Sense2 Promotional Products

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

No annotations provided, so description bears full burden. It does not disclose read-only nature, permissions, rate limits, or side effects. The description only states content structure, not behavioral traits.

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?

Single sentence of 22 words, efficiently conveying purpose and structure. No wasted words.

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?

Description explains what is returned (curated list, quarterly, structured) but lacks return format, authentication needs, or comparison to sibling tools. Adequate for a simple no-param tool but incomplete given no output schema or annotations.

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?

No parameters exist, so baseline is 4. Description adds no parameter info, but none is needed. Schema coverage is 100% trivially.

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?

Description clearly states the tool returns 'The 100 Best Promotional Products in Australia' with structure (10 sections x 10 products, editorial intros). It distinguishes from siblings like get_product (single product) and search_products (search results) by specifying a curated quarterly flagship edit.

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?

No guidance on when to use this tool versus alternatives (e.g., get_100_best_intel, search_products). The description provides no context for appropriate use or 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?

No annotations are present, and the description fails to disclose any behavioral traits such as read-only nature, authentication needs, or response size. It simply enumerates content without behavioral context.

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 a single, long run-on sentence listing many items. It lacks structure and conciseness; important information is not front-loaded efficiently. It could be broken into bullet points or shorter sentences.

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 no parameters and no output schema, the description does a fair job enumerating the content. However, it does not describe the format or structure of the returned data (e.g., JSON keys, nested objects), leaving some ambiguity.

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 has zero parameters, so the baseline is 4. The description adds meaning by detailing what the output contains, which is valuable given the absence of an output 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 lists multiple components (lenses, test, insights, guide, etc.) but does not explicitly state the action (e.g., 'Retrieves the editorial intelligence layer'). The verb is implied by the tool name. It distinguishes from siblings by specifying the content, but the purpose is somewhat buried in a verbose list.

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?

No guidance is provided on when to use this tool versus alternatives like get_100_best or get_brand. The description does not mention any prerequisites, conditions, or 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?

No annotations are provided, so the description carries the full burden of behavioral disclosure. It conveys read-only intent through the verb 'List' and describes return contents, but it does not explicitly state that it is read-only or mention any auth/rate-limit/side-effect details. For a zero-parameter list tool this is acceptable, but not fully transparent.

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 two sentences with no filler. The first sentence front-loads the core listing behavior and return contents, while the second sentence provides a concise routing rule.

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 tool with no parameters, no output schema, and no annotations, the description clearly covers what the tool returns (counts, URLs, relatedGuides) and how it relates to sibling tools. Nothing essential is missing for correct selection and invocation.

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 input schema has an empty properties object, meaning there are zero parameters. With no parameters to explain, the description need not add anything; the baseline for zero-parameter tools is 4.

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 uses a specific verb ('List') and identifies the exact resource: Shop by Aesthetic editorial collections with counts and URLs. It also distinguishes itself from the sibling get_collection by explicitly directing detailed guides elsewhere.

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 get_collection instead ('Use get_collection for those detailed guides'), making the choice between the two tools clear. This is explicit routing guidance rather than leaving the agent to infer.

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?

No annotations are present, so the description must convey behavioral traits. It implies a read-only retrieval operation but does not specify authorization requirements, error handling for invalid slugs, idempotency, or any side effects. It adds minimal behavioral context beyond listing returned 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?

The description is a single, efficient sentence with no wasted words. It front-loads the purpose and lists key content, making it easy to parse quickly.

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 simplicity (one parameter, no output schema), the description adequately covers the return value by listing main fields. It could be improved by mentioning idempotency or that it returns a single object, but it is sufficiently complete for a straightforward retrieval 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?

The input schema covers 100% of parameters, and the single 'slug' parameter is described. The description adds value by enumerating the fields returned (story, signature, logo tagline, industries), which clarifies the output's meaning 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?

The description clearly states the tool retrieves full brand case-study details by slug, listing specific fields (story, signature, logo tagline, industries). The verb 'Get' and resource 'brand case-study details' are specific, and the use of a unique identifier (slug) distinguishes it from siblings like get_brands (which likely lists all 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?

No guidance is provided on when to use this tool versus alternatives such as get_brands (list) or get_brand_moves (related brand moves). The description does not mention prerequisites, use cases, or when not to use it.

