Seduction.lk

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

Annotations already declare readOnly, idempotent, non-destructive, and closed-world, so the safety profile is covered. The description still adds real context beyond them: the stock precondition and the explicit 'Nothing is ordered or charged', which reassures an agent this is a shareable link rather than an order placement. It omits link lifetime and whether repeated calls return the same link despite the idempotent hint.

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 short sentences, front-loaded with the core action, then the precondition, then the side-effect disclaimer. Every sentence adds distinct information with no repetition 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 one-parameter tool with no output schema, the description covers what is produced, what it does not do, and the main prerequisite. The remaining gap is the shape of the returned link and any expiry behavior, which matters because the whole point is handing that link to a shopper.

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 must carry the meaning of the single `items` parameter, and it only gestures at 'these products'. It never explains the product_id/quantity pairing, the default quantity of 1, or the caps (max 20 items, quantity 1-10), all of which an agent must discover from raw schema constraints alone.

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 artifact: 'Build a Seduction.lk link that opens the store with these products in the shopper's cart.' That is unmistakably different from the sibling read tools (get_product, search_products, list_categories), so an agent can select it without inspecting 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?

It states the key precondition ('Products must be in stock') and the intended outcome ('ready for them to check out themselves'), which tells the agent when this is the right call. It stops short of naming an alternative or explicitly saying when not to use it, but no sibling offers a comparable capability.

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, and destructiveHint=false, so the safety profile is covered structurally. The description adds value by enumerating the payload an agent will get back (price in LKR, stock, ingredients, skin types, rating, link), which matters because no output schema exists. It does not mention behavior on a missing/invalid id, which is the main remaining gap.

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 core purpose, and the field enumeration is justified by the absence of an output schema. The list is slightly dense ("rating and its link"), but no sentence 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?

For a single-parameter read tool with no output schema, the description covers purpose, input source, and return contents adequately. Only the not-found/error behavior is left unstated, which is a minor omission given the annotations already cover read safety.

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 goes beyond the schema's "Product id or slug" by telling the agent where to obtain a valid value (from search_products). It adds provenance rather than restating the type.

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 ("Full details of one Seduction.lk product") and enumerates exactly which fields are returned, so an agent can distinguish it from search_products or list_categories without opening the schema. The scope is unambiguous: one product, by id or slug.

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?

"Use the id or the slug from search_products" gives clear context for when this tool applies — after a search — and implicitly routes the agent to the right sibling for discovery. It stops short of stating exclusions (e.g. that it should not be used to browse or filter a list), so it is a 4 rather than 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, destructiveHint=false and a closed-world scope, so the safety profile is fully covered without the description. The description adds only that the returned content spans named policy topics, but says nothing about format, currency of the information, or whether it is static versus account-specific.

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 well-formed sentence that front-loads the core purpose and then lists covered topics. No filler, though the colon-separated topic list borders on enumerating rather than explaining.

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 parameters, no output schema and fully covered annotations, the description carries the remaining burden well by enumerating the topics it covers, effectively serving as loose return-value documentation. It could be stronger only by stating when to prefer this over the product-search siblings.

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 no parameter semantics to convey and the baseline is a 4. The description correctly makes no attempt to describe inputs.

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 resource and enumerates its scope (delivery areas/times/charges, payment methods, cancellations and returns, customer-service contact). An agent can immediately tell this is the store's static policy/FAQ content rather than product data, so it is distinguishable from product-oriented siblings even without naming them.

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 topic list signals this is the right call for policy, shipping, payment or returns questions. There is no explicit 'use this when...' statement, no exclusions, and no reference to alternatives among the sibling tools.

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 and closed-world, so the safety profile is fully covered elsewhere. The description's only added behavioral fact is that results carry product counts, which is useful since no output schema exists, but it omits ordering, pagination, and whether all brands are returned.

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 clause with no filler; the scope and the filter are stated before any elaboration. The phrasing is a fragment rather than a complete sentence, which is slightly less clear than it could be, but 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?

For a one-optional-parameter read-only list tool with full schema coverage and annotations, the description covers resource, scope, filtering, and the notable product-count output. Only result ordering and pagination behavior are unaddressed, which is minor at this complexity.

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 name parameter already documents partial-match semantics ("Part of a brand name"). The description restates the filter as optional and substring-based, adding little 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?

The description names the resource (brands sold on Seduction.lk), the scope, and an optional filter, so the agent knows it is an enumeration tool. It stops short of distinguishing itself explicitly from the sibling list_categories or search_products, leaving the brands-vs-categories split to be inferred from the noun.

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?

"optionally filtered by name" implies when to supply the filter, but there is no explicit guidance on when to use this tool versus search_products or list_categories. Usage is inferable from the resource name 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?

Annotations already declare readOnly, idempotent, non-destructive, closed-world behavior, so safety is fully covered. The description adds the useful fact that results carry product counts, but since no output schema exists it could go further, e.g. whether the hierarchy is returned flat with parent references or nested, and whether counts reflect all products or only available ones.

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 a concrete example that earns its place by clarifying the hierarchy notation. No filler, no repetition of the name or 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-parameter, read-only taxonomy tool, the description covers what it returns and shows the hierarchy format. Slight gap: with no output schema, the agent cannot tell whether the response is a flat list with parent fields or a nested tree, nor whether ordering is stable.

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 the description's job is minimal and it correctly introduces no parameter discussion. It even surfaces the output content (product counts), which is the only semantic detail an agent needs here.

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 resource (Seduction.lk product categories), illustrates the hierarchy with a concrete path example (Skin Care > Serums > Brightening Serums), and states the payload includes product counts. It's a noun phrase rather than a verb phrase, relying on the name/title for the 'list' action, but the resource and its shape are unambiguous and distinct from siblings like list_brands.

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: an agent can infer this is for browsing the category taxonomy, but there is no statement of when to prefer it over list_brands or search_products, nor any precondition or exclusion. Adequate but with a clear gap given the presence of taxonomy-related siblings.

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, closed-world, non-destructive reads, so the safety bar is covered. The description adds genuinely new behavior: what the response contains (LKR price, stock, link) and the default filter of in-stock items only. It stops short of noting the 20-result cap or ordering/pagination behavior.

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, no filler, with the scope and searchable facets front-loaded before the return values and stock default. The parenthetical example list is long but each example adds query-phrasing signal rather than padding.

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 correctly takes on the job of stating what comes back (price in LKR, stock, link), and it flags the in-stock default that an agent would otherwise have to dig out of the schema. Minor gaps remain around result limits and how price filters interact with the query, but nothing blocking for a single required string parameter.

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 80%, so most parameters are already documented in structured fields; the description mainly elaborates on what the free-text 'query' can contain, which does add real semantic range beyond 'What the shopper is looking for.' It says nothing about min_price/max_price or limit beyond what the schema already states, so a 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?

States a specific verb and resource ('Search Seduction.lk ... products') and enumerates the searchable facets (name, brand, ingredient, skin type, concern, plain-language need), which clearly separates it from sibling lookups like get_product or list_brands.

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 query-phrasing guidance with three examples, including natural-language needs, and states the in-stock default with an implicit override ('unless asked otherwise'). It does not explicitly name when to prefer list_categories, list_brands, or get_product instead, so routing against siblings 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.