lead-stampede

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 conditional behavior (returns a tracked booking URL if online scheduling exists, otherwise phone/email) and scope (works for all client types). This goes beyond a basic description, though it omits details like error handling or response format.

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 words. It front-loads the core output, then adds conditional detail and scope, making every sentence earn 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 the tool's low complexity (one parameter, no annotations, no output schema), the description adequately explains the return value and conditional branches. It could mention error scenarios, but for a simple getter, this 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?

The schema covers the only parameter (client_slug) with a clear description ('Unique slug identifying the client'), so 100% coverage is met. The description adds no additional parameter-specific 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 the tool returns business hours and booking options, which is a specific verb+resource combination. It distinguishes itself from sibling tools like get_client or get_reviews by focusing on availability and scheduling, making its purpose 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 notes it works for all client types (service, automotive, ecommerce), providing useful context for when to use it. However, it does not explicitly mention alternatives or exclusions, though the sibling tools are distinct enough that this is not a major gap.

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 transparency burden. It discloses that the data is public and enumerates the included fields (business details, contact info, services, hours, reviews, booking URL), which gives concrete behavioral context. It doesn't address error cases or rate limits, but the tool's simplicity limits the need.

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 sentence that front-loads the core purpose, then lists the included data categories. There is no redundancy or unnecessary detail.

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 low-complexity tool with one parameter and no output schema, the description adequately covers the return contents and scope. It is a simple retrieval operation, and the description is sufficiently complete.

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 has 100% coverage for the single parameter 'slug', including an example. The description only restates 'by slug' without adding any semantic detail beyond the schema, so the baseline 3 applies.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool returns the full public Agent Card for a single client by slug, using a specific verb and resource. It distinguishes itself from sibling tools like search_clients by specifying 'single Lead Stampede client by 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?

The description implies use when you have a known slug and need detailed client data, but it does not explicitly mention alternatives like search_clients or specify when not to use. No direct usage exclusions are provided.

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 states what is returned but does not mention error behavior (e.g., product not found), permissions, rate limits, or whether the operation is read-only. The read-only nature is implied by 'Returns', but no edge cases or side effects are disclosed.

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, well-structured sentence that front-loads the main purpose, then lists the returned fields in a compact list. No wasted words, and the 'Only for e-commerce clients' clause is a valuable differentiator.

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 retrieval tool with two well-documented parameters and no output schema, the description adequately specifies the return content. It could be more complete by addressing not-found behavior or prerequisites, but given the low complexity and the field list, it is sufficiently complete for most use cases.

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 already provides full descriptions for both parameters (100% coverage), so the description adds little beyond confirming the lookup is by slug. The phrase 'by slug' reinforces the parameter semantics but does not introduce new 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 uses the specific verb 'Returns' and clearly identifies the resource ('full details for a single product by slug'), then enumerates the returned fields. It also narrows the context with 'Only for e-commerce clients', which differentiates it from sibling tools like search_products or get_vehicle_details.

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 for when to use the tool: for a single product's full details, and only for e-commerce clients. It does not explicitly exclude alternatives or name sibling tools, but the scope is sufficiently defined to guide selection in most cases.

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 says 'Returns' which implies a read operation, but it does not explicitly state that it is read-only, does not describe permissions, error handling, or side effects. The tool could be a safe getter, but the description gives no behavioral guarantees.

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, front-loaded sentence that states the purpose and output without redundancy. Every word contributes value, making it highly concise for an AI agent to parse.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

The tool is simple (1 parameter, no output schema, no annotations), and the description covers the key elements: what it returns (review count, average rating, summary) and its applicability. It could mention the exact return format or error behavior, but for a getter of this simplicity, the provided context is sufficient for an agent to invoke it.

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 describes the only parameter (client_slug) with 100% coverage. The description adds a small amount of context by referencing 'Lead Stampede client' and 'all client types', which reinforces the parameter's domain, but it does not significantly enhance understanding 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 uses a specific verb ('Returns') and clearly identifies the resource ('social proof for a Lead Stampede client') with concrete output details ('review count, average rating, and a brief summary'). It also adds a scope qualifier ('Works for all client types') that distinguishes it from other client-related tools like get_client.

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 by naming the output (reviews/ratings), and the 'works for all client types' provides a scoping hint. However, it does not explicitly state when to use this tool versus alternatives like get_client, nor does it mention exclusions or prerequisites. Guidance is implied rather than direct.

