documented schema, an agent has everything needed to select and invoke this tool 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 baseline is 3, but the description adds semantic framing the schema lacks: it maps natural-language user intent onto min_discount ("at least min_discount percent off") and category ("biggest beauty discounts"), clarifying that category is a restriction rather than a ranking modifier. limit is left to 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 states a specific verb (list) and resource (stores with the largest percentage discounts), plus scope details: sorted descending, threshold-filtered by min_discount, code-level data returned. It is immediately distinguishable from find_coupons (named store) and search_stores.

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 explicit trigger conditions with example user phrasings ("biggest beauty discounts", "codes for at least 30% off") and an explicit exclusion routing to a named sibling: "Do not use for a specific named store (use find_coupons)." That is the when/when-not/alternative trifecta.

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 annotations already covering read-only, idempotent, non-destructive, and non-open-world safety traits, the description adds substantial behavioral context: matching order, up to 25 returned stores, what fields come back, and what happens when no match is found. This goes well beyond the structured annotation data.

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 front-loaded with the primary use case, then covers matching, returns, parameter guidance, fallback, and exclusions without wasted sentences. Despite its length, every sentence supports routing or invocation.

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 tool has three optional parameters, full schema coverage, annotations, an output schema, and multiple siblings, the description supplies the missing decision context: when to use it, when not to, and how results behave. Nothing critical for correct invocation appears absent.

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 description coverage is 100%, so the schema already documents limit, query, and category. The description reinforces that query, category, or both may be supplied and gives category-inference examples, but it adds little parameter syntax or constraint detail beyond the schema, matching the baseline for high schema coverage.

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 specific verb and resource: searching stores for coupon codes by product or category rather than a named store. It explicitly distinguishes this tool from siblings find_coupons and list_largest_discounts, so an agent can route correctly without opening schemas.

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 explicit when-to-use conditions, concrete example queries, and clear when-not-to-use guidance with named alternatives. The fallback behavior when nothing matches is also stated, leaving little to inference.

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