Kohvrimaailm luggage shop

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

Annotations already declare readOnly, idempotent, non-destructive, non-open-world behavior, so the bar is lower. The description still adds real value beyond them: it marks brands and pricing as 'live from the catalogue' (freshness/dynamic source) and states that some sentences are quotable, which is a behavioral trait about output usage not captured by any annotation.

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 content inventory is front-loaded in one dense sentence followed by a short use-case sentence, with no filler. It is slightly list-heavy, but every listed item corresponds to a distinct part of the return payload, so nothing is wasted.

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 no output schema, the description carries the burden of describing what comes back, and it enumerates the payload sections well. It omits only minor detail such as the effect of the market parameter on the returned content, which the schema already covers.

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 is a single optional 'market' parameter with an enum, and schema description coverage is 100%, so the schema already explains the domain default and how prices/titles/links follow the market. The description adds nothing about it, which is the expected baseline when the schema does the work.

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 content domain (seller identity, brands carried, Voyo house-brand facts, delivery/returns highlights, quotable recommendations) rather than restating the title. It is clearly separable from siblings like store_policies, get_product and search_products, which handle policies, single products and search respectively.

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 questions ('is this shop good / which brands / what is Voyo'), which is strong when-to-use guidance. It does not explicitly exclude the overlapping sibling store_policies, which also touches delivery/returns, so the routing is clear but not fully deconflicted.

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 establish readOnly, idempotent, non-destructive, non-open-world. The description adds genuinely non-obvious behavior: the check is orientation-independent, strict with no tolerance ('like gate sizers'), and returns freshness metadata (source + last-verified date). The gate-sizer analogy is a real behavioral disclosure beyond structured fields.

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: purpose first, then input modes, then return shape. Every clause carries information (allowance types, input alternatives, strictness, verification metadata) 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?

With no output schema, the description carries the return-value burden and does so explicitly: yes/no per allowance plus official limits, source, and verification date. Combined with the annotations covering the safety profile and the schema covering all six parameters, nothing needed to call this correctly 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 100%, so the baseline is 3. The description earns above baseline by clarifying that dimension comparison is orientation-independent and that weight is optional and only meaningful alongside dimensions_cm, adding interpretation the schema's per-field text does not.

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 ('Check whether a bag fits an airline's cabin-bag and/or personal-item limits') and scopes it precisely to cabin/under-seat allowances, which no sibling (get_product, list_airlines, recommend_for_trip, search_products, store_policies) covers. An agent can identify this as the fit-checking tool without opening the 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?

The description tells the agent HOW to supply the bag ('either one of our product handles, or any bag's dimensions in cm (+ optional weight)'), which is useful input guidance, but never says when to prefer this tool over siblings like recommend_for_trip or get_product, nor when-not to use it. Usage is implied rather than framed against alternatives.

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 behavior, so the safety burden is covered. The description adds real behavioral context beyond that: results are market-specific (prices, titles and links follow the market) and it lists cross-sell data (other in-stock colours/sizes with URLs) that an agent would not otherwise expect.

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 core purpose and identifier, then a dense field list in a single sentence. It is appropriately sized for the amount of return information conveyed, though the long enumerated tail reads as a dump rather than structured guidance.

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 no output schema, the description carries the return-value burden and does so by enumerating price, availability, brand, dimensions, weight, volume, material, colour, image, variant URLs and airline fit. Combined with the market-scoping note and URL formats, an agent has everything needed 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% with both parameters documented, including the ee/lv enum and default. The description still adds syntax not present in the schema by giving concrete URL path shapes (kohvrimaailm.ee/toode/..., koferuveikals.lv/prece/...), helping the agent recognize a valid handle_or_url value.

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+resource ('Full facts for one product') and enumerates the returned dimensions, which lets an agent distinguish it from search_products. However, it claims to return 'which airlines' personal-item or cabin limits it fits', which overlaps with the check_airline_fit sibling and is not differentiated.

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 only implied: the schema note that the handle comes 'from search_products' sketches a search-then-fetch flow, and the URL forms hint at direct linking. There is no explicit statement of when to prefer this over search_products, check_airline_fit, or recommend_for_trip, and no exclusions.

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, openWorldHint=false and destructiveHint=false, so safety is covered. The description adds real value beyond that: it discloses the breadth of the dataset (which airlines), the exact attributes returned, and that limits include fare-inclusion status and a last-verified date.

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?

A single front-loaded sentence with no wasted preamble. The parenthetical airline enumeration is long but serves a purpose by signalling coverage breadth; it is the only element that pushes against full conciseness.