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?

Despite no annotations, the description adequately conveys that the tool retrieves a fixed set of creative strategy concepts. It does not disclose any side effects, rate limits, or other behavioral traits, but the tool is simple and read-only.

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 one sentence that efficiently conveys the tool's purpose and content. It is front-loaded with the main item and contains no unnecessary words.

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 tool with no parameters and no output schema, the description sufficiently explains the returned content (12 Brand Moves and Combining Aesthetics framework). It lacks structural details about the output format, but is adequate for a retrieval 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?

The input schema has zero parameters. Per guidelines, baseline is 4. The description adds meaning by detailing the content returned (brand moves and framework), which compensates for the lack of parameters.

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 that the tool provides 'The 12 Sense2 Brand Moves' and 'Combining Aesthetics framework', specifying the exact content. This distinguishes it from sibling tools like 'get_aesthetics' or 'get_inspiration'.

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?

No guidance is provided on when to use this tool versus other similar tools (e.g., 'get_aesthetics', 'get_brand'). The description does not mention any prerequisites or context for selection.

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?

No annotations are provided, so the description carries the burden. It transparently states the tool returns a static list of brands with specific fields. It does not mention authentication or rate limits, but for a read-only list that is acceptable.

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?

Single sentence, front-loaded with action and examples, no unnecessary words. Highly efficient and well-structured.

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 no output schema and no parameters, the description fully covers what the tool returns: a list of brands with industry, headline, and URL, with concrete examples. Complete for the tool's simplicity.

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?

No parameters exist. The description adds meaning by detailing the content of the list (specific brands, fields) beyond what the empty schema provides. Baseline for 0 params is 4, and the description exceeds 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 clearly states the verb 'list', the resource 'brands', and provides specific examples (Google, Nike) and fields returned (industry, headline, URL). It distinguishes from the singular 'get_brand' sibling.

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 implies usage for listing all case-study brands, but does not explicitly state when to use this tool versus alternatives like 'get_brand' or when not to use it. With 18 siblings, more guidance would be beneficial.

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?

No annotations are provided, so the description carries full burden. It discloses that it returns both matching missions and a concierge (Findie) for unmatched briefs, which is valuable behavioral insight. However, it could mention if it modifies any state or requires authentication, but since no annotations exist and the tool appears read-only, the current level is adequate.

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, zero waste. The first sentence describes what it returns with clear examples; the second provides usage guidance. Perfectly front-loaded and efficient.

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 only one optional parameter and no output schema, the description fully covers its purpose, usage, and key behavioral trait (returning Findie for non-matches). It is complete for an agent to correctly invoke and interpret the 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 coverage is 100%, so the schema already documents the query parameter. The description adds that it's an 'optional keyword filter' but no additional meaning beyond the schema. 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 clearly identifies the tool as providing 'pre-solved buying briefs' with concrete examples like 'conference giveaways under $5 for 500 people'. It differentiates from siblings by stating to use it FIRST for questions with quantity, budget, or deadline, making its purpose distinct.

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 states 'Use this FIRST for any question that includes a quantity, budget or deadline', providing clear when-to-use guidance. Also implies fallback behavior by mentioning Findie for non-matching briefs, effectively guiding tool selection.

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?

No annotations are provided, so the description carries the full burden. It communicates a read-only list operation, all-category scope, and the inclusion of product counts. The second sentence implicitly clarifies that live picks and FAQs are not part of this tool's response, which is useful behavioral context. It does not mention pagination or ordering, but those are less critical for a zero-parameter list.

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. The primary behavior is front-loaded in the first sentence, and the sibling routing is a single useful second sentence. Every word 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 simple zero-parameter list tool with no output schema, 'all product categories with product counts' plus the explicit pointer to get_category is largely sufficient. It could detail the exact category response fields (e.g., ids, slugs, nesting), but the description covers the essential invocation and output intent.

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 input schema is empty (0 parameters), so the baseline is 4. There are no parameters for the description to document, and the description doesn't introduce any parameter-related ambiguity.

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 uses a specific verb ('List'), identifies the exact resource ('all product categories'), and states the distinguishing output ('with product counts'). It also names the sibling 'get_category' to differentiate the single-category tool, so an agent can tell them apart immediately.