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 behavioral burden. It communicates that the tool is a read operation ('Returns') and notes an availability limitation for automotive clients, but it does not disclose other behavioral traits such as authentication requirements, rate limits, response format, or whether results are live or cached.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is two sentences, immediately front-loads the core functionality, and includes only meaningful details. No redundant or filler content exists; 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 tool's low complexity (2 parameters, simple list operation) and the presence of a complete input schema, the description adequately covers purpose and scope. It omits return structure, but since there is no output schema, a brief mention of response format would be helpful; however, the description is still sufficient for a straightforward read-only list 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 already provides descriptions for both parameters (100% coverage). The description adds semantic detail by mapping the category enum values to specific offer types (e.g., 'new-vehicle APR/lease specials', 'service & parts coupons'), which clarifies what each category means beyond the schema's generic 'Filter by category.'

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 'Returns' and identifies the exact resource: the dealership's current specials and offers. It enumerates concrete categories (new-vehicle APR/lease, pre-owned, service & parts, rebates, incentive programs), which clearly distinguishes it from sibling tools like get_vehicle_details or get_reviews.

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 the use case: when you need to retrieve dealership specials or offers. It includes a notable constraint—'Only available for automotive clients'—but does not explicitly compare with alternative tools or provide when-not-to-use guidance. No sibling tools are referenced.

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 carry the behavioral disclosure burden. It states a constraint ('Only available for automotive clients') but does not mention read-only nature, required permissions, rate limits, or what happens if VIN/stock_number is omitted—only that one is used. It also doesn't clarify whether 'full details' includes specific 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 two concise sentences, front-loaded with the primary action and resource. Every word adds meaning, with no filler or repetition of schema details.

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 3-parameter tool with no output schema and no annotations, the description is minimally adequate. It communicates the core purpose and a key restriction, but lacks detail on return format (what 'full details' includes), error behavior, or prerequisites beyond automotive client status. More completeness would be expected given the absence of an output schema.

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. The description adds semantic value by stating the vehicle is identified by VIN or stock number, implying a logical either/or relationship that the schema doesn't enforce. It also ties client_slug to the automotive-client restriction.

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 ('Returns') and a specific resource ('full details for a specific vehicle') while naming the lookup keys ('by VIN or stock number'). This clearly distinguishes it from sibling tools like get_product_details and search_inventory.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

Usage is implied: use when needing full details for one specific vehicle. The phrase 'Only available for automotive clients' gives a context restriction, but there is no explicit when-not-to-use or comparison with alternatives like search_inventory.

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 carries the full transparency burden. It discloses the source (public client directory) and that it returns distinct values, implying a read-only operation. However, it does not mention potential limitations, ordering, exact return structure, or error conditions. For a zero-parameter list tool, the description is adequate but sparse.

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 sentence that is front-loaded with the action 'Returns.' It contains no filler or redundant wording, earning full marks for conciseness and clear structure.

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 listing tool, the description fully covers the core functionality: returning distinct business types. It adds scope by mentioning the 'public client directory,' which is useful context. Since there is no output schema and no annotations, the description is the primary information source and it is sufficient, though it could theoretically expand on the exact output format.

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 schema coverage is 100% by vacuity. The baseline for 0 parameters is 4, and the description appropriately does not need to explain parameter details. It adds no irrelevant parameter information.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool returns 'distinct business types' from the 'Lead Stampede public client directory.' The verb 'Returns' is specific, and the resource is unambiguous. It also distinguishes itself from siblings (get_client, list_cities, search_clients) by focusing on business types rather than client records or locations.

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 explicit when-to-use or alternative guidance is provided. The context implies this tool is used when one needs to know available business types, but it does not explicitly say to avoid using it for other purposes or mention alternatives. The usage is inferred but not 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?

With no annotations provided, the description carries full burden for disclosing behavior. It indicates a read-only operation via 'Returns,' but does not mention potential limitations such as ordering, result size, or whether any authentication is required. This is a clear gap for a tool with zero annotation coverage.

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 concise sentences that front-load the primary purpose and add a practical use case. Every sentence earns its place, with no redundancy or fluff.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

The simple nature of the tool (no params, no output schema) means the description covers the core functionality well. However, it could have elaborated on the return format (e.g., list of strings) or any guaranteed ordering, so it isn't perfect.

