glowjakt

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

Beyond the readOnlyHint and destructiveHint annotations, the description adds substantial behavioral detail: sold-out offers are included to keep store_count consistent with search_beauty_prices, in_stock_store_count counts shippable offers, shipping semantics are defined, cheapest_store_incl_shipping vs is_cheapest are distinguished, and below_min_order is explained. This gives an agent reliable expectations about edge cases and return semantics.

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 its core purpose and usage, then organized into clear paragraphs and an Args section. It is longer than strictly necessary because an output schema exists, but the detailed output semantics are behaviorally useful and not filler. Slight trimming could be possible if the schema documents the field names, but overall structure and clarity are strong.

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 its complexity, the description covers when to use the tool, where to obtain the required parameter, supported markets, how to discover additional markets, and the exact semantics of in-stock, shipping, cheapest-store, and minimum-order behavior. No critical piece of information an agent would need to select and call this tool correctly appears to be 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 description coverage is 0%, but the description fully compensates by defining product_slug as a value originating from search_beauty_prices and by listing valid country codes with a pointer to get_service_info for the current market list. This adds real semantic meaning beyond the bare schema properties. The only minor omission is not stating the 'se' default, but the schema already exposes that.

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 'Compare one product's price across all stores that sell it', which clearly identifies the verb, resource, and scope. It also separates this tool from search_beauty_prices by instructing that product_slug must come from that tool, and the focus on a single product's cross-store comparison distinguishes it from siblings like get_price_history and find_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 states when to use the tool: 'when the user asks where to buy a specific product or wants to see every store's price for it'. It also gives a clear prerequisite by telling the agent to get product_slug from search_beauty_prices. However, it does not explicitly name exclusions or alternatives such as find_deals or get_price_history, so it stops just short of full when-not-to-use guidance.

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 mark this as read-only and non-destructive, and the description adds valuable behavioral nuances: discount_percent is based on the store's own previous list price, large discounts may be one-off clearances, prices exclude shipping, and min_discount affects returned count but not total_on_sale. These details go well 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?

The description is front-loaded with the core action and then adds only high-value information: usage trigger, pricing caveat, and parameter details. No sentence is wasted or redundant with the schema/annotations.

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 return-shape details are handled. The description covers all parameter semantics, usage context, pricing interpretation, and important caveats. An agent has everything needed to 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 0%, but the description fully compensates by documenting every parameter: country enumerates se/no/dk/fi, category lists valid values, min_discount gives an example and explains its filtering behavior, and limit specifies the 1-20 range. This gives the agent complete semantic information.

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 verb and resource: 'List beauty products currently discounted in a market, largest discount first.' It clearly defines the tool's scope and differentiates itself from price-history exploration by pointing to get_price_history.

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 explicitly states when to use the tool: 'Use when the user asks for current sales, offers or discounts, optionally within a category.' It also provides an alternative for related but different needs, noting that get_price_history shows the actual price trajectory.

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 mark the tool as readOnly and non-destructive, and the description adds useful behavioral detail beyond that: it returns the lowest price per day over 30 days, is limited to one product, and warns that available markets may change over time. This gives the agent expectations about scope and dynamic 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 well structured, front-loading the core purpose before usage guidance and parameter details. The argument list is slightly redundant with the schema, but the added semantic value justifies the length.

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?

The tool is simple with only two parameters, annotations already establish the safety profile, and an output schema exists to describe return values. The description covers purpose, usage triggers, parameter semantics, and the dynamic-market caveat, leaving no critical 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?

With 0% schema description coverage, the description fully compensates by defining both parameters. product_slug is explicitly tied to the output of search_beauty_prices, and country is explained with concrete market codes and a pointer to get_service_info for the authoritative list.

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 verb and resource: 'Return the recent price history for one product,' and narrows it further with 'lowest price per day over the last 30 days.' It clearly differentiates this from siblings like compare_product_prices by emphasizing single-product history over time.

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 explicitly states when to use the tool: when the user asks whether a price changed recently, whether a discount is new, or whether today's price is typical. It also tells the agent to get product_slug from search_beauty_prices, though it does not explicitly state when not to use it or name alternative tools for comparison.

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=true and destructiveHint=false, so safety is covered. The description adds meaningful behavioral context: devices and accessories lack INCI lists, certifications are third-party while brand_claims_unverified are the brand's own statements, and the latter should be presented as claims. This goes beyond the annotations without contradicting them.

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 purpose and usage, then provides field-level semantics and product-type caveats. Every section earns its place: the INCI caveat, certification vs. brand claim distinction, slug source, and country list all add necessary information without 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?