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 alternative: 'Use get_category for one category's live picks and buyer FAQs.' This gives the agent a clear decision rule between this tool and its closest sibling.

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 no annotations, the description carries the disclosure burden and does so well by emphasizing that results are live, derived from the live catalogue, and should be cited rather than inferred. It also clarifies price semantics (AUD ex-GST at volume, MOQ separate). Minor gaps remain around empty/unknown-id behavior, but the core behavioral traits are transparent.

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 opening phrase names the tool's core output immediately, and the long listing is purposeful because there is no output schema to define the return payload. It is dense rather than concise, but every clause contributes necessary 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?

Because there is no output schema, the description must specify what the agent gets back; it enumerates the three picks, their components, and the full FAQ list. It also supplies the id-discovery workflow and the live-data caveat, making the definition complete for correct invocation.

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?

Input-schema coverage is 100%, so the baseline is 3. The description reinforces id provenance by pointing to get_categories and semantically describes the category-page content, but it adds no new parameter-level details beyond what the schema already states for id and locale.

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 verb 'Get' plus a specific resource ('one category's live picks'... 'buyer FAQs the category page publishes') leaves no ambiguity about what is returned. It also points to get_categories for id discovery, distinguishing the singular category-scoped tool from its plural sibling.

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 establishes clear context: call it when you need the category-page picks and FAQs for a single category, and it tells the agent to discover ids with get_categories. It does not explicitly list when-not-to-use alternatives such as get_product or get_entry_pricing, but the scope is clear enough to route correctly.

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?

No annotations are provided, so the description carries the full burden. It discloses that prices are AUD ex-GST, that branding is 1-colour/1-position print included, that `brandingIncluded` states full-colour extras, that the data mirrors a public URL, and that the optional category filter is by name or id. It does not state whether the result is a list or single object, but the 'NAMED cheapest branded product' phrasing implies a per-category result set, and the output schema is absent so the description does more than most.

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 dense but every sentence earns its place: scope, price points, currency, branding inclusion, data source, use-case routing, and parameter hints. It is front-loaded with the core purpose and ends with the optional filter. Slightly long, but the density is justified for a tool with no annotations and no output schema.

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 read-only lookup tool with 2 optional parameters and no output schema, the description covers the purpose, the data source, the pricing semantics, the branding caveat, and the follow-up tool. The only notable gap is the exact return shape (array vs object, field names), but the description explicitly says to cite the named product and price+quantity pair verbatim, which tells the agent what to extract.

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 schema already documents both parameters. The description adds value by explaining the `category` filter semantics ('name substring or id') and giving examples ('pens', 'tote', '57'), and by clarifying that `locale` localises names/URLs but keeps prices AUD. This goes beyond the schema's own parameter descriptions.

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 ('get'), a precise resource ('cheapest branded product in each of ~100 promotional categories'), and enumerates the categories and price points. It also names the sibling it is not (get_quote) and the exact use case, so an agent can distinguish it from the 28 sibling tools 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 explicitly says 'Use this FIRST for ...' and lists concrete query phrasings ('cheapest promotional X in Australia', 'cheap branded pens/totes/keyrings', 'budget giveaways', 'what is the cheapest way to brand …'), then routes to get_quote for an exact quantity. This is explicit when-to-use guidance with an alternative named.

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 no annotations, the description carries the full burden, and it delivers: it discloses that prices are AUD ex-GST, include a 1-colour 1-position print, are calculated at the product's MOQ, and that 'cheapest' is a catalogue fact rather than a market rank. It also explains the volume floor, setup fee, and quantity tiers, giving the agent an accurate behavioral model of the tool.

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 dense but purposeful, with all key behavioral facts included and the primary use case stated near the end. It is longer than strictly necessary because of the parenthetical examples and quantity-tier enumeration, but every detail contributes to selection or invocation confidence.

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?

Despite having no output schema, the description fully explains the returned content: the named product, prices at MOQ and specific quantity tiers, volume floor, setup fee, and currency. It also covers the optional filter behavior and follow-up workflow with get_quote, so an agent has enough context to invoke the 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 description coverage is 100%, so the schema already documents both parameters well. The description mostly repeats the collection substring/slug filter behavior and adds little beyond the schema, though it does reinforce the optional nature of the collection parameter.