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 empty input schema fully describes the interface. According to guidelines, 0 parameters receives a baseline score of 4, and the description adds no parameter-specific details but also doesn't need to.

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 the specific verb 'Returns' and clearly identifies the resource as 'distinct cities served by public Lead Stampede clients.' This distinguishes it from sibling tools like get_client and list_business_types, which have different purposes.

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 states it is 'useful for discovering which locations have businesses in the directory,' which gives clear usage context. However, it does not explicitly mention when not to use it or name alternative tools, so it stops short of a 5.

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

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

Without annotations, the description carries the burden of behavioral disclosure. It states the tool is for a 'public client directory' (implying non-destructive, likely no auth) and describes the return payload. However, it does not mention pagination, result limits, ordering, or error behavior, which are common gaps for search tools. The provided context is basic but not comprehensive.

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 concise sentences, front-loaded with the core action and then providing return details and filter options. Every sentence earns its place, and there is no redundant or verbose phrasing.

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 straightforward search tool with no output schema and no annotations, the description covers the essential facets: what is searched, what is returned, and what filters are available. It is slightly incomplete in not addressing pagination or result limits, but given the simplicity, it is largely sufficient for an agent to invoke correctly.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The input schema provides 100% coverage with descriptions for all three parameters, so the baseline is 3. The description adds meaningful semantic value by clarifying that 'query' is free-text across name, description, and industry, and explicitly enumerates the available filters. This goes beyond the schema's terse 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 clearly states the tool 'Search Lead Stampede's public client directory' with a specific verb and resource, and further specifies return content (contact info, services, ratings) and filtering capabilities. It implicitly distinguishes from siblings like get_client (specific retrieval) and list_business_types/list_cities (enumeration helpers), making its purpose 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 implies usage by listing filters (city, business type, free-text query) and the output structure, but it does not explicitly state when to use this tool versus alternatives such as get_client for fetching a single known client. There is no mention of when not to use it, though the context is clear enough for basic usage.

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 the result limit (up to 25), the optional natural-language query, and the automotive-client restriction. However, it does not explain how filters combine (e.g., AND semantics), the response structure, or any authentication requirements, leaving gaps in behavioral 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 concise, with five short sentences delivering purpose, filters, special query behavior, result limit, and availability. It is front-loaded with the main action and wastes no words, making it effective 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 tool with 13 parameters and no output schema, the description covers the core search capabilities and key constraints, but omits essential context such as the shape of return values, filter combination semantics, and any preconditions like authentication or authorization. This leaves the agent somewhat under-informed for complex 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?

The schema covers 69% of parameters with descriptions, leaving year_min, year_max, body_type, and fuel_type undocumented. The description adds context by grouping these as 'year range', 'body type', and 'fuel type', which helps agent understand their purpose. It does not, however, provide deeper semantics like range inclusivity or how the natural-language query interacts with structured filters.

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 action ('Searches') on a clear resource ('a dealership's vehicle inventory') and enumerates the filter dimensions. This distinguishes it from sibling tools like get_vehicle_details (which likely fetches one vehicle) and search_clients/search_products, which target different entities.

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 its use for inventory search but does not explicitly explain when to choose it over get_vehicle_details or other sibling search tools. The only usage restriction mentioned is 'Only available for automotive clients,' which is a constraint but not a comparative guideline.

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. It discloses the natural-language query capability and the e-commerce-only restriction, which is useful. However, it does not mention pagination, result format, or any potential side effects/read-only nature, so transparency is incomplete but not poor.

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 only two sentences long, front-loaded with the core purpose, and mentions key capabilities without any filler or repetition. 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 the tool's complexity (8 parameters, no annotated metadata), the description covers the essential usage context, parameter families, and scope. A minor gap is the absence of any indication of return format, but the purpose of a search tool implies a result set, making this adequate.

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 has 100% descriptive coverage for all parameters. The description summarizes the filter groups (category, price range, collection, stock status) but adds no semantic detail beyond what the schema property descriptions already provide, so the baseline of 3 applies.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the verb-resource relationship: 'Searches the product catalog of an e-commerce client.' It also adds a specific scope ('Only works for e-commerce clients') that differentiates it from sibling tools like search_clients and search_inventory.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description gives clear context for use—searching product catalogs for e-commerce clients—and implies filters for narrowing results. However, it does not explicitly mention alternatives or when NOT to use this tool, so it stops short of full usage guidance.

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