Given the tool's complexity, two parameters, output schema, and sibling context, the description is complete. It covers when to use the tool, how to obtain the required parameter, acceptable market values, and how to interpret certifications versus brand claims. It even handles the edge case of devices and accessories without INCI lists.

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 0%, so the description carries the full burden for parameter meaning. It defines product_slug as the value from search_beauty_prices and enumerates all current country codes with the note that get_service_info returns the updated list. This fully compensates for the bare string-type schema fields.

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 opening sentence names a specific action ('Return full details') and resource ('one product') and enumerates the returned contents: INCI ingredient list, key ingredients, description, images, and brand-level certifications and claims. This clearly distinguishes it from sibling tools that search, compare, or list prices.

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 states when to use the tool: when the user asks what a product contains, whether it includes or avoids an ingredient, or whether a brand's vegan/cruelty-free/organic status is certified. It also tells the agent to get product_slug from search_beauty_prices. However, it does not explicitly contrast with sibling list_key_ingredients or give negative usage cases, so it stops short of a full 5.

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 indicate read-only, non-destructive behavior, and the description matches this. It adds useful context about the kind of information returned, but does not disclose additional behavioral details such as data freshness, rate limits, or whether the returned statistics are cached.

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, with the return purpose in the first sentence and a practical usage hint in the second paragraph. Every sentence adds value without repetition.

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 zero-parameter, read-only service-overview tool with an output schema and supporting annotations, the description fully covers what an agent needs to know before invoking it or deciding it is the right tool.

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?

There are zero parameters, so no parameter documentation is needed. The description explains what the tool returns, which is sufficient for an invokable no-argument operation.

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 names a specific verb and resource ('Return service information') and enumerates exactly what is included: operator, markets, live catalog statistics, price matching, and tools overview. This clearly distinguishes it from the product-oriented sibling 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 gives clear guidance on when to use the tool: when you need to know which markets are supported or how the service works before calling other tools. It does not explicitly name alternatives or exclusions, but the context is strong enough for an agent to select it appropriately.

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 readOnly and non-destructive behavior, so the description adds useful extra context: slugs are identical across markets, display names are localized, and the 'Controversial' category maps to exclude_controversial=True. This goes beyond the structured fields without contradicting them.

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 with the essential purpose. The 'Args' section is clearly separated, and every sentence adds either usage guidance or behavioral context without fluff.

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 simple listing tool with an output schema, this description is complete. It covers purpose, usage triggers, localization behavior, the controversial-category link, and parameter semantics. 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 description coverage is 0%, but the description fully compensates for the country parameter by explaining it is the market for localized display names and listing valid values: 'se', 'no', 'dk', 'fi'. It also notes that get_service_info returns the current market list, which is valuable for future-proofing.

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 verb and resource: 'List the 67 ingredient groups accepted by key_ingredient and exclude_ingredient in search_beauty_prices.' This clearly identifies what the tool does and distinguishes it from the sibling search/price 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 explicitly states when to use the tool: 'Use when you need a valid ingredient slug, or to show the user which ingredients can be filtered on.' It provides clear context but does not explicitly name alternatives or exclusions, so it stops short of a 5.

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?

Beyond the read-only annotation, it discloses exact pricing behavior: EAN grouping, lowest in-stock price with sold-out fallback, and shipping exclusion. It also explains the two stock counters and natural-language mapping for ingredient filters. This level of transparency leaves little room for an agent to mispredict the tool's behavior, and nothing contradicts readOnlyHint/destructiveHint.

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?

Although long, the description is organized into a lead summary, usage statement, grouped behavioral notes, and an Args section, so no section is redundant. All content earns its place given 9 parameters and zero schema descriptions.

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 9 parameters, no enums, and no schema descriptions, the description covers every decision an agent needs: query requirements, filtering semantics, value domains, result grouping, stock fields, and sibling routing. The presence of an output schema means return-structure details are not required here.

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 0% schema coverage, the description fully compensates by documenting every parameter: example query values, country codes, category and attribute slugs, ingredient AND/OR semantics, max_price currency, and limit bounds. The empty-query-with-filters rule is explained, satisfying the schema's required query by allowing an empty string when filters are present.

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 opening sentence uses a specific verb and resource—'Search the beauty catalog'—and defines the outcome as returning the lowest current price per product. It further distinguishes this from compare_product_prices by clarifying the grouping and shipping exclusion. This gives an agent a precise identity for the tool beyond its title.

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 'Use when' sentence enumerates concrete triggers: finding a beauty product, checking cost, or browsing by category, price, ingredients, or brand values. It routes to compare_product_prices when shipping or total delivered price is needed, and points to get_service_info, list_key_ingredients, and get_product_details for valid values. This is explicit guidance on when to choose this tool versus its siblings.

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