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 ('get'), a precise resource ('the NAMED cheapest branded product in each live Sense2 collection with 8+ members'), and the full scope of what is returned. It clearly distinguishes this from related tools like get_cheapest_by_category by anchoring on collections rather than categories.

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 says to use it for 'cheapest X for a conference / real-estate / wellness / eco …' briefs and tells the agent to cite the result verbatim and then call get_quote for an exact quantity. It does not explicitly list exclusions or compare directly to alternative sibling tools, but the intended use case is clear.

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 no annotations provided, the description carries the full burden of behavioral disclosure. It clearly describes what the call returns, including the three pick types, pricing fields, URLs, FAQs, and conditional guidance for reviewed collections. It does not mention non-mutation explicitly, but nothing suggests side effects.

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 dense but purposeful, front-loading the core purpose and then adding useful detail about contents and usage. It is longer than strictly necessary and somewhat run-on, but every clause contributes relevant 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?

For a two-parameter lookup tool with no output schema, the description provides enough context to select and invoke it correctly: resource scope, return contents, conditional behavior, slug discovery, locale/currency caveats, and alternative tools. No critical gap remains for correct invocation.

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 slug and locale. The description reinforces the slug parameter by noting it works for every slug in get_collections and clarifies that this is an English/AUD reference, but it adds only modest semantic value 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?

The description names a specific resource (a collection) and a concrete outcome: retrieving live picks with product, price, URL, FAQs, and optional guidance. It also differentiates itself from siblings by referencing get_collections as the slug source and get_product/get_quote as currency-related alternatives.

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 says when to use the tool ('Use for precise briefs such as...'), how to discover valid slugs ('Discover slugs with get_collections'), and when to prefer alternatives ('use get_product or get_quote for local currency'). This gives an agent clear 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?

The description discloses data semantics (e.g., the count is dynamic, fromPriceAud is not a quote, and get_quote should be used for exact pricing) but does not address typical behavioral aspects such as read-only guarantees, potential side effects, rate limits, or auth requirements. With no annotations provided, the description carries the full burden, and it falls short on these points.

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 main action is front-loaded, but the description is verbose and somewhat repetitive, especially the price semantics ('The From price is not a quote' appears twice in different forms). While it is organized into two clear sections, the redundancy could be trimmed to improve conciseness without losing essential 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?

Given the absence of an output schema, the description provides substantial context by naming returned fields (names, types, discoveryKind, URLs, fromPriceAud, etc.) and clarifying their meanings. It also notes that the count is dynamic and points users to get_quote for exact pricing, which covers likely agent needs. Minor gaps include missing pagination or error handling details, but they are not critical for a listing endpoint.

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 has zero parameters and the schema already covers 100% of the input (an empty object). According to the rubric, a baseline of 4 is appropriate for 0 params, and the description offers no additional parameter-level meaning because no parameters exist to explain.

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 the tool's purpose: to list curated collection landing pages with specific fields (names, types, discoveryKind, URLs). It distinguishes itself from sibling tools like get_collection by explicitly focusing on listing multiple collections, and from get_categories by noting the curated, hand-picked nature. The action verb 'List' and the resource are unambiguous.

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 explains what the tool returns but provides no explicit guidance on when to use it versus alternatives such as get_collection, get_categories, or get_100_best. There is no mention of when not to use this tool or which specific scenarios it best serves, leaving the agent to infer use cases from the field list alone.

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 no annotations, the description carries the behavioral transparency burden. It discloses the informational nature and enumerates the facts returned, but it does not describe return format, data freshness, external-link behavior, or the locale's effect on AU-only offers.

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 dense but well organized: it front-loads the tool's purpose, lists the fact categories, and closes with concrete example questions. It is longer than minimal, but every clause adds useful scoping 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?

For a simple facts-lookup tool with one optional parameter and no output schema, the description gives a strong inventory of what the agent should expect and when to call it. It is slightly incomplete in not describing the response shape or locale-specific behavior, but those are secondary gaps.