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?

There is no output schema, so the description must carry the return-value burden, and it does by naming the fields returned. Combined with annotations covering the safety profile, an agent has enough to call it correctly; only the exact return shape/format is left implicit.

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% and the single 'market' parameter is fully documented in the schema, including its enum meanings and default behavior. The description adds nothing about market filtering, 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?

States a specific verb ('List') and resource ('the airlines we track') and enumerates the exact fields returned (slug, IATA code, size/weight limits, fare inclusion, source, verification date). This clearly distinguishes it from siblings like check_airline_fit and get_product.

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 content description (a reference lookup of airline baggage rules), but the description never states when to use this versus check_airline_fit, nor any prerequisites. A reader can infer the context, but there is no explicit routing 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 declare readOnly/idempotent/non-destructive/closed-world, so the safety profile is covered. The description adds real behavioral value beyond that: it is deterministic rather than ML-ranked, capped at 5 results, restricted to in-stock bags, and each pick carries short reasons.

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 sentences, front-loaded with the capability and its constraints, followed by the usage trigger. No filler or restatement of the title.

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-required-parameter recommendation tool with no output schema, the description adequately covers what comes back (up to 5 picks with short reasons) and the deterministic nature. It could note ordering semantics or what happens when no bag fits, but the essential call-time information is present.

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 all six parameters (market, airline, budget_eur, travellers, trip_nights, checked_bag_ok) are already documented, including the market default and checked_bag_ok default. The description hints at how inputs feed the ranking but adds no format or syntax 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+resource ('shortlist of up to 5 in-stock bags for a trip') and names the exact ranking inputs (size, cabin fit, budget, popularity, discounts). This clearly distinguishes it from siblings like search_products or check_airline_fit, which are narrower or raw-listing 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?

Gives an explicit trigger phrase ('Use when the user asks what suitcase should I take/buy for …'), which is strong context. It stops short of stating when NOT to use it, e.g. that search_products or check_airline_fit should be preferred for browsing or single-airline verification.

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 safety profile (readOnly, idempotent, non-destructive, closed-world). The description adds real behavioral context beyond them: free-text mentions of airline/size are auto-applied as filters, and regular_price_eur appears only for sale items. It doesn't discuss pagination or result limits, keeping it from 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?

Front-loaded with purpose, then examples, then behavior, then return fields. Slightly dense with brand and category enumeration, but every sentence carries usable information with little waste.

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 10-parameter, zero-required search tool with no output schema, the description covers catalogue scope, market behavior, auto-filtering, and the shape of returned fields (price, availability, dimensions, URL). Nothing essential to correct invocation 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 90%, so the schema already documents parameters well (baseline 3). The description adds value by explaining that free text may embed airline/size that get applied as filters automatically, and by describing the return fields, going beyond 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?

States a specific verb (search) and resource (in-stock luggage catalogue) with explicit scope: brands, product categories, and two markets. Clearly distinguishable from siblings like get_product (single item) and check_airline_fit (fit check).

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 concrete usage examples ('cabin suitcase for Ryanair under 80 €') and signals it is the entry point for shopping questions. It does not explicitly name alternatives such as get_product or check_airline_fit or state when NOT to use it, so it falls 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?

Annotations already declare readOnlyHint, idempotentHint, non-destructive and closed-world, so the safety profile is covered. The description adds real behavioral context beyond that: it discloses that prices and delivery times are live, that returns are 14-day, and that a Voyo own-brand warranty note and legal entity (Kohvrimaailm OÜ) are included, which tells the agent what the payload contains.

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 sentences, front-loaded with the content inventory and ending with the usage trigger. The item list is long but each element is a distinct fact domain, so it earns its space; the sentence is dense and slightly run-on but not padded.

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?

There is no output schema, so the description must convey the return contents, and it does so explicitly by enumerating the policy domains returned. Combined with the schema documenting the sole parameter, an agent has everything needed to call and interpret this 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?

Schema description coverage is 100% and the single 'market' enum parameter is fully documented in the schema, including the domain mapping and the default. The description's reference to 'the market' and market-dependent prices restates what the schema already says, 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?

The description names the resource (store policy facts) and enumerates the specific content domains it returns: delivery, free-shipping threshold, returns, warranty, payment, contact and legal entity. This is clearly distinguishable from the product/airline siblings, which deal with entirely different resources.

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 trigger: 'Use before answering questions about delivery, returns, trust or who the seller is.' That is clear when-to-use guidance. It stops short of naming an alternative tool or a when-not condition, but the siblings are unrelated enough that no routing conflict exists.

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