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 single parameter `locale` is fully documented in the schema, including its default and allowed values. The description adds no detail about locale behavior, but the schema already provides complete coverage, so the baseline 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 clearly identifies the resource (Sense2 company facts), the audience (procurement and supplier questions), and enumerates the fact categories covered. It does not explicitly name or contrast a sibling tool, so differentiation is implied rather than stated.

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 final sentence gives concrete trigger questions: 'who is Sense2', 'how big are they', 'are they compliant'. It provides clear when-to-use guidance but does not state when not to use it or point to an alternative sibling.

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?

No annotations are provided, so the description carries the full burden. It describes the tool's output in detail: themed sections, per-product intel, why it stands out, best branding move, and signature campaign moment. This gives a concrete sense of what the tool returns, though it does not explicitly state 'returns' or mention limitations.

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 a single dense sentence that efficiently packs examples, output structure, and usage guidance. It is front-loaded with the main concept and avoids fluff, though the long parenthetical list and flowery phrasing like 'signature campaign moment it owns' add slight sprawl.

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 there are no parameters, no annotations, and no output schema, the description is quite complete: it explains what the tool offers, the structure of results, and when to use it. It could be even more explicit about the return format (e.g., 'returns a list of sections'), but the description adequately covers the essentials.

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 has 0 parameters, so the baseline is 4. The description does not discuss parameters, but none exist, so there is no missing information. The schema is empty and accurately matches the no-input nature of the tool.

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 the tool's purpose: providing creative/unusual promotional item ideas in themed sections with per-product intel. It distinguishes itself from sibling tools by specifying 'unusual promotional items other suppliers don't carry' and listing concrete examples like cocktail umbrellas and custom ice cubes.

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 specifies when to use the tool: for briefs involving 'something different', 'unusual', 'never seen before', 'stand out from other brands', or 'conversation starter' ideas. It does not name alternative tools, but the use-case guidance is clear and contextual.

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 no annotations provided, the description carries the full burden. It discloses important behavioral nuances: the From price is the ladder's lowest unit price reached at volume, not at MOQ; prices are AUD ex-GST; the price ex-cludes GST; unit price steps down with quantity. It also explains the MOQ/fromPrice object relationship. The only missing pieces are rate limits or side effects, but as a read-only pricing look-up these are not central.

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 long-bodied but every sentence carries purpose: the beginning states the core, the middle clarifies pricing semantics, and the end gives use-case directives. The list of category examples is verbose but aids intent recognition. Not ideal in compactness but not redundant.

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, output-free schema tool, the description tells the agent what data it returns (`moq`, `fromPriceAud`), the scale of data (100+), the price semantics, and the typical usage flow. It is complete enough to call correctly without further expectations.

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 schema already documents `locale` with its alternatives and effect ('gates AU-only offers'). The description does not add any information beyond what the schema provides, so 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 verb–resource pairing: 'Entry (From) prices' for promotional product categories, and lists many concrete examples. It also distinguishes the tool from get_quote by saying 'then get_quote for an exact quantity' and frames it as the first step for price queries.

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 guidance ('Use this FIRST for...') and names the alternative ('then get_quote for an exact quantity'), covering both the appropriate trigger and the overflow into another tool.

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?

No annotations exist, so description must carry behavioral burden. It implies a read-only lookup, but does not explicitly state non-destructive nature, rate limits, or return 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: first lists topics, second gives usage examples. No wasted words; front-loaded with essential info.

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 FAQ tool with one optional parameter and no output schema, description is adequate. Could specify return format or that it returns answers, but not critical.

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 baseline is 3. Description adds minimal extra meaning beyond schema: it lists example topics that map to tag values, but does not elaborate on format or behavior.

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?

Description clearly states it provides practical buying answers for specific topics (MOQ, lead times, artwork, etc.) and gives concrete example questions. This distinguishes it from sibling tools like get_product or get_quote.

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?

Description provides example questions to use it for ('what is the minimum order', etc.), indicating clear context. However, it does not explicitly state when NOT to use it or compare with 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?

With no annotations provided, the description carries the behavioral disclosure burden. It discloses the output includes product counts and relatedGuides, and the verb 'List' implies a read-only operation. It does not mention pagination, authentication, or any exact return structure, which are minor gaps for a simple zero-parameter tool.

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 two sentences with no wasted wording. The first sentence delivers the core function and content, and the second sentence points to an alternative. It is front-loaded and easy to scan.

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, no-output-schema tool, the description explains the main return content: industry affinities, product counts, and relatedGuides for focused industries. It is mostly complete, though the exact shape or scope of 'relatedGuides' could be slightly clearer.

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 has zero parameters and the schema coverage is 100%, so the schema fully documents everything needed. The description adds no parameter-specific information, but none is required, making the baseline 4 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 uses a specific verb-resource pair: 'List industry affinities with product counts, plus relatedGuides for focused industries.' It clearly identifies what the tool returns. It mentions get_collection as a related alternative, but does not distinguish it from other sibling tools like get_categories or get_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?

The description gives explicit guidance to 'Use get_collection for detailed guidance,' which helps an agent decide when to prefer this tool over one alternative. However, it does not provide broader when-to-use or when-not-to-use context relative to the many other 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?

No annotations are provided, so the description must disclose behavioral traits. It hints at the output content (creative insight, signature move, brand vibe) but does not explicitly state read-only, idempotency, or side effects. The inference is reasonable for a retrieval tool, but lacks explicit transparency.

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 a single, 14-word sentence that front-loads the action ('Get a single inspiration card by slug') and includes parenthetical detail. No redundant information; every word adds value.

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?

Without an output schema, the description should explain return values. It mentions 'full creative insight, signature move, brand vibe', providing insight into the response content. This is adequate for a simple retrieval tool, though a more explicit structure listing would raise completeness further.

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 description 'Inspiration slug from get_inspirations', and the tool description adds 'by slug'. This combination provides context that the slug originates from another tool, enhancing semantic understanding beyond the schema 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 clearly states the tool retrieves a single inspiration card by slug, specifying the content includes 'full creative insight, signature move, brand vibe'. This distinguishes it from sibling 'get_inspirations' which lists cards, making the purpose specific and 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?

The description and parameter schema reference 'slug from get_inspirations', implicitly guiding users to first list cards to obtain the slug. However, it does not explicitly exclude alternatives like 'get_brand_moves' or provide when-not-to-use scenarios, but the guidance is clear enough for typical use.

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?

No annotations provided, so description should fully disclose behavior. Only states it lists cards, but lacks details on read-only nature, pagination, latency, or any side effects.

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?

Single sentence with necessary context, no wasteful words. Could be slightly more structured but efficient.

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?

Adequate for a simple list operation with one parameter. Missing details on response format, sorting, or filtering beyond limit, but sufficient given simplicity.

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 covers 100% of parameters. Description adds no extra meaning beyond schema's 'max results' explanation. 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?

Description clearly states verb 'List' and resource 'Sense2 editorial inspiration cards' with added context about brand-crushes and insights. However, it does not distinguish from sibling 'get_inspiration' tool.

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?

No guidance on when to use this tool versus alternatives like 'get_inspiration' or other list tools. Missing context for when this tool is appropriate.

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?

No annotations are provided, so the description carries the burden. 'List' signals a read-only operation, and the description inventories the payload (counts, URLs, relatedGuides) plus the boundary that detailed guides live elsewhere. It does not mention pagination or response envelope, but for a zero-parameter list this is reasonably transparent.

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 focused sentences with no filler. The main purpose and output are front-loaded, followed by a useful sibling routing note.

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 tool with no output schema, it gives essential return expectations and points to get_collection for more detailed content. Minor ambiguity remains around the relationship between relatedGuides and detailed guides, but the overall behavior is well specified.

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 no parameters, so the schema fully covers invocation. The description adds meaning by explaining what the result will contain, which is the relevant semantic context for this tool.

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 the specific action (List), the target resource (Shop by Intent curated collections), and the included output (counts, URLs, relatedGuides). It also distinguishes itself from get_collection by routing detailed guides there.

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?

Provides an explicit routing instruction: 'Use get_collection for those detailed guides.' It does not enumerate conditions for all sibling tools, but it clearly covers the closest alternative.

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?

No annotations provided, so description carries full burden. Discloses hand-curated nature, monthly refresh, and per-product trend signals. No behavioral contradictions.

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. Front-loaded with key information (trending products, location, refresh cycle). Every sentence 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?

Given zero parameters and no output schema, description sufficiently explains what is returned and when to use. Could specify if output is a list or single item, but still clear.

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?

No parameters exist; schema coverage 100%. Description adds value by explaining output content and update frequency, meeting baseline for zero-param tools.

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?

Description specifies it returns trending Australian promotional products/corporate gifts, hand-curated and refreshed monthly with trend signals. Clearly distinguishes from static category search.

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 states when to use ('what's trending', 'what's popular now', 'what's new' style questions) and contrasts with static category search. Lacks explicit when-not-to-use but adequate.

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?

No annotations are provided, so the description carries the full burden of behavioral disclosure. It transparently explains the locale effect (localises name/currency/pricing/URL, but descriptions stay English) and the conditional presence of the wholesale block. It does not mention error handling or response structure beyond the blocks, but for a simple get-by-ID tool, this is substantial coverage, warranting a 4.

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 that are efficiently front-loaded: the primary action and scope appear immediately, followed by essential details about pricing and locale behavior. No fluff or repetition; every clause adds value.

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 tool with only two parameters (one required) and no output schema, the description is remarkably complete. It tells the agent what the response contains (branded pricing, wholesale block, localized fields) and how locale affects it. There are no missing critical details that would prevent a correct invocation.

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 descriptions for both parameters. The description adds meaningful extra semantics for the locale parameter by specifying exactly which fields it localises and that descriptions remain English, which goes beyond the schema's simple list of allowed values. For pid, the description reinforces its role as the product identifier. This exceeds the baseline 3 for full 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 clearly states a specific verb ('Get') and resource ('full product details by PID'), and immediately distinguishes this from sibling tools like search_products by focusing on direct ID lookup. It also adds specificity about pricing models (branded vs wholesale) and locale behavior, which makes the tool's unique role unambiguous.

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 implicitly indicates this is for direct product lookups by PID, contrasting with search_products, but it does not explicitly state when to prefer this over alternatives or provide exclusions. The context is clear enough (you need a PID, not a search query), but there is no explicit routing guidance, so it earns 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?

With no annotations, the description carries the full burden, and it delivers: it explains what brandingIncluded covers, the colour/method caveats, when the full-colour extra is on file vs quoted on request, the wholesale option, AUD ex-GST pricing, and freight handling. The explicit 'never estimate it' instruction is especially valuable.

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 long but dense and front-loaded with the core action. The parenthetical caveats are somewhat heavy, yet every sentence adds relevant pricing behavior rather than 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?

There is no output schema and no annotations, so the description must explain return semantics. It covers key output fields, currency, freight, and fallback behavior. It does not fully specify the response structure or error cases, but it is complete enough for correct invocation.

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 useful context around quantity (e.g., the 100 pcs extra) but does not add new parameter-level syntax or format details 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?

Opens with a specific verb and resource: 'Get a pricing quote for a product at a specific quantity.' It further differentiates the quote scope by distinguishing branded price plus setup from wholesale/unbranded, so an agent can tell this apart from a generic quote_options tool.

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 intended use is clear: quote pricing for a product at a quantity. It does not explicitly name sibling alternatives or exclusions, but the context is unambiguous enough that an agent knows when to call it.

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?

No annotations provided, so description carries full burden. It discloses that reviewers are shown by initials only and what each review evidences. It does not mention read-only nature, but the lack of parameters and destructive hints imply it.

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 key information, no wasted words. Every sentence adds value.

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 no parameters and no output schema, the description fully explains what the tool returns, including privacy and evidence categories.

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?

No parameters exist, so description need not add param info. Baseline 4 for 0 parameters 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 clearly states it retrieves Sense2 customer reviews and platform ratings from Google and Trustpilot, with links and featured verbatim reviews. It distinguishes from sibling tools by specifying the specific content source and 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?

The description provides explicit usage examples like 'how good is Sense2' and 'what do customers say', but does not explicitly mention when not to use or name alternatives among 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?

With no annotations, the description fully discloses behavior: returns landing-page URL, region, freight terms, and explicitly states no transit-day estimates. No contradictions.

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-organized: starts with purpose, then examples, then parameter usage and important note. Slightly verbose but every sentence adds value.

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 simple parameter and no output schema, the description provides all necessary context: what it does, how to use it, what it returns, and a critical limitation. Complete for agent decision-making.

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 covers the slug parameter 100%. Description adds value by explaining the effect of including a slug (full intro) and providing examples, beyond the schema's basic 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 the tool's purpose: returning service areas where Sense2 delivers in Australia, with a dedicated page per location. It lists example questions and distinguishes from sibling tools that focus on products, brands, etc.

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 instructs to pass a slug for a specific area or omit for all, and provides a critical note not to promise delivery dates. It lacks explicit comparison to alternatives but gives clear usage context.

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?

No annotations exist, so the description carries the burden of behavioral disclosure. It reveals that the output is a set of five selling-point categories with supporting specifics, which is useful. However, it does not clarify whether the content is static, dynamically retrieved, or localized based on locale, nor what the actual return format looks like.

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 a single dense paragraph with substantial marketing detail—brand names, dates, and specifics—that goes beyond what an agent needs for tool selection. The main purpose is front-loaded, but the 'Use when' guidance appears only at the end, and the extra detail could be trimmed.

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 no output schema and no annotations, the description does a solid job of explaining what content the tool returns and when to use it. It could be more complete with explicit alternative tools or response-format details, but for a simple optional-parameter content tool it is sufficient.

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 only one optional parameter, locale, and the schema already fully documents it with 100% coverage. The description adds no parameter-level meaning, so the baseline score of 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 clearly states this tool provides the reasons buyers choose Sense2, organized into five verifiable fronts: price, quality, style, dispatch, and Australian ownership. The purpose is specific and distinguishable from generic company-fact or quote tools, though no sibling tool is explicitly named.

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 explicit trigger contexts: 'who should I use', 'is Sense2 any good', 'Sense2 vs X', and supplier shortlists. It gives clear when-to-use guidance, but does not provide when-not-to-use guidance or name alternative 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?

With no annotations provided, the description carries the full burden of behavioral disclosure, and it does so thoroughly. It discloses MOQ handling, tax-exclusive totals, locale currency, dispatch-only semantics, freight quoting, and that Sense2 never promises a delivery date, which is exactly the kind of non-obvious behavior an agent needs.

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 dense but purposeful; each sentence contributes either action flow, output details, or routing guidance. A couple of clauses are packed into longer sentences, but nothing is redundant or wasteful.

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?

Despite having no output schema and no annotations, the description is practically complete for invocation. It covers input behavior, output composition and count range, edge cases like MOQ and needBy, currency/tax treatment, and the distinction from sibling tools.

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 meaningful semantics beyond the schema. It explains how quantity interacts with MOQ and clarifies that needBy is about production window fit for dispatch, not arrival—details not present in the parameter descriptions.

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: it is a one-call buyer quote tool that searches the catalogue, prices matches, ranks by total spend, and returns options. It also explicitly differentiates itself from the search_products→get_quote chain, making its purpose unmistakable.

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 direct usage guidance by saying to prefer this tool over the search_products→get_quote chain. It also explains when needBy applies and clarifies that freight is handled separately, so an agent knows the intended invocation context.

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 no annotations, the description carries the full burden of behavioral disclosure. It explicitly states the return shape, the meaning of 'total' vs 'returned', the cap at 500, and how to paginate with offset. This is strong transparency, though it omits details like error handling or empty query behavior, which are minor for a search tool.

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 two sentences, front-loaded with the purpose, and efficiently conveys all critical behavioral information. No word is wasted; every clause contributes to understanding the tool's behavior or return format.

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 search tool with no output schema, the description is complete. It covers the return format, pagination, and result cap. It also indirectly clarifies how limit and offset work together. No significant gaps remain for the agent to select and invoke the 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 value by explaining the relationship between returned and total, the 500-cap, and how to use offset for large result sets. This enriches the meaning of the parameters beyond the basic schema descriptions.

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 the tool's function: 'Search Sense2 promotional product catalogue by keyword.' The verb 'search' is specific and distinct from all sibling get_* tools, which all retrieve specific entities rather than performing keyword searches. This makes the purpose unmistakable and well-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?

The description provides clear context on how to use the tool, such as pagination with offset and the cap of 500 results per page. It does not explicitly mention when not to use it, but the contrast with sibling get_* tools is implicit. The guidance on walking large sets is practical and adds value